esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### Unidirectional/Bidirectional Grid LSTM  
Next, we verify the effect of BiGridLSTM on the character error rate. In order to separate the influence between the bidirectional structure and depth, we only compare the bidirectional models with two layers of recurrent layers.  
Table 2 shows that by introducing a bidirectional structure, all bidirectional model CERs are lower than that of their unidirectional models, and F1 value is higher. It is verified that the bidirectional structure can capture the information before and after the current moment. Surprisingly, BiGridLSTM achieves a relative gain of approximately 26% in their CER as compared to unidirectional GridLSTM, which demonstrates the validity of our proposed model. It is worth noting that the bidirectional grid LSTM has more gain than the bidirectional LSTM and the bidirectional GRU, and has higher sensitivity and specificity. Therefore, it is demonstrated that with the bidirectional structure, the grid LSTM can further reduce the CER of speech recognition on the LibriSpeech corpus, and improve the sensitivity and specificity of the model. More discussion is found in Section 5.3.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **Layer** & **Hidden Size** & **Params** & **CER (\%)** & **F1 (\%)** & **Sensitivity (\%)** & **Specificity (\%)** \\ \hline LSTM & 2 & 256 & 835,869 & 21.64 & 79.50 & 80.11 & 78.93 \\ GRU & 2 & 256 & 628,765 & 24.62 & 76.68 & 77.38 & 76.12 \\ GridLSTM & 2 & 256 & 2,262,813 & **20.44** & **81.03** & **81.46** & **79.78** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Character error rate (CER) comparison of GridLSTM model and baseline. The bold data in table indicates the minimum CER and highest sensitivity, specificity, and F1 in the experiment.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **Layer** & **Hidden Size** & **Params** & **CER (\%)** & **F1 (\%)** & **Sensitivity (\%)** & **Specificity (\%)** \\ \hline GRU & 2 & 256 & 628,765 & 24.62 & 76.68 & 77.38 & 76.12 \\ BiGRU & 2 & 256 & 1,250,077 & 18.93 & 82.10 & 82.89 & 81.66 \\ LSTM & 2 & 256 & 835,869 & 21.64 & 79.50 & 80.11 & 78.93 \\ BiLSTM & 2 & 256 & 1,664,285 & 16.22 & 83.93 & 84.57 & 83.21 \\ GridLSTM & 2 & 256 & 2,262,813 & 20.44 & 81.03 & 81.46 & 79.78 \\ BiGridLSTM & 2 & 256 & 4,518,173 & **15.06** & **85.77** & **86.30** & **85.38** \\ \hline \hline \end{tabular}
\end{table}
Table 2: CER comparison of bidirectional models and unidirectional models. The bold data in table indicates the minimum CER and highest sensitivity, specificity, and F1 in the experiment.
## 5 Data Analysis and Results
### Analysis of PLS-SEM Results
#### 5.1.2 Structural Model Assessment
As depicted in Figure 2, the result of the proposed model showed that 81% of the variance in CC adoption can be described by the technological, environmental, and organizational factors (TOE) factors. Figure 2: Results of structural model. Note: ** \(<0.05\), *** \(<0.01\).
# Telecommunication Technologies for Smart Grid Projects with Focus on Smart Metering Applications
## 4 Smart Metering Applications--Smart Grid Projects
### Renewable Energy Sources
#### 4.6.3 i-Em  
Monitoring of renewable energy plants and the smart grid in general, has been the topic of the project \"i-EM\" that took place in Italy. During this project, several products have been created for several purposes like: the smart grid management, the integration with renewable sources, monitoring of photovoltaic systems and the design and construction of renewable energy plants [114].
# A Proof-of-Multiple-State Consensus Mechanism for Mobile Nodes in Internet of Vehicles
## 4 Consensus Mechanism Based on Integrated State
### Voting Options  
After calculating the value, the node initiates a local vote among the remaining nodes. Only when the number of votes inclined towards \"agree\" exceeds the threshold of votes, will the node be considered an honest node. Verified vehicles can package messages and send them to the miner node to participate in the next round of relay node competition, and such messages are marked as valid messages. Conversely, for vehicles below this threshold, the nodes vote \"reject\", meaning communication is not possible or denied. A node is considered honest and can only forward its packet to the miner node for the next relay node election if it receives a majority of \"acceptance\" votes, marking its messages as valid. Conversely, nodes that do not receive enough approval votes are considered malicious for that consensus round, their messages are considered invalid, and ignored by the miner nodes as harmful to the network. After enough valid messages are collected in a consensus round, miner nodes rank these messages according to their values. The node with the highest value is selected as the next relay node. If two nodes \(i\) and \(j\) have identical \(SF\) values, the miner node compares their acceptance votes. The node with more acceptance votes is considered more capable of message delivery and is therefore more suitable as a forwarding node. If the miner node fails to collect enough valid messages within the allotted time, it ends the current round of consensus and requests other nodes to begin the next round.
## 3 Cable Models and Methodology  
This study compares three cable designs--single-segment, three-segment, and five-segment--under four operating frequencies (0, 50, 150, and 250 Hz). All simulations assume an infinite lay length (i.e., no helical twisting) to isolate the influence of conductor segmentation on electromagnetic losses. A consistent total conductor cross-sectional area of approximately 2711 mm\({}^{2}\) is maintained for each design, ensuring that differences in AC losses reflect segmentation rather than size.
## 4 Optimization Design Algorithm
### Ordinary Multi-Objective Particle Swarm Optimization Algorithm
#### 4.2.2 Archives Maintenance Algorithm  
In the process of multi-objective optimization, multiple Pareto solutions (non-dominated solutions) will be obtained. The solution set composed of all Pareto solutions is called the Pareto front (PF). Therefore, it is necessary to introduce an archive to store the Pareto solutions. The archive maintenance algorithm is as follows:  
1. The target space is divided according to the boundary value of each objective function of all particles in the archive and the grid width;
2. Calculate the grid number of each particle in the archive;
3. Calculate the crowding degree of the grid, that is, the number of particles in the grid;
4. Determine whether the number of particles in the archive exceeds the archive's capacity. If it exceeds, randomly delete a particle in the most crowded grid until the number of particles does not exceed the archive's capacity.
# Signal to Noise Ratio of a Coded Slit Hyperspectral Sensor
## 3 Results and Data Analysis  
In this section the SNR of hypothetical CA multiplexed sensors is estimated by using the results that have been derived previously. Realistic system components and parameters have been employed, and the scenario of a pushbroom system for an air-borne remote sensing application is envisaged here. Three sensor configurations have been considered:(a). Separate visible-near infrared (NIR) and shortwave infrared (SWIR) sensors components as representative of systems that have been widely marketed for commercial use; (b). An integrated (i.e., combined) visible and SWIR sensor using a single focal plane array (FPA) and dual-angled blazed grating, which are more representative of systems that have been widely utilized by large organisations such as government and research laboratories (such as AVIRIS-NG or MaRS, etc.).  
According to the analysis in the previous sections, it is realised that the SNR of the sensing system is dependent on the levels of input signals, hence a reference radiance curve must be used here for assessing the SNR of the 3 different configuration of the sensing systems. The sensing condition is assumed a spatially uniform lambertian surface with a constant spectral reflectance when the scene is illuminated under solar irradiance. The \"direct normal spectral irradiance\" specified in ASTM G-173-03 [14] has been adopted as the solar irradiance spectrum here, and negligible absorption and scattering in the atmosphere between the surface and the sensor have been assumed. Typical characteristics and parameters for the modelled sensors in this work are listed in Table 1.
# Corrosion Behavior and Conductivity of TiNb and TiNb Coated Steel for Metallic Bipolar Plates
## 1 Introduction
Among all the PVD ways, magnetron sputtering is used extensively in industry due to its high stability and limited contamination. During sputtering, influenced by electrical and magnetic fields, argon ions in the vacuum chamber bombard the targets causing atoms or clusters to separate from the targets and finally be deposited on the substrate. Usually, coating particles form firm bonds with the base material. In this study, TiNb and TiNbN coatings with low Ti content were deposited on 316L SS by magnetron sputtering to explore their performances in simulated PEMFC cathode environments. The microstructures, morphologies, and chemical compositions of the coatings were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), energy dispersive spectrometry (EDS), and X-ray photoelectron spectroscopy (XPS). Electrochemical experiments and ICR tests were conducted to evaluate the corrosion resistance and conductivity of coated and uncoated 316L SS.
## 4 Methodology and Prototype Implementation
### Implementation of the Active EMP  
The implemented functions of the aEMP are the distribution of the time-of-use electricity prices as an exemplary use of the transparent data tunnel, the monitoring of the status of the EMS, and the communication of the grid state according to the smart grid traffic light concept. The first function is the transmission of the time-of-use electricity prices. In our prototype we use historic electricity prices from the German European Power Exchange day-ahead market, adjusted by grid fees and taxes, and send them to the automated EMS once a day. Furthermore, the grid state is communicated each time the connection to the EMS is established, or if a change of the smart grid traffic light phase takes place. As the prototype is encapsulated from other systems, for the scope of the prototype, it is sufficient to manually set the grid state in the systems of the aEMP. The aEMP software is built in a modular fashion with multiple components interconnected via a message broker. If a grid state change is detected, a message is published to Mosquito. The ComHandler, analogously to the EMS, acts as mediator between message broker and SMGW. It encrypts all outgoing traffic to the SMGW and decrypts all incoming traffic to the message broker. The ComHandler embodies, for all three HKS3-5, a TLS Server, waiting for the connection request from the SMGW. From the automated EMS, the aEMP receives different selected states, like the state of the charging station, the current charging power, and the consumer's requests. The received information is stored in a time-series database by the aEMP. As database, we use InfluxDB ([[https://www.influxdata.com/](https://www.influxdata.com/)]([https://www.influxdata.com/](https://www.influxdata.com/))). The aEMP can use this data to visualize the grid states and the power flows with Grafana--open source software for analytics and interactive visualization ([[https://grafana.com/](https://grafana.com/)]([https://grafana.com/](https://grafana.com/))). In that way the aEMP can also track whether the EMS acts according to the transmitted grid state.
## 3 System Model
### Feature Extraction and Distillation  
#### 3.1.1 Deep Feature Extraction  
In LCD tasks, obtaining the ground truth for the dataset entails a substantial workload. Therefore, when training data are limited, it is crucial to efficiently utilize these data to train a model with strong generalization performance. The paper introduces the idea of transfer learning and fine-tunes the pre-trained network in an end-to-end manner on the existing limited training dataset, making the network more suitable for LCD tasks. This approach not only shortens the training time, promoting faster network convergence, but also enhances the generalization ability of the model.  
To select the network with the best initial performance for the LCD task, this paper pre-screened the existing pre-trained networks. The selection method involved testing multiple networks on the same dataset and choosing the one that achieved the highest recall rate at 100% accuracy. As shown in Figure 2, AlexNet [20] achieved the highest recall rate at 100% accuracy, thus it was chosen as the feature extraction module.  
The complete AlexNet network has five combined convolutional layers (including convolution, normalization, and max-pooling), three fully connected layers, and a subsequent softmax layer. The output of each layer of the AlexNet network can be extracted separately as the global features of the image, and as the network goes deeper, the ability of features to represent images is enhanced layer by layer. However, it has been demonstrated [6] that the features extracted from the fully connected layers cannot ideally characterize the image due to the loss of spatial information, as shown in Figure 3.  
Figure 2: Comparison results of different pre-trained networks.  
Therefore, as shown in Figure 1, this paper discards the subsequent fully connected and softmax layers of AlexNet and only preserves five combined convolutional layers (5 CONVs) for our feature extraction. The details of the network structure are shown in Table 1, including the settings of network type, output dimension, filter size, step size, and padding.
## 2 Experimental Investigation
### Arrangement of Electrodes and Voltage Application Method  
Figure 3 features the configuration of the copper mesh electrodes embedded in the specimen to apply voltage. As shown in Figure 3, the electrodes were embedded at 40 mm intervals in a straight line with four copper meshes arranged in a row at equal intervals on the prism-shaped specimens, prepared with dimensions of 40 \(\times\) 40 \(\times\) 160 mm. The two electrodes were installed at a distance of 40 mm and 120 mm, respectively, and a copper mesh was employed to minimize the contact resistance between the cement and the electrode. In this study, the heating performance test was performed by applying DC voltages of 10 V, 20 V and 30 V to the prismatic specimens. The DC voltage application method was divided into two cases as shown in Figure 3. In case 1, the voltage was applied by connecting the electrodes installed at the two measuring points A and D at both ends of the specimen; in case 2, the voltage was applied by connecting the electrodes installed at measuring points B and C of the specimen. In case 1, the total volume of the voltage-applicable part is 40 \(\times\) 40 \(\times\) 120 mm\({}^{3}\) while, in case 2, the total volume of the voltage-applicable part is 40 \(\times\) 40 \(\times\) 40 mm\({}^{3}\).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Designations**} & \multicolumn{4}{c}{**Fiber Contents by Volume**} & \multicolumn{4}{c}{**Unit (kg/m\({}^{3}\))**} & \multirow{2}{*}{**SP 1**} \\ \cline{2-2} \cline{4-7}  & \multicolumn{2}{c}{**CF**} & \multicolumn{2}{c}{**SF**} & & & \\ \cline{2-2} \cline{4-7}  & \% & **kg/m\({}^{3}\)** & \% & **kg/m\({}^{3}\)** & & & & \\ \hline ECM-CF25 & 0.25 & 4.5 & & & & & & \\ ECM-CF50 & 0.50 & 9.0 & & & & & & \\ ECM-CF75 & 0.75 & 13.5 & & & 198 & 450 & 900 & 0.5\(\sim\)1.0 \\ ECM-CF100 & 1.00 & 18.0 & & & & & & \\ ECM-CF125 & 1.25 & 22.5 & & & & & & \\ ECM-SF25 & & & 0.25 & 19.62 & & & & \\ ECM-SF50 & & & 0.50 & 39.25 & & & & \\ ECM-SF75 & & & 0.75 & 58.87 & 198 & 450 & 900 & - \\ ECM-SF100 & & 1.00 & 78.50 & & & & & \\ ECM-SF125 & & 1.25 & 98.12 & & & & & \\ PM & Plain cement mortar mixture (0\% conductive fiber) & 198 & 450 & 900 & - & & \\ \hline \hline \end{tabular} \({}^{1}\) SP = superplasticizer (% mass of cement).  
\end{table}
Table 4: Designations of specimens and mix proportions of mortars (from 0 vol% to 1.25 vol%).  
Figure 2: Fabrication process of ECM by every stage: (**a**) material preparation, (**b**) dry mixing (2 min), (**c**) fiber input, (**d**) addition of water and SP admixture (3 min), (**e**) pouring and compaction of the 1 st floor, (**f**) pouring and copper mesh installation, (**g**) 2 nd and 3 rd floor compaction (3 min) and (**h**) demolding and curing.
## 5 Challenges and Open Directions
### Challenges in Payload Capacity and Security  
Payload capacity is another major challenge when designing watermarking solutions for latent diffusion models. Traditional methods are often limited by the amount of data that can be embedded without compromising the quality of the output image. For LDMs, the latent space offers an opportunity to embed larger payloads while maintaining imperceptibility. However, maximizing payload capacity often risks reducing the robustness of the watermark. Techniques like METR [29] have made strides in embedding larger messages, but there is still a need for more efficient payload management strategies that can balance capacity with resilience to various attacks.  
Security, in terms of preventing unauthorized detection or removal of the watermark, is also a critical concern. Many current methods rely heavily on key-based security, such as using secret keys to embed and extract watermarks securely. Safe-SD [26] introduces a text-prompt-triggered mechanism to enhance watermark security by using secure prompts to verify content authenticity. Future directions should explore hybrid security methods that combine key-based techniques with stochastic embedding processes, making it more difficult for adversaries to reverse-engineer or remove the watermark.
## 1 Introduction
### Discount Factor and Learning Rate  
The discount factor is a value which is used to balance immediate and future rewards. The agent is trying to maximize the total future rewards, instead of the immediate reward. It is used to adjust the weight of the reward of the current optimal value and indicate the significance of future rewards [24]. The discount factor is necessary in order to help the agent find optimal sequences of future actions that lead to high rewards soon and not only the current optimal action.  
A visit frequency criterion is applied for ensuring that the proposed Q-learning-based GMPPT process will converge to the GMPP. The agent is encouraged to consider that knowledge acquired from states with a small number of visits is more important than knowledge acquired from states with a higher number of visits. Thus, states that have been visited fewer times will have a higher learning rate than states that have been explored more times. This is accomplished by calculating the value of the learning rate _alpha_ in (2) as follows:  
\[alpha=\frac{k_{1}}{k_{2}+k_{3}\cdot N} \tag{8}\]  
where \(k_{1}\), \(k_{2}\) and \(k_{3}\) are factors which determine the initial learning rate value for a state with no visits and \(N\) is the number of times that the agent visited the specific state. The rationale behind the variable value of alpha is that its value decreases as the number of visits increases to enable convergence in the limit of many visits.
# Fluid Flow-Based Vibration Energy Harvesters: A Critical Review of State-of-the-Art Technologies
## 5 Flutter Energy Harvesting
### Flutter EM-FBEHs  
[PERSON] et al. [113] described a flutter-based EM-FBEH, using an airfoil linked to a cantilever beam which bends due to airflow over the airfoil. The beam's bending depends on the lift force from the airfoil and the beam stiffness. Figure 9a illustrates a permanent magnet linked to the airfoil and a coil attached to the harvester's base. The magnetic flux cutting the coil changes as the airfoil moves, resulting in the generation of electrical power. At wind speeds as low as 2.5 m/s and 5 m/s, the harvester output was 470 \(\upmu\)W and 1.6 mW, respectively. An EM-FBEH [114] was utilized to capture energy from the self-excited aerodynamic response of a T-shaped cantilever structure. The T-shaped cantilever, which had a vertical plate attached to the windward side of a horizontal plate, produced large vortex patterns at its leading edge. These vortices generated considerable lift as they traveled downstream, generating rotational instability at low wind speeds due to interactions between the upper and lower surfaces. Mechanical and electrical damping parameters were employed in the harvester to determine the ideal load resistance and the critical wind speed at which fluttering occurred. The harvester generated 1.1 mW of average power at 8 m/s. [PERSON] et al. [115] investigated the effects of performance design factors such as wind speed, the position and size of the magnets, the pre-applied tension of the membrane, the angle of attack of the membrane and the orientation of the harvester in the flow field. The model shown in Figure 9b consists of a flexible membrane, coil, magnet and support. Experiments demonstrated that a single wind belt can provide power from 3 to 5 mW. Moreover, it is observed that the wind panel generates power of about 50 mW at a wind speed of 7 m/s. A wind panel made up of five such wind belts can produce power between 30 and 100 mW with winds less than 8 m/s. The use of the Karman vortex in belt fluttering EMEH was proposed in [116]. A cylinder was used for the formation of the Karman vortex behind it to induce vibrations when placed in the fluid flow. The membrane strip experiences pressure from the fluid flow, which in turn deforms the belt, and the magnet attached to it oscillates due to which the coil experiences a changing magnetic flux density and hence induces electric charge in the coil terminals. The coil voltage of 6 V peak-to-peak was generated at a wind speed of 3 m/s. [PERSON] et al. [117] presented a wind belt-type EM-FBEH with a reduced critical wind speed by positioning the magnets close to the membrane's center. At a wind speed of 10 m/s, the harvester was found to yield a maximum average power of 705 \(\upmu\)W.
# Oblique Wide-Angle Multi-Sector Metamaterial Absorber for Space Applications
## 2 Design
### Optimization of Sector A
This is more pronounced for TM, for which \(\Delta Y_{0}^{TM}=0.2\ Y_{0}\) for \([0^{\circ},\,35^{\circ}]\), \(0.5Y_{0}\) for \([35^{\circ},\,55^{\circ}]\), and \(0.6Y_{0}\) for \([55^{\circ},\,65^{\circ}]\). A classical method allowing both scan compensation and bandwidth enhancement is to use dielectric materials with higher permittivity instead of the honeycomb and to add a dielectric matching slab of approximately \(\lambda/4\) in front of the absorber [7]. This solution results in a consequent mass augmentation and is thus not adapted to space conditions. Another approach which is used in the study is to adjust the value of \(C\) and \(G\) in Equations (5) and (6) in order to obtain the desired frequency and absorption level for a given value of \(\theta\). This does not compensate for the scan and mismatch issues when \(\theta\) varies but allows a locally satisfactory solution. In practice, this amounts to modifying the geometric dimensions of the unit cell and therefore to designing separate sectors adapted to a more limited angular range.