esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Effects of EAFS  
#### 3.2.1 Rheological Properties  
The average diameters for the J-Ring flow and slump flow for samples containing varying doses of EAFS are shown in Figure 11. As shown in the figure, introducing EAFS showed no adverse effects on the flow-ability of the SCC, which means that it will not be subject to any significant segregation. All trials containing EAFS can be considered. Figure 12 shows the numerical difference between the J-Ring flow and slump flow for each of the trials. All doses of EAFS met both requirements of average diameter and numerical difference as stated in ASTM C1621 and are therefore considered successful.  
Figure 11: Results of the J-Ring flow and slump flow for SCC samples containing varying doses of EAFS.  
Figure 10: Variation of compressive strength of hydraulic cement for samples containing various dosages of CWP compared to a plain mortar mix.  
Introducing EAFS had no effect on the flow-ability of the SCC. All samples of SCC had ideal flow-ability as their H2/H1 ratio was 1.0. This, along with the J-Ring test results, show that introducing EAFS to a benchmark SCC mix at these doses does not undermine its rheological properties.  
Figure 13 shows the time it takes for samples containing different doses of EAFS to pass through a V-Funnel. It is clear that adding EAFS to a benchmark SCC mix significantly decreases its viscosity. A small dose of EAFS reduced the time required to clear the V-Funnel to 1 s, compared to 15 s for the benchmark SCC mix. All doses up to and including 15% EAFS were at or under the maximum desirable time stated in BS EN 12350-9 [18]. The results from this test shows a strong correlation between the EAFS content in a sample of SCC and its viscosity. The results of the 5, 10, and 15% replacements stand at odds with the findings of another study, which stated that the incorporation of EAFS would significantly increase the viscosity of the SCC mix [10].  
Figure 12: Numerical difference of the J-Ring flow and slump flow for SCC samples containing varying doses of EAFS.  
Figure 13: The time taken for SCC samples containing varying doses of EAFS to pass through a V-Funnel.
###### Abstract
As a result of these reactions, an electronic variation is produced on the surface that can be verified by the change in capacitance, electrical resistance, work function, or changes in its optical properties [9]. Among the wide variety of investigations on gas sensors, the binary compounds SnO\({}_{2}\) and ZnO [13] and the ternaries SmCoO\({}_{3}\) and ZnAl\({}_{2}\)O\({}_{4}\) have been the most studied [14]. In fact, several studies have indicated that the trirutile-type antimonates ZnSb\({}_{2}\)O\({}_{6}\) and CoSb\({}_{2}\)O\({}_{6}\) are good candidates for application as gas sensors due to their interesting detection properties [15]. As for the nickel antimoniate, NiSb\({}_{2}\)O\({}_{6}\), this is a trirutile-type oxide that conforms to the formula of the antimonates \(\mathrm{ASb}_{2}\)O\({}_{6}\) (where A is replaced, in this case, by the Ni cation) [16]. This family of compounds has interesting optical, catalytic, and electrical properties that allow them to be applied, for example, in lithium-ion batteries and in many other fields [16; 17]. These compounds have traditionally been synthesized using the ceramic method, obtaining different microstructures [17]. However, better results have been achieved using chemical methods, obtaining particles of the order of nanometers, which is a crucial factor in the ability to detect different types of gases [17; 18]. We conducted extensive literature research, finding out that NiSb\({}_{2}\)O\({}_{6}\) has already been studied as a potential sensor of static (for CO and C\({}_{3}\)H\({}_{8}\)) and dynamic (for LPG) atmospheres. However, we found no evidence that NiSb\({}_{2}\)O\({}_{6}\) has yet been dynamically tested in CO and C\({}_{3}\)H\({}_{8}\). Therefore, to contribute to the studies of this material as a gas sensor, NiSb\({}_{2}\)O\({}_{6}\) powders were synthesized to evaluate their ability to detect carbon monoxide and propane gases at different concentrations and temperatures, achieving excellent results. Additionally, based on the ability of the studied oxide to detect high and low concentrations of both toxic gases, an electronic circuit was successfully designed for a high-efficiency and fast response gas sensor in such atmospheres.
# Hybrid Encoding Scheme for AMBTC Compressed Images Using Ternary Representation Technique
###### Abstract  
Absolute moment block truncated coding (AMBTC) is a lossy image compression technique aiming at low computational cost, and has been widely studied. Previous studies have investigated the performance improvement of AMBTC; however, they often over describe the details of image blocks during encoding, causing an increase in bitrate. In this paper, we propose an efficient method to improve the compression performance by classifying image blocks into flat, smooth, and complex blocks according to their complexity. Flat blocks are encoded by their block means, while smooth blocks are encoded by a pair of adjusted quantized values and an index pointing to one of the \(k\) representative bitmaps. Complex blocks are encoded by three quantized values and a ternary map obtained by a clustering algorithm. Ternary indicators are used to specify the encoding cases. In our method, the details of most blocks can be retained without significantly increasing the bitrate. Experimental results show that, compared with prior works, the proposed method achieves higher image quality at a better compression ratio for all of the test images.  
image compression; AMBTC; ternary representation; \(k\)-means
###### Abstract  
We introduce a hybrid deep learning model for recognizing hand gestures from electromyography (EMG) signals in subacute stroke patients: the one-dimensional convolutional long short-term memory neural network (CNN-LSTM). The proposed network was trained, tested, and cross-validated on seven hand gesture movements, collected via EMG from 25 patients exhibiting clinical features of paresis. EMG data from these patients were collected twice post-stroke, at least one week apart, and divided into datasets A and B to assess performance over time while balancing subject-specific content and minimizing training bias. Dataset A had a median post-stroke time of 16.0 \(\pm\) 8.6 days, while dataset B had a median of 19.2 \(\pm\) 13.7 days. In classification tests based on the number of gesture classes (ranging from two to seven), the hybrid model achieved accuracies ranging from 85.66% to 82.27% in dataset A and from 88.36% to 81.69% in dataset B. To address the limitations of deep learning with small datasets, we developed a novel bilateral data fusion approach that incorporates EMG signals from the non-paretic limb during training. This approach significantly enhanced model performance across both datasets, as evidenced by improvements in sensitivity, specificity, accuracy, and F1-score metrics. The most substantial gains were observed in the three-gesture subset, where classification accuracy increased from 73.01% to 78.42% in dataset A, and from 77.95% to 85.69% in dataset B. In conclusion, although these results may be slightly lower than those of traditional supervised learning algorithms, the combination of bilateral data fusion and the absence of feature engineering offers a novel perspective for neurorehabilitation, where every data segment is critically significant.  
stroke; paresis; upper extremity motor impairment; hand gesture recognition; electromyography (EMG); machine learning; deep learning; CNN-LSTM; neurorehabilitation; healthcare
## 5 Experiments
### Complexity Analysis
#### 5.5.1 Computational Complexity  
In each training step, the algorithm samples \(N_{\text{candidates}}\) actions from the discrete policy distribution \(\pi_{\theta}\), computes the probability ratios \(r_{t}^{(l)}\), evaluates the clipping conditions and adjusts the clipping range based on the estimated KL divergence. Given that these operations are repeated for each state in a mini-batch, the computational complexity per training iteration can be expressed as: \(\mathcal{O}(B\cdot N_{\text{candidates}}\cdot F)\), \(B\) denotes the batch size and \(F\) represents the cost of a single forward/backward pass through the actor-critic network. Although this adds slight overhead compared to standard PPO, the small fixed number of candidates ensures tractability and maintains computational efficiency.
# Adaptive Beamforming Damage Imaging of Lamb Wave Based on CNN
## 1 Introduction
[22] proposed a semi-supervised deep convolutional neural network probabilistic imaging algorithm by using a CNN to automatically extract DI in the guided wave signals and output the probabilistic image of the damage, achieving the damage imaging detection of aluminum and composite panels, which demonstrated good material generalization. [PERSON] et al. [23] proposed a sparse guided wave imaging algorithm based on compressed perception and a deep learning model to solve the problem that the imaging quality is constrained by the number of transducers and achieved the quantitative assessment of corrosion damage of aluminum plates by establishing the mapping relationship between the guided wave signals and corrosion imaging of aluminum plates. [PERSON] et al. [24] introduced a deep-learning-based adaptive beamforming technique, which achieved high-resolution imaging of damage in plate structures by dynamically adjusting the apodization weights for pixel input data from the transducers using a Fully Connected Neural Network (FCNN). The above research shows that deep neural networks can be well-applied to structural damage detection by establishing a correct and generalized mapping from input to output. Although MVDR methods can improve damage imaging resolution and reduce imaging artifacts, they are, however, limited by the low imaging efficiency caused by the inverse computation of channel weights on the covariance matrix. We propose leveraging deep learning for data mapping and introducing a CNN to directly map delay-corrected Lamb wave scattering signals to the weights of each channel, thereby improving imagingperformance and efficiency. This approach provides technical support for the real-time online SHM of Lamb waves. The remainder of this paper is organized as follows: Section 2 describes the conventional DAS and MVDR imaging methods; Section 3 details the design and training strategy of the CNN neural network architecture proposed in this paper; Section 4 discusses the simulation and experimental results and quantitatively compares and analyzes the performance of the different imaging methods; and the conclusions are presented in Section 5.
## 5 Performance Test
### Application Testing  
Finally, the component is applied to a star sensor for testing, with a 450 nm narrowband spectrum as the background, and a 800 nm narrowband starlight as the target. The detection signal-to-noise ratio of the star sensor across different spectral bands is tested, and the results are presented in Table 6.  
Figure 23: Test site for multiple spectra.  
Figure 24: Filtering spectral ranges of 20 combinations: (**a**) combination 1–5; (**b**) combination 6–10; (**c**) combination 11–15; (**d**) combination 16–20.  
The test results indicate that the detection signal-to-noise ratio can be significantly enhanced by selecting appropriate spectral bands, thereby validating the engineering application value of the mechanism discussed in this paper.
## 5 System Prototype
### TTN Application Creation  
Once the gateway is configured and connected to TTN, the application that will collect the data can be added. For this, it is also necessary to register the device (LoRa node). The node in the present case only collects temperature and humidity data by sending the data to the server every minute, via the LoRa gateway. In order for the application to be able to collect the device data, it is necessary to proceed with the configuration of the device with the data of the access keys to the application, otherwise the added device will not be visible in the application. In this way, TTN ensures that packets sent by the device are effectively collected by the correct application (Figure 8).  
Figure 8: _Cont_.  
Figure 7: LoRa node in operation with reading data from the DHT22 sensor.  
The more expanded the LoRa gateway network, is the better coverage it will have, so once the application is created and the LoRa node device is configured, data packets can be received by more than one gateway. Since multiple gateways can receive the same LoRa RF data packets from a single end device, LNS (LoRa Network Server) eliminates duplicate data and removes all copies. Based on the Received Signal Strength Indication (RSSI) levels of identical messages (data packets), the network server typically selects the gateway that received the best RSSI message when transmitting a downlink message because, from the outset, that gateway it is the closest to the device that sent the message, ensuring a better quality of service [65].
# State of the Art and Trends Review of _Smart Metering_ in Electricity Grids
## 2 Trends of the _Smart Metering_ Systems  
The metering side of the distribution system has been the focus of most recent infrastructure investments. The first attempts at metering automatization, or _Automated Meter Reading_ (_AMR_), allowed utilities to remotely read the consumption records and basic status information from customers' premises [6]. Due to its one-way communication system, _AMR_ is limited to remote reading and cannot run additional applications, which prompted utilities to move towards the _Smart Metering_ or _Advanced Metering Infrastructure_ (_AMI_). _Smart Metering_ provides utilities with bidirectional communication to the meter but also the ability of evaluating the status of the grid. Recent _Smart Metering_ systems, equipped with an improved architecture, and working together with smart sensors and more sophisticated distributed control technology, allow utilities to perform grid control and management [7]. Figure 1 shows the evolution from _AMR_ to _AMI_ with lists of stakeholders and benefactors for each step.
## 3 Path Optimization  
Traditional path planning algorithms have common issues: during the path planning process, the vehicle is abstracted as a point mass, ignoring the vehicle's actual size. In narrow environments, the point mass model is unsuitable for trajectory tracking for certain types of wheeled mobile robots. By neglecting the exact size of the robot and treating it as a point mass, a point or circular-shaped vehicle can pass through, but vehicles with other shapes may have difficulty passing through obstacle nodes. However, due to the robot's actual size, its center of mass cannot get too close to obstacles. If the robot gets too close to an obstacle during movement, a collision may occur, affecting the robot's safety. For example, with a three-wheeled, independently steered, and independently driven robot, it is crucial to consider the robot's shape as a triangle. Therefore, non-point mass representation (i.e., arbitrary convex shapes) is necessary.
# Multi-Objective Pareto Optimization of Tensile Membrane Architecture for Energy Harvesting
## 3 Methodology  
The goal of the experiments performed in this study was to identify the TMA with the optimal power-generation and material-stiffness values for the wind environments of Inner Mongolia (in China) and Texas (in the USA), which are the regions of the world with the largest amounts of offshore wind generation. Thus, a total of 18 optimizations were performed, for 2 regions (Inner Mongolia and Texas); for three wind patterns (low-frequency, medium-frequency, and high-frequency); and for three basic types of TMAs (hyper, conic, and barrel vault). The Grasshopper plug-in Kangaroo [32] was used to implement the computational model of the TMA, and the Grasshopper plug-in Wallacei X [33], which employs the NSGA-2 algorithm [34] as the primary evolutionary algorithm, was used for the optimization.
# Optimal Path Routing Protocol for Warning Messages Dissemination for Highway VANET
## 4 Optimal Path Discovery Phase
### Optimal Path Discovery
#### 4.2.5 Scenario 5  
In this instance, we take into consideration the scenario shown in Figure 4 with the following requirements for the source and destination nodes:  
* Source node C6: S is the rear node.
* Destination node C1: A is the front node.
* Source and destination nodes travel in opposite directions, toward each other.  
This scenario is similar to Scenario 3 in terms of the positions of the source and destination nodes. However, the situation changes entirely because the two nodes are located on opposite sides of the road and travel in opposite directions, toward each other. The directional component is still essential in addition to the distance between nodes. Assume initially that there are two paths to go to the destination node C1: A in the scenario shown in Figure 4. The first path is C6: S >> CH >> C3: CH >> C2: CH >> C1: CH >> A, and the second, C6: S >> CH >> C5: CH >> C4: CH >> C1: CH >> A. The traditional greedy protocol selects the first path as \(O_{f}\). However, in our suggested OPRP, Equation (7) is employed to establish the priority between nodes in NG\({}_{5}\) when \(H\) (\(S,D\)) = 0 and the nodes are moving near one another. Therefore, Node C5:CH is preferred by OPRP as the \(Of\).
## 5 Experiments and Results
### Choosing the Speed of the Initial Flooding, \(\Delta_{t}\)  
A parameter that can be tuned in F&C and impacts its performance is \(\Delta_{t}\). We performed simulations to evaluate the impact of selecting such a parameter on signaling overhead and resource discovery time. Simulation parameters are shown in Table 5 and results are shown in Figure 9a,b. The simulations comprise the evaluation of five different fixed \(\Delta_{t}\) values and a variable version of \(\Delta_{t}\). The variable version behaves as follows: When the hop count between the source and target nodes is smaller than the repeal-flooding boundary, \(\Delta_{t}\) is the hop count from source to the current node times \(\delta\) where \(\delta\) is defined as the time necessary to transmit one packet from two adjacent nodes. Else, \(\Delta_{t}=1\times\delta\). As Figure 9a,b show, the use of a greater \(\Delta_{t}\) results in longer delays but also decreases signaling overhead since the initial flooding is contained earlier. On the other hand, for smaller values of \(\Delta_{t}\), the overall delay decreases since nodes wait less time to retransmit the RQ. However, the initial flooding is contained farther, thus increasing the signaling overhead. Variable \(\Delta_{t}\) shows an exponential behavior in the delay before hop 16, behaving just like \(\Delta_{t}=\delta\) afterward. Notice that variable \(\Delta_{t}\) behaves similarly to \(\Delta_{t}=8\delta\) in terms of signaling overhead, but variable \(\Delta_{t}\) outperforms it in terms of delay considering the whole distance range.  
Figure 8: Repeal-flooding boundary (optimized vs. estimated). (**a**) After 2 resource searches; (**b**) after 5 resource searches.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Nodes & 500 \\ Sim area & 3100\(\times\)3100 m\({}^{2}\) \\ Tx range & 250 m \\ Positioning & Random \\ Scenarios & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Simulation parameters.
## 4 Computational Experiments and Results Analysis
### Comparison Solution Algorithms  
In this section, the performance of the ILS-optimization and ILS-simulation with dispatch strategy is evaluated. The one-stage optimization algorithm is not considered due to its limitation in solving instances of low complexity. The computational results indicate that with the same set of instance data, the ILS-optimization algorithm takes 4 min per iteration, whereas ILS-simulation with dispatch strategy model only takes 0.625 s.  
Furthermore, in comparing the results of the models using the same generators and batteries, the optimization algorithm demonstrates the ability to avoid continuously activating the diesel generator. Instead, it maximizes the use of the diesel generator to charge the battery. Conversely, the dispatch strategy relies solely on renewable energy sources for charging.  
In summary, the ILS-optimization solution is more cost-effective, but the computational time required for the algorithm makes multiple iterations challenging.  
Figures 9 and 10 illustrate the results: the gray bar represents the energy charged to the battery, the red bar indicates diesel generation, the light blue bar represents the not served or unsupplied demand, the blue bar represents renewable wind generation, and the green and purple bars show battery discharges. The solid line depicts the demand. It is worth noting that in this case, the model did not install solar generators.
# A Review of Time-Scale Modification of Music Signals +
## 3 TSM Based on Overlap-Add (OLA)
### Artifacts  
The OLA procedure is in general not capable of preserving local periodic structures that are present in the input signal. This is visualized in Figure 4 where a periodic input signal \(x\) is stretched by a factor of \(\alpha=1.8\) using OLA. When relocating the analysis frames, the periodic structures of \(x\) may not align any longer in the superimposed synthesis frames. In the resulting output signal \(y\), the periodic patterns are distorted. These distortions are also known as _phase jump artifacts_. Since local periodicities in the waveforms of audio signals correspond to harmonic sounds, OLA is not suited to modify signals that contain harmonic components. When applied to harmonic signals, the output signals of OLA have a characteristic _wardling_ sound, which is a kind of periodic frequency modulation [6]. Since most music signals contain at least some harmonic sources (as for example singing voice, piano, violins, or guitars), OLA is usually not suited to modify music.  
Figure 3: The principle of TSM based on overlap-add (OLA). (**a**) Input audio signal \(x\) with analysis frame \(x_{m}\). The output signal \(y\) is constructed iteratively; (**b**) Application of Hann window function \(w\) to the analysis frame \(x_{m}\) resulting in the synthesis frame \(y_{m}\); (**c**) The next analysis frame \(x_{m+1}\) having a specified distance of \(H_{\text{a}}\) samples from \(x_{m}\); (**d**) Overlap-add using the specified synthesis hopsize \(H_{\text{s}}\).