esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Hybrid Topology Architecture
### Implementation
#### 3.2.7 Control User Interface  
For a clear understanding of the operation of our proposed solution for star and mesh adaptable topology coexistence in LoRaWAN networks, a control user interface has been developed. The UI allows users to visualize the current topology mode of all the end devices in the network. The topology mode can also be manually selected by the user. The UI permits users to see the RSSI, SNR, delay, last parameter update, and other parameter data history, as well as queuing custom downlink messages and configuring some of the parameters and thresholds of the automatic mode enabled by the topology manager. This UI was implemented using Apache2 in an instance of Ubuntu server. The UI connects to the MongoDB database to consult network parameters, which requires the connection driver to be configured with MongoDB to enable data petitions.
## 3 Numerical Results and Discussion
### Verification of Convergence and Correctness
Additionally, a 20 mm diameter hole was opened at an appropriate position on the cylindrical shell to cross the wires of various test instruments. Figure 7 presents the test schematic diagram of the coupled system between the circular stiffened plate, the cylindrical cavity, and the software parameter interface. The arrangement of the coupled system between the circular stiffened plate, the cylindrical cavity, and the experimental instruments in the sound pressure response test is shown in Figure 8. Figure 9 presents the sound pressure comparison between the experimental measurements and the results obtained using the present method of the coupled system between the circular stiffened plate and the cylindrical cavity at specific frequencies and at different observation points. The observation points are located at (0.14 m, 0.14 m) on the circular stiffened plate: observation point 1 is located at (\(-\)0.14 m, 0 m, 0.25 m) inside the cylindrical cavity, and observation point 2 is located at (0 m, 0.13 m, 0.22 m) inside the cylindrical cavity. From Figure 9, it can be observed that the maximum error among the experimental measurements and the results calculated using the present method is within an acceptable range of 7.49%, demonstrating the correctness of the unified analysis model of the vibro-acoustic characteristics of the coupled system established in this study. Figure 6: Variation curves of the natural frequencies of the coupling system among the composite laminated rotationally stiffened plate and the cylindrical-cylindrical cavities under different plate-beam coupling spring stiffness values for two boundary conditions. Figure 8: The arrangement of the coupled system between the circular stiffened plate, the cylindrical cavity, and the experimental instruments in the sound pressure response test. Figure 7: The test schematic diagram of the coupled system between the circular stiffened plate, the cylindrical cavity, and the software parameter interface. Figure 9: Sound pressure comparison between the experimental measurements and the results obtained using the present method of the coupled system between the circular stiffened plate and the cylindrical cavity at specific frequencies and at different observation points.
# A Study of a Gain Based Approach for Query Aspects in Recall Oriented Tasks
## 4 Experiments and Analysis
### Analysis of the Results
#### 4.3.1. Saturated GAIN Distribution  
In Figure 2a,d,h, we show that the value of the gain saturates to 1 for most query reformulation. This is more evident when we increase the number of documents \(N\) of Equation (6) from \(N=100\) up to \(N=10{,}000\). This behavior, due to the importance in Equation (1) that multiplies \(N\) numbers less than one, makes the GAIN not useful to discriminate the different query variants of an information need, since every variant will have gain equal to 1. In addition, when all the reformulations have the same gain, it is impossible to compute the Kendall \(\tau\) correlation to predict the performance (black dots with correlation value 0 in the figure). Being not saturated is not by itself a desirable feature for the gain measure. Nevertheless, the faster the gain saturates, the harder it is to discriminate between different formulations. In this sense, a GAIN measure capable of spreading better the options in the entire domain is preferable.
# Toward Collaborative Intelligence in IoV Systems: Recent Advances and Open Issues
## 4 Collaborative Computing in IoV
### Data Storage and Cache Management in IoV  
Like computing resources, the distribution of storage resources in the IoV is also uneven and increasingly unable to meet the needs of various large data services under collaborative intelligence. Both vehicles, RSUs, and MEC servers have limited storage space. Therefore, it is necessary to dynamically optimize storage data and cache management by fully considering the capacity and usage of storage resources of each node when computing tasks are uninstalled.  
In Ref. [87], a cache service registry on mobile entities and a new metric to evaluate the effectiveness of service discovery are proposed. Caching the service registry makes the content discovery process less interactive with the RSUs. The cached contents of vehicles are available to neighbors. This kind of vehicle cache sharing can improve the discovery rate of accessible services and the utilization rate of services. When integrated into the RSUs content caching scheme, users can switch from edge caching to vehicle content acquisition based on density and mobility. This allows for more flexibility in design and is a great advantage for V2X environments.  
[PERSON] et al. [88] proposed a collaborative edge caching scheme, which is a shared framework for communication, computation, and caching. It co-optimizes content placement and delivery through flexible trilateral cooperation among macro cell stations, roadside units, and intelligent vehicles in vehicle edge computing and networking. Data are cached and shared around infrastructure such as vehicles and RSUs in the form of multiple queues. Different strategies are designed for different time scales. The deterministic policy gradient deep learning algorithm is used to compute the optimal solution, which encourages and optimizes content sharing and reduces latency and packet loss.  
In the IoV in the smart city scenario, [PERSON] et al. [79] designed E-cache, an edge content caching framework including road side units and base stations. Combined with the deep spatiotemporal residual network, the traffic flow is predicted, and then the future service demand is predicted. This process can be divided into two stages: (1) the historical traffic flows are arranged into traffic flow vectors by a specific data embedding method and then spliced into tensors with different time dependencies. The future service demand is predicted after predicting the future traffic flow through the deep spatiotemporal residual network. (2) Initialize the edge content caching scheme according to the above selection, then modify the crossover and mutation, and select the reference point to get the optimal edge content caching scheme with the shortest executive time and lowest energy usage.
## 3 Results and Discussion
### Equivalent Electrochemical Circuits Analysis
(**a**) Steel bar without any corrosion inhibitor, (**b**) Calcium nitrite, (**c**) Multi-component corrosion inhibitor, (**d**) Organic corrosion inhibitor, (**e**) Inorganic corrosion inhibitor. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Specimen**} & \multicolumn{3}{c}{**Rct (k\(\Omega\cdot\)cm\({}^{2}\))**} \\ \cline{2-4}  & **5 d** & **30 d** & **60 d** \\ \hline Ref. & 93.85 & 18.21 & 5.63 \\ Calcium nitrite & 69.54 & 532.22 & 465.17 \\ Multi-component corrosion inhibitor & 730.51 & 1000.01 & 932.19 \\ Organic corrosion inhibitor & 119.26 & 691.15 & 723.38 \\ Inorganic corrosion inhibitor & 123.41 & 52.23 & 48.75 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Fitting parameter of EIS results prepared with different corrosion inhibitors. the late period. In the multi-component corrosion inhibitor group, maximum Rct reached 1000 k\(\Omega\cdot\)cm\({}^{2}\) at 30 days and maintained a high level after 60 days soaking. The greater Rct value prevents the steel bar from receiving a chloride attack. The Rct values of organic and inorganic corrosion inhibitors were 119.26 k\(\Omega\cdot\)cm\({}^{2}\) and 123.41 k\(\Omega\cdot\)cm\({}^{2}\), respectively. The resistance is still a little more than the reference group, and corrosion resistance cannot improve, even though long-term immersion [3]. Multi-component corrosion inhibitors could generate protectable films and protect steel bars from chloride ions attack in the long term.
## 3 Calculation of Free Vibration of Crankshaft System Substructures  
Due to the inherent characteristics of the structure's free vibration response, the free vibration calculation method is presented here to verify whether the substructure model can accurately reflect the dynamic behaviour of the crankshaft system. A six-cylinder diesel engine crankshaft system is used as an example to assess whether the established substructure model can effectively replace the finite element model in dynamic calculations. The free vibration of its substructure dynamic model is formulated as a generalized eigenvalue-eigenvector problem, expressed as below:  
\[\left(K-\omega^{2}M\right)\varphi=0 \tag{18}\]  
To efficiently solve this problem, the symmetry of the mass and stiffness matrices is utilized, transforming the problem into a standard eigenvalue-eigenvector problem. First, the mass matrix is decomposed using Cholesky decomposition, which transforms the matrix into the following:  
\[M=L_{M}L_{M}^{T} \tag{19}\]  
where \(L_{M}\) is a lower triangular matrix. The Equation (18) can then be further transformed into a standard semi-definite symmetric matrix eigenvalue problem, which is expressed as follows:  
\[A\varphi=\omega^{2}\varphi \tag{20}\]  
where \(A=L_{M}^{-1}K\left[L_{M}^{-1}\right]^{T}\).  
Since matrix \(A\) is a semi-definite symmetric matrix, the Jacobi iteration method can be used for efficient solving. The updated formula for the Jacobi iteration method is  
\[\varphi_{k+1}=\varphi_{k}-\alpha_{k}(A\varphi_{k}-\lambda_{k}\varphi_{k}) \tag{21}\]  
where \(\lambda_{k}\) is the eigenvalue at the \(k\)-th iteration, and \(\alpha_{k}\) is the step factor. The system's natural frequencies and corresponding mode shapes are ultimately obtained through multiple iterations.  
Since the finite element model can accurately represent the mechanical properties of the crankshaft system, the results from the finite element method are used as the verification standard to assess the accuracy of the substructure model and associated numerical calculation methods. The calculations were performed considering different coupling region sizes, as shown in Table 1. The calculation results for the natural frequencies of the crankshaft's free vibration are shown in Table 2. In the table, the main shaft necks are defined with one and three main nodes. Compared with previous studies, the validation results presented in this work are more convincing. The free vibration response was computed using Cholesky decomposition, and a detailed comparison was conducted with ANSYS 19.2 results by varying the number of master nodes and the size of the coupling region. These compar  
Figure 4: Master node and subsidiary node coupling relationship establishment.  
isons demonstrate the robustness and reliability of the proposed substructure modelling approach [27]. Compared to the model with one main node, the model with three main nodes demonstrates higher accuracy, with its natural frequencies being closer to those calculated using the ANSYS 19.2 finite element method.
## 4 Bionic Morse Coding Method  
The process of bionic Morse coding mimicking sea natural noise is briefly discussed in this section. It is slightly different from the process of underwater acoustic communication as it requires the communication signal to be perfectly imperceptible to sea natural noise. The structure is shown in Figure 2. We will concisely elaborate on the process step by step with the practical example of mimicking a humpback whale song.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Character** & **Freq.** & **Morse Code** & **Bionic Morse Code** & **Character** & **Freq.** & **Morse Code** & **Bionic Morse Code** \\ A & \(8.17\%\) & \(\bullet-\) & & N & 6.75\% & \(-\bullet\) & \(\blacktriangle\) \\ B & \(1.49\%\) & \(-\bullet\bullet\bullet\) & & O & 7.51\% & \(---\) & & \\ C & \(2.78\%\) & \(-\bullet-\bullet\) & & P & \(1.93\%\) & \(\bullet-\bullet\) & & \\ D & \(4.25\%\) & \(-\bullet\bullet\) & & Q & \(0.10\%\) & \(-\bullet-\) & & \\ E & \(12.70\%\) & \(\bullet\) & & R & \(5.99\%\) & \(\bullet-\bullet\) & & \\ F & \(2.23\%\) & \(\bullet\bullet-\bullet\) & & S & \(6.33\%\) & \(\bullet\bullet\bullet\) & & \\ G & \(2.02\%\) & \(-\bullet\bullet\) & & T & \(9.06\%\) & \(-\) & & \\ H & \(6.09\%\) & \(\bullet\bullet\bullet\) & & U & \(2.76\%\) & \(\bullet\bullet-\) & & \\ I & \(6.97\%\) & \(\bullet\bullet\) & & V & \(0.98\%\) & \(\bullet\bullet-\) & & \\ J & \(0.15\%\) & \(\bullet---\) & & W & \(2.36\%\) & \(\bullet-\) & & \\ K & \(0.77\%\) & \(-\bullet-\) & & X & \(0.15\%\) & \(-\bullet\bullet-\) & & \\ L & \(4.03\%\) & \(\bullet-\bullet\bullet\) & & Y & \(1.97\%\) & \(-\bullet-\) & & \\ M & \(2.41\%\) & \(--\) & & Z & \(0.07\%\) & \(-\bullet\bullet\) & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Representation of English language characters through Morse and bionic Morse codes.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Morse Codes** & **Bionic Morse Codes** \\ \hline Contains two elements dot \(\bullet\) and dash – & Contains four elements club \(\clubsuit\), diamond, heart and spade \(\clubsuit\) \\ \hline Longer sequences. & Shorter sequence. \\ \hline Fixed duration of elements. & Durations depends of carrier. \\ \hline Covert communication possible but chances & Covert communication possible no chances of being decryption. \\ \hline LPI constraint communication. & Perfect LPR constraint communication. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of Morse Codes and bionic Morse Codes.
# Applications of Nanomaterials in RFID Wireless Sensor Components
## 5 Applications
### Food Fresh Management  
RFID, which has strengths in inventory management, allows for convenient and effective monitoring of food quality [149]. Detecting food spoilage is crucial due to its potential consequences for human health and environmental issues. Monitoring of food involves the analysis of gases or bacteria occurring within the packaging [150].  
[PERSON] et al. conducted research on food quality through carbon dioxide monitoring using the intriguing properties of wheat gluten, a polar substance, at high relative humidity (Figure 12a) [99]. Wheat gluten, when in contact with carbon dioxide at 90% relative humidity, serves as a detecting material by changing its electrical and dielectric characteristics, effectively integrated into a UHF RFID system operating at 868 MHz.  
[PERSON] et al. proposed a biosensor using Dextrin-capped gold nanoparticles (d-AuNP) as markers for quality management of milk (Figure 12b) [106]. The resonant frequency shifts in capacitive loading, depending on the bacteria count in contaminated milk, were measured to analyze the concentration of _E. coli_ C3000. Detection through the attachment of conductive nanoparticles and cells enabled the development of a RFID sensor, demonstrating a low threshold of 5 log CFU/mL bacteria.  
Ammonia is a harmful gas generated by bacteria during the protein-containing food spoilage process. [PERSON] et al. applied highly conductive polymer PANI with an affinity for ammonia to a wireless sensor system (Figure 12c) [102]. The system, combining PANI thin films with high ammonia affinity with a RFID system, enabled wireless and long-distance monitoring of food quality, exhibiting the capability to detect small amounts  
Figure 11: (**a**) NFC-based wireless biosensor using rheostat [110]. (**b**) Graphite–polyurethane composite-based stretchable wireless system for sweat pH monitoring [119]. (**c**) Multifunctional hydrogel-interlayer RF/NFC resonator and its in vivo implementation performance [127]. (**d**) BodyNET stretchable sensor system that simultaneously monitors human body movement, pulse and breathing [120].  
of ammonia (3 ppm) at room temperature and atmospheric pressure. [PERSON] et al. enhanced detection performance by adding p-toluene sulfonate hexahydrate (PTS) to PANI (PTS-PANi). As shown in Figure 12d, PTS-PANI formed a porous structure with a larger surface area and smaller diameter, demonstrating sensitivity to analytes [103]. This detection material successfully achieved detection not only for ammonia but also for actual biological amines such as putrescine and cadaverine, showing \(\Delta\)R/R\({}_{0}\) values of 46% and 17%, respectively.
## 3 WFBRCSP Methods
### Fisher Score Feature Selection  
Due to the frequency band division, the number of features will increase exponentially.  
\[f=\left\{f^{(1)},f^{(2)},\ldots f^{(B)},\ldots,f^{(\Omega)}\right\} \tag{11}\]  
where \(f^{(B)}\) is the feature of the sub-band \(B\). To reduce the feature dimension, a feature selection algorithm is used to perform dimensionality reduction on the extracted features. The goal of Fisher score feature selection is to select a set of features that can distinguish between different classes. To achieve this, the discriminability of each feature with respect to the target class is calculated by maximizing the ratio of inter-class variance to intra-class variance for feature selection.  
Intra-class variance reflects the classification of samples within the same class. That is, the degree of dispersion between the sample points within each class.  
\[S_{W}(F_{c})=\frac{1}{M_{1}}\sum_{i=1}^{M_{1}}\left(f_{1}-\mu_{1}\right)^{2}+ \frac{1}{M_{2}}\sum_{i=1}^{M_{2}}\left(f_{2}-\mu_{2}\right)^{2} \tag{12}\]  
where \(f_{c}\) is the feature of a sample in class 1 or class 2, and \(\mu_{1}\) and \(\mu_{2}\) are the means of the corresponding feature \(f_{c}\) in class 1 and class 2, respectively.  
Inter-class variance reflects the difference in sample means between different classes. The larger the inter-class variance, the more distinct the difference between the two classes.  
\[S_{B}(F_{c})=M_{1}(\mu_{1}-\mu)^{2}+M_{2}(\mu_{2}-\mu)^{2} \tag{13}\]  
where \(\mu\) is the global mean of all samples.  
The Fisher score is a measure of the ability of feature \(f_{c}\) to distinguish between two classes, defined as the ratio of inter-class variance to intra-class variance.  
\[FisherScore(x_{c})=\frac{S_{B}(f_{c})}{S_{W}(f_{c})} \tag{14}\]  
Features are sorted in descending order based on their Fisher scores. RCSP features exist in pairs, so when selecting \(N\) features, the corresponding features should also be included, ensuring that the final selected features contain complete feature pairs.
## 2 Related Works
This framework was applied to a building HVAC system and used the EnergyPlus building energy simulation program to simulate a multizone building. The authors emphasize that their model can accurately predict the indoor temperature and sensitive HVAC cooling rate of an area in the building for a two-day horizon. Additionally, [PERSON], [PERSON] et al. [54] proposed a _seq2 seq_ (encoder-decoder) model based on LSTM for estimating the energy consumed by air conditioning. They described some HPs such as the number of LSTM layers for the encoder and decoder, the number of nodes, the activation function, and the regularization technique. The authors contrasted the proposed model with other algorithms and concluded that the LSTM-based _seq2 seq_ model is superior because it effectively combines past and future knowledge. From the researches cited above, we can deduce that different architectures based on DL models have been used to forecast the energy consumption of HVAC systems in buildings, such as MLP, RNN, and LSTM models. However, HVAC system data are a sequence of values recorded in a period of time (time series) [29], so future predictions of HVAC energy consumption depend not only on the current values in the inputs but also on the overall trend itself that past data maintain. This concept is known as long-term dependency, and both the MLP network and standard RNN do not have the capacity to handle this dependency. MLP models do not consider data as time series and RNNs do not have the ability to retain and consider long time lags during learning [55]. From this perspective, LSTM networks have the ability to transmit the cell state information through the different LSTM units, which is why the model is able to maintain the information of large historical sequences, as well as to consider the data as a sequence of ordered data [48; 56]. For this reason, an LSTM-based model is ideal for forecasting the energy consumption of HVAC systems in buildings. Taking into account the above, most of the previous studies only selected some hyperparameters for the development of DL-based models. However, few studies deeply examined the past behavior of the time series to perform a short-term forecast, as well as the use of regularization techniques such as dropout and early stopping within the training environment of the model. Therefore, it is necessary to build and evaluate a DL model based on LSTM in which different time lags are considered, various HPs settings are used, and the regularization techniques mentioned for the deep neural network (DNN) are incorporated, to identify the best configuration for performing daily forecasting of HVAC system energy consumption in buildings with complex usage.
## 2 Experimental Works
### Uniaxial Compressive Strength Tests  
The American Society for Testing and Materials (ASTM) standard [4] was followed to perform the tests. A fully servo-controlled 300 t press (CONTROLS 50-C52Z00 + MCCS 50-C8422/M) was used. The load velocity was fixed to 0.2 MPa-s\({}^{-1}\) in order to achieve failure between 5 and 10 min after the test began. On average, the uniaxial compressive strength was 68.99 MPa.