esa-sceva/satcom-chunk-collection huggingface dataset:
# Preddiagnosis of Heart Failure (HF) Using Deep Learning and the Korotkoff Sound
## 3 Materials and Methods
### Deep Learning (DL) Network
#### 3.5.6 Transfer Learning  
Deep neural networks require a large amount of data to ensure model analysis accuracy and resistance to overfitting. Unfortunately, most researchers struggle to collect enough data during the data collection process, limiting the performance of DL models [65]. The emergence of transfer learning lends strong support to the advancement of DL algorithms. It allows the transfer of knowledge learned from a domain pattern to a new domain, and performs classification [66]. The ImageNet database, which contains 14 million images classified into 1000 categories, has been used to train our deep neural network pre-training model. To match the classification task presented in this paper, we reduced the number of fully connected layer outputs from 1000 to 2 and set the fully connected layer's initial weight and bias to 10.
# Efficient Verification of Cryptographic Protocols with Dynamic Epistemic Logic
## 5 Verification of the Protocol  
### The Protocol Targets  
In the initial state, \(S\) and \(R\) have their own key. Only \(S\) has a secret message \(m\). Our goal is that \(R\) will have \(m\) but \(T\) can not have \(m\) after the execution of all actions. So, we give the target formulas.  
**Target 1**: After the execution of the last action \(\delta_{3}\), \(R\) will have secret message \(m\).  
\[\mathfrak{M}\models\bigwedge_{\theta\in\Theta}[Crypto_{Intr},\delta_{3}( \theta)]K_{Ag}const_{R}m(\theta)\]  
**Target 2**: \(T\) does not have \(m\) at the initial state. After execution of the last action \(\delta_{3}\), \(T\) still does not have secret message \(m\).  
\[\mathfrak{M}\models\bigwedge_{\theta\in\Theta}\
eg has_{T}m(\theta)\to[Crypto_{ Intr},\delta_{3}(\theta)]K_{Ag}\
eg const_{T}m(\theta)\]  
If this protocol satisfies Target 1 and 2, then the protocol is secure.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Action** & **Direction** & **Message** & **_Pre_ & \(\mathbf{Pos_{1}}\) & \(\mathbf{Pos_{AL}}\) \\ \hline \(\sigma_{1}\) & \(S\to In\) & \(m_{s}\) & \(const_{S}m_{s}\) & \(m_{s}\in I_{In}\) & \(\sigma_{1}^{+}\) \\ \(\delta_{1}\) & \(Out\to R\) & \(m_{s}\) & \(has_{Out}m_{s}\) & \(m_{s}\in I_{R}\) & \(\delta_{1}^{+}\) \\ \(\sigma_{2}\) & \(R\to In\) & \(m_{sr}\) & \(const_{R}m_{sr}\) & \(m_{sr}\in I_{In}\) & \(\delta_{1}^{-}\), \(\sigma_{2}^{+}\) \\ \(\delta_{2}\) & \(Out\to S\) & \(m_{sr}\) & \(has_{Out}m_{sr}\) & \(m_{sr}\in I_{S}\) & \(\delta_{2}^{+}\) \\ \(\sigma_{3}\) & \(S\to In\) & \(m_{r}\) & \(const_{S}m_{r}\) & \(m_{r}\in I_{In}\) & \(\delta_{2}^{-}\), \(\sigma_{3}^{+}\) \\ \(\delta_{3}\) & \(Out\to R\) & \(m_{r}\) & \(has_{Out}m_{r}\) & \(m_{r}\in I_{R}\) & \(\delta_{3}^{+}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Action model in the protocol.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Action** & **Direction** & **Message** & \(\mathbf{Pre}\) & \(\mathbf{Pos_{1}}\) & \(\mathbf{Pos_{AL}}\) \\ \hline \(\sigma_{1}\) & \(S\to In\) & \(m_{s}\) & \(const_{S}m_{s}\) & \(m_{s}\in I_{In}\) & \(\sigma_{1}^{+}\) \\ \(\delta_{1}\) & \(Out\to R\) & \(m_{s}\) & \(has_{Out}m_{s}\) & \(m_{s}\in I_{R}\) & \(\delta_{1}^{+}\) \\ \(\sigma_{2}\) & \(R\to In\) & \(m_{sr}\) & \(const_{R}m_{sr}\) & \(m_{sr}\in I_{In}\) & \(\delta_{1}^{-}\), \(\sigma_{2}^{+}\) \\ \(\delta_{2}\) & \(Out\to S\) & \(m_{sr}\) & \(has_{Out}m_{sr}\) & \(m_{sr}\in I_{S}\) & \(\delta_{2}^{+}\) \\ \(\sigma_{3}\) & \(S\to In\) & \(m_{r}\) & \(const_{S}m_{r}\) & \(m_{r}\in I_{In}\) & \(\delta_{2}^{-}\), \(\sigma_{3}^{+}\) \\ \(\delta_{3}\) & \(Out\to R\) & \(m_{r}\) & \(has_{Out}m_{r}\) & \(m_{r}\in I_{R}\) & \(\delta_{3}^{+}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Action model including intruder’s actions.
## 3 Results
### Crop Yield Prediction
#### 3.1.2 RTM Integration with CGMs
\begin{table}
\begin{tabular}{p{34.1 pt} p{34.1 pt}} \hline \hline
**Paper** & **Summary** \\ \hline \hline
[45] & The study proposed early winter wheat yield forecasting under low-information systems of crop cultivars, management practices and uncertain weather conditions. They used the semi-empirical radiative transfer model PILOTE to assimilate green LAI using the particle filtering method. This data assimilation resulted in accuracy improvements for LAI estimation (–1 to 0.2 m\({}^{2}\)/m\({}^{2}\)) and crop yield prediction (3 to 6%). \\ \hline
[44] & The Random Forest (RF) method was employed to emulate RTM (SCOPE) simulations using machine learning surrogate models (RTM-RF). RTM-RF-based analysis of LAI and PLSR leaf traits demonstrated impressive precision in predicting canopy-level traits (leaf traits \(\times\) LAI) retrieval, including canopy chlorophyll (R\({}^{2}\) = 0.80), nitrogen (R\({}^{2}\) = 0.85) and Vmax,27 (R\({}^{2}\) = 0.82), using airborne hyperspectral images. Notably, R\({}^{2}\) between photosynthetic rate and maize grain yield was highest with 0.81 and lowest with 0.68 between soil nitrogen fertilization rates and yield. \\ \hline
[21] & The validation of PROSAIL for LAI retrieval yielded an R\({}^{2}\) value of 0.5 with an RMSE of 0.8 m\({}^{2}\)/m\({}^{2}\) against ground LAI using hyperspectral aerial images via vegetation index techniques. Grain yield based on normalized difference red edge (NDRE) showed the highest accuracies with R\({}^{2}\) values in the range of 0.62 to 0.83 for two maize fields having different irrigation techniques. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summarized information of RTM-based Crop Yield Prediction Articles. Figure 5: Summary flow chart of RTM-based CYP characteristics.
## 3 Finite Element Analysis  
### Parametric Analysis  
The mechanical behavior of DS-CFCST stub columns under axial compression was investigated by developing full-scale FE models using a validated FE modeling technique. The analysis included many factors, such as a constant column diameter (\(D\) = 500 mm) and length (\(L\) = 1500 mm), along with varying degrees of infilled DS concrete strength (\(f_{\text{cr},r}\)), steel yield strength (\(f_{y}\)), steel ratio (\(\rho\)), and DS replacement ratio (\(r\)). One hundred models were generated in total, and the variations in the parameters used are described in Table 3.  
Figure 5 shows the load-axial strain curves obtained from the numerical investigation. The graphs clearly demonstrate that the axial compression response of DS-CFCST stub columns is significantly influenced by the strength of DS concrete, steel, steel ratio, and DS replacement ratio. The ultimate bearing capacity of DS-CFCST columns, similar to CFCST columns, is greatly affected by the concrete strength, ste  
Figure 4: Comparative analysis of load–axial strain curves between FE simulations and experimental data. (**a**) [PERSON] et al. (2020); (**b**) [PERSON] et al. (2020); (**c**) and (**d**) [PERSON] et al. (2020).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(D\) (mm) & \(L\) (mm) & \(\rho\) & \(R\) & \(f_{\text{cr},\rho}\) (MPa) & \(f_{y}\) (MPa) \\ \hline
500 & 1500 & 0.02, 0.04, 0.06, 0.08 & 1 & 30, 50, 70 & 235 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Material and geometric characteristics of the specimen for FE parametric analysis.  
ratio, particularly when considering different DS replacement ratios. Increasing the yield strength of the steel from 235 MPa to 345 MPa results in an approximate 20% increase in the ultimate bearing capacity. By increasing the strength of the infilled DS concrete from 50 MPa to 70 MPa, there is a projected 20% improvement in its ultimate bearing capacity. Furthermore, when the steel ratio is raised from 0.02 to 0.04, 0.06, and 0.08, the axial compressive ultimate bearing capacity experiences an approximate rise of 12%, 33%, and 54% accordingly. However, the DS replacement ratio has a little effect on the elastic stiffness and ultimate bearing capacity of the columns, with effects being less than 10% in comparison to other significant parameters.
# A Cloud-Based Framework for Large-Scale Log Mining through Apache Spark and Elasticsearch
## 5 Experiments  
In this research, the NASA Advanced Information System Technologies (AIST) cloud platform was used to set up a testbed for the framework effortlessly and efficiently. An image with required software, e.g., HDFS, Spark, and Elasticsearch, was deployed in advance to facilitate the creation of a log-mining cluster on demand. Conveniently, whenever the cluster needs more computing resources to process growing logs, we can start a new instance from the preconfigured image. In our experiments, six virtual machines were launched for the log-mining process. Each machine was configured with four CPU cores, 16 GB of memory, and a 2.4-GHz clock speed (Figure 9). Operations provided by the AIST cloud platform make it possible and efficient to group these virtual machines into a cluster.  
The entire 2014 PO.DAAC logs were used to test the performance of the framework. The log volume was \(>\)32.8 GB and the logs consisted of \(>\)154 million records in total. Figure 10 shows the distribution of logs in the 12 months in 2014. The user traffic changed dramatically from month to month and, in February, there were 6,727,710 (\(\sim\)7 million) logs in total and the log volume was \(>\)3.5 GB.  
Three experiments were conducted to evaluate the performance of the framework. Firstly, the February 2014 monthly log was randomly selected and processed with HashPartitioner and LogPartitioner separately to validate the proposed partitioner. Secondly, the entire logs were processed by the proposed framework to test performance. The log-mining algorithm implemented only on Elasticsearch with the same procedure was used for comparison. Then, the monthly log was processed with multiple clusters consisting of different numbers of workers to validate the scalability of the framework. Finally, we discussed the evaluation results.  
Figure 10: Monthly log distribution of the Physical Oceanography Distributed Active Archive Center (PO.DAAC).  
Figure 9: The testbeds for the proposed framework.
# Fast, Lightweight, and Efficient Cybersecurity Optimization for Tactical-Operational Management
## 2 Problem Modeling and Formulation
### Determining Value of FLECO Parameters  
In the course of designing and developing FLECO, multiple adjustments were required to ensure that the algorithm operated as intended and yielded valuable solutions. FLECO is designed for organizations that are implementing the CyberTOMP framework to manage cybersecurity at the tactical and operational levels, and to ensure its comprehensive validity, we collaborated with two organizations in the design and validation process. The first organization is a non-technological small or medium-sized enterprise (SME), consisting of fewer than 40 employees and only 2 departments. The meetings held to discuss the cybersecurity actions to be implemented include only three to five individuals. The second organization is a public entity with over 300 direct employees, 5 departments, and 11 primary functional areas. This organization has several outsourcing contracts, and its teams comprise in-house as well as external personnel. Meetings held to determine the set of cybersecurity actions involve 15-20 individuals. Both organizations are implementing CyberTOMP to varying degrees and have encountered the challenges outlined in Section 1. During the multifunctional cybersecurity workforce meetings, where the different teams must reach an agreement on which cybersecurity actions to implement and to what depth, these teams were unable to find a solution in these two companies. The main reason is the large number of existing combinations, which is unmanageable manually; a secondary reason is that the different teams were unable to search for combinations that simultaneously satisfied more than one objective: complying with the constraints defined at the strategic level related to CyberTOMP metrics; maximizing similarity with respect to the currently enforced combination to leverage previously completed work; and maximizing all the assets' global cybersecurity state. Without the possibility of finding valid combinations that maintain a balance between all objectives, the work meetings planned in CyberTOMP to ensure the required holism are meaningless. Therefore, the main motivation of our work is the design of a technological mechanism that enables the quick obtainment of solutionsthat can be shared in the meetings planned in CyberTOMP, and thus, achieve the necessary holism in these organizations.  
During the design phase of FLECO, we conducted hundreds of executions, most of which were unsuccessful. We made numerous modifications and decisions in collaboration with the aforementioned organizations, such as determining the value of the weights, defining strategic constraints, specifying the requirements for a solution to be deemed acceptable, and defining the genetic operators, among others [22]. At the end of the experimentation phase, these organizations participated in validating the efficacy of the proposal and testing its effectiveness in their specific use cases.
## 2 Method to Extract the Rule-Based Knowledge
### PR Generation through NAAR Mining and Prolog
#### 2.3.2 Rule Inference Using Prolog  
A set of NAARs can simply put on Prolog to inference rules and obtain conclusions in accordance with PRs that we have obtained. Those NAARs are treated as facts in rule-based systems. In PRSs, note that it is possible to deduce new final conclusions by creating a chain and linking two or more rules through a predicate logic approach or rule of inferences as long as the rule chain is non-circular. For instance, suppose we have three NAARs: \(R_{1}:\)\(X_{1}\to Y_{1}\), \(R_{2}:\)\(X_{2}\to Y_{2}\) and \(R_{3}:\)\(X_{3}\to Y_{3}\). Thus, we can create a rule-chain, \(R_{1}\to R_{2}\to R_{3}\), from those rules as long as \(Y_{1}=X_{2}\), \(Y_{2}=X_{3}\) and \(Y_{3}\
eq X_{1}\). According to predicate logic, we can deduce a new conclusion, \(R_{1}:\)\(X_{1}\to Y_{3}\), from the rule-chain based on the concept of hypothetical syllogism by creating the Prolog rule format to make conditional statements about our rule bases clear for programming the inference engine.  
**Example 1.**  
Suppose we have two rules which create a rule chain:  
\(\mathcal{R}_{1}:\ (bmi:\ obese)\rightarrow(sbp:\textit{hypertension}1)\).  
\(\mathcal{R}_{2}:(sbp:\textit{hypertension}1)\rightarrow(mri:\ abnormal)\).  
Then, \(bmi\) is the attribute and _obese_ is the value. An attribute-value pair will look like av(Attribute, Value); where Attribute and Value are simple atoms. Then, the fact structure in Prolog looks like rule(LHS, RHS). rule(lhs[(av(bmi,obese))], rhs(av(sdp,hypertension_J))). rule(lhs[(av(sdp,hypertension_J))]), rhs(av(mri_brain,abnormal))).  
While the conclusion deduced for a rule-chain can be inferred through hypothetical syllogism logic, as represented by the following Prolog rule format:  
Conclusion(X,Z):-rule(X, Y), rule(Y, Z).
# Q-RPL: Q-Learning-Based Routing Protocol for Advanced Metering Infrastructure in Smart Grids
###### Abstract  
Efficient and reliable data routing is critical in Advanced Metering Infrastructure (AMI) within Smart Grids, dictating the overall network performance and resilience. This paper introduces Q-RPL, a novel Q-learning-based Routing Protocol designed to enhance routing decisions in AMI deployments based on wireless mesh technologies. Q-RPL leverages the principles of Reinforcement Learning (RL) to dynamically select optimal next-hop forwarding candidates, adapting to changing network conditions. The protocol operates on top of the standard IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL), integrating it with intelligent decision-making capabilities. Through extensive simulations carried out in real map scenarios, Q-RPL demonstrates a significant improvement in key performance metrics such as packet delivery ratio, end-to-end delay, and compliant factor compared to the standard RPL implementation and other benchmark algorithms found in the literature. The adaptability and robustness of Q-RPL mark a significant advancement in the evolution of routing protocols for Smart Grid AMI, promising enhanced efficiency and reliability for future intelligent energy systems. The findings of this study also underscore the potential of Reinforcement Learning to improve networking protocols.  
machine learning; reinforcement learning; smart grids; routing protocol for low-power and lossy networks (RPL) 2024 24 4818. [[https://doi.org/10.3390/s24154818](https://doi.org/10.3390/s24154818)]([https://doi.org/10.3390/s24154818](https://doi.org/10.3390/s24154818))  
Footnote †: journal: _[[https://doi.org/10.3390/s24154818](https://doi.org/10.3390/s24154818)]([https://doi.org/10.3390/s24154818](https://doi.org/10.3390/s24154818))
# Speech Sentiment Analysis Using Hierarchical Conformer Networks
## 3 Methods
### Loss Function  
The loss function widely used in sentiment classification tasks is the softmax function [36]; however, we adopt a combination of a central loss function [37] for computing depth features and a softmax function for sentiment classification to enhance the performed of the model and obtain a higher accuracy. The central loss function learns and discovers the centers of the feature vectors of each class, and defines the distances of between features as well as their consistent class centers. We utilize the mean \"\(\lambda\)\" setting to compute and adjust the update loss function for inter/intra-class distances. We measure the minimum distance between classes with the central loss function and measure the maximum distance between classes with the softmax loss function, which can be expressed as:  
\[L_{s}=-\sum_{i=1}^{m}\log\frac{e^{W^{h}_{t_{i}}\cdot x_{i}+b_{y_{i}}}}{\sum_{ j=1}^{m}e^{W^{h}_{t_{i}}\cdot x_{i}+b_{y_{i}}}} \tag{15}\]  
\[L_{c}=\frac{1}{2}\sum_{i=1}^{m}||x_{i}-c_{y_{i}}||_{2}^{2} \tag{16}\]  
We use \"\(n\)\" and \"\(m\)\" to denote the number of classes and the minimum batch size to the classifier, and \(c_{y_{i}}\) denotes the center of the class \(y_{i}\) in the i-th sample. We use the \"\(\lambda\)\" notation for central loss to compute the minimum distance required to avoid misclassification in the real-time case. The global loss we use in our model is:  
\[L=L_{S}+\lambda L_{C} \tag{17}\]  
Here \"\(\lambda\)\" is used as a hyperparameter. Experiments demonstrate that combining the central loss function gives better results.
## 2 Waveguide Fabrication  
The flexible polymer waveguides were fabricated using polynorbornene by the \"photodressing\" technology proposed by Sumitomo Bakelite Co., Ltd. (Tokyo, Japan) [35]. The fabrication process is depicted in Figure 1. First, the material of the core layer is coated onto the substrate, followed by a laser direct inscription (LDI) process for waveguide patterning. The LDI process allows for uniform exposure over a large area without being limited by the size of the mask. Then, the core layer is removed from the substrate after a baking process and sandwiched between upper and lower cladding layers and this process is also finished with baking. Last, a 25 \(\upmu\)m-thick polyimide film was laminated on both sides of the waveguide for protection. No wet development is needed throughout the fabrication processes, which effectively minimizes the sidewall roughness of the core and makes the fabrication process more environmentally friendly. Without the detrimental effect of sidewall roughness, the propagation loss of the waveguide can be significantly optimized [19].  
The refractive index of the core is about 1.55 at a wavelength of 850 nm, and the refractive index of the horizontal and vertical cladding is 1.53 and 1.52, respectively [36]. As shown in Figure 2, the core size of the waveguide is about 43 \(\times\) 44 \(\upmu\)m and each waveguide group contains single-row 12-channel waveguides with a core pitch of 250 \(\upmu\)m, which coincides with that of the commercially available VCSELs, photodiode arrays, and MMF ribbons. The waveguides are connectorized using single-row 12-channel polymer mechanically transferable (PMT) connectors in accordance with the recommendation of IEC 62496-4-1 (Figure 2b), which guarantees a low-loss interconnection with 12-channel fiber MT connectors in accordance with IEC 61754-5 (Figure 2c).  
The fabricated 1 m and 1.7 m long waveguide films illuminated by a red identification light are shown in Figure 3. Both have a length of about 43.5 cm and a width of 5.5 cm. The thickness of them with the polyimide protection films is about 110 \(\upmu\)m, which is equivalent to that of a sheet of printing paper and is over 60% thinner than the MMF ribbon, which usually has a thickness of about 300 \(\upmu\)m. Figure 4 demonstrates the flexibility of the fabricated 1 m long and 1.7 m long waveguide film, respectively. In order to minimize the in-plane bending loss and make full use of the area of the waveguide film, the minimum in-plane bending radius of the 1 m long and 1.7 m long waveguides is designed to be 24 mm and 21 mm, respectively.
# Calculation of Displacement-Dependent Active Earth Pressure for Deep Excavations in Soft Soil
## 2 Materials and Methods
### Test Specimens  
In order to eliminate the influence of complex factors such as the preconsolidation pressure and the structure of clay on the measurements, only the behavior of the reconstituted soil was studied in the tests. The soil sample, which belonged to the ancient Qinhuai River floodplain landform, was taken from a construction site (Figure 4) in Nanjing, China, at depths of 8.5-9.5 m (the red circle in Figure 4). The averaged values of the main physical properties of the sampling location are listed in Table 1.  
Field soil was dried in a constant-temperature oven for no less than 12 h; then, the dry soil was crushed and filtered with a 1 mm aperture screen to obtain soil powder, as shown in Figure 5a. The powder was stored in a covered container. The void ratio and saturation density of the specimens were 1.03 and 1.86 g/cm\({}^{3}\), respectively; therefore, a sample with a diameter of 50 mm and height of 100 mm equated to 265 g dry powder. The specimens were prepared with dry soil powder in five layers using three split molds constituting circular cross-sections, as shown in Figure 5b. The dry soil specimens, created with the three split molds, were moved to a vacuum-saturated cylinder, as shown in Figure 5c. An atmospheric pressure vacuum was applied to the specimens for more than 1 h. Water was poured into the cylinders slowly, and the specimens were soaked in the water for no less than 12 h. Figure 5d shows the specimen after it was removed from the mold.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Soil Strata**} & **Layer** & **Specific** & **Plasticity** & **Liquidity** & **Unit** & **Void Ratio** & **Water** & **Cohesion** & **Friction** \\  & **Thickness** & **(m)** & **Gs** & **Index I\({}_{\mathbf{\gamma}}\)** & **Index I\({}_{\mathbf{\gamma}}\)** & **Weight** & **\(e\)** & **Content** & **\(\mathbf{\Omega}\) (\%)** & **Angle \(\mathbf{\varphi}\) (\%)** \\ \hline Fill & 2.61 & 2.72 & 13.4 & 0.64 & 19.12 & 0.78 & 28.4 & 20.1 & 17.4 \\ Slity Clay & 2.13 & 2.72 & 12.9 & 0.57 & 19.01 & 0.81 & 28.5 & 17.4 & 19.8 \\ Silty & 3.63 & 2.71 & 9.9 & 0.68 & 19.33 & 0.72 & 26.5 & 14.8 & 29.9 \\ Muddy Clay & 10.52 & 2.74 & 14.7 & 1.14 & 18.42 & 1.07 & 39.02 & 10.9 & 11.9 \\ Silty Clay & 9.69 & 2.74 & 14.9 & 0.43 & 19.59 & 0.74 & 25.3 & 49.3 & 18.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physical properties of the excavation site.  
Figure 3: Schematic diagram of GDS triaxial testing system.