esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Model Performance for Each Observation Element
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Elements** & \multicolumn{2}{c}{**RMSE**} & \multicolumn{2}{c}{**MAE**} & \multicolumn{2}{c}{**R\({}^{2}\)**} & \multicolumn{2}{c}{**SMAPE**} \\ \hline \multirow{3}{*}{TOBS} & RF & 1.61 \({}^{\circ}\)C & RF & 0.93 \({}^{\circ}\)C & RF & 0.96 & RF & 5.51 \\  & AdaBoost & 3.51 \({}^{\circ}\)C & AdaBoost & 2.64 \({}^{\circ}\)C & Enet & 0.78 & AdaBoost & 11.71 \\  & RF & 1.73 \({}^{\circ}\)C & RF & 1.04 \({}^{\circ}\)C & RF & 0.96 & RF & 6.62 \\  & AdaBoost & 3.50 \({}^{\circ}\)C & AdaBoost & 2.62 \({}^{\circ}\)C & AdaBoost & 0.87 & AdaBoost & 12.38 \\  & RF & 1.64 \({}^{\circ}\)C & RF & 0.89 \({}^{\circ}\)C & RF & 0.96 & RF & 5.25 \\  & AdaBoost & 3.10 \({}^{\circ}\)C & AdaBoost & 2.26 \({}^{\circ}\)C & AdaBoost & 0.89 & AdaBoost & 10.74 \\  & RF & 2.95 Mbh & RF & 2.09 Mbh & TCN & 0.66 & RF & 6.47 \\  & ERT & 4.10 Mbh & AdaBoost & 3.01 Mbh & ERT & 0.29 & AdaBoost & 11.5 \\  & RF & 1.88 Mbh & RF & 1.32 Mbh & TCN & 0.64 & RF & 7.21 \\  & ERT & 2.62 Mbh & ERT & 1.83 Mbh & ERT & 0.28 & AdaBoost & 12.86 \\  & RF & 194.76 W/m\({}^{2}\) & RF & 155.79 W/m\({}^{2}\) & RF & 0.48 & RF & 15.53 \\  & ERT & 278.71 W/m\({}^{2}\) & ERT & 213.15 W/m\({}^{2}\) & ERT & 0.07 & ERT & 17.97 \\  & RF & 2.09 \({}^{\circ}\)C & RF & 0.75 \({}^{\circ}\)C & RF & 0.94 & RF & 12.47 \\  & SVR & 7.13 \({}^{\circ}\)C & SVR & 5.75 \({}^{\circ}\)C & AdaBoost & 0.75 & AdaBoost & 19.14 \\  & RF & 0.25 KPa & RF & 0.09 KPa & RF & 0.93 & RF & 4.38 \\  & TCN & 0.66 kPa & SVR & 0.46 kPa & Enet & 0.50 & BR & 6.43 \\  & RF & 5.03\% & RF & 2.99\% & RF & 0.93 & RF & 3.73 \\  & AdaBoost & 8.61\% & AdaBoost & 6.41\% & Enet & 0.82 & AdaBoost & 8.46 \\  & RF & 5.66\% & RF & 2.89\% & RF & 0.92 & RF & 2.68 \\  & AdaBoost & 10.18\% & AdaBoost & 7.21\% & Enet & 0.75 & AdaBoost & 9.01 \\  & RF & 5.34\% & RF & 3.27\% & RF & 0.92 & RF & 2.75 \\  & AdaBoost & 9.45\% & AdaBoost & 7.24\% & Enet & 0.76 & AdaBoost & 8.72 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The best and worst imputation methods for each meteorological observation element. missing rate is less than 60%, the RF imputation results for temperature (TBOS, TMIN, TMAX, and DPTP), humidity (RHUM, RHUMN, and RHUMX), and air pressure (PVPV) are highly consistent with the original data.
# A Study on Improving M2M Network Security through Abnormal Traffic Control
## 2 M2M Intelligent Network Background
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Port** & **Service** & **Port** & **Service** & **Port** & **Service** \\ \hline
21 & FTP & 465 & SMTP & 5632 & PC Anywhere \\
22 & SSH & 623 & IPMI & 5900 & VNC \\
23 & Telnet & 993 & IMAP+SSL & 6379 & Redis \\
25 & SMTP & 995 & POP3+SSL & 7777 & Oracle \\
53 & DNS & 1023 & Telnet & 8000 & Qconn \\
80 & HTTP & 1434 & MS-SQL & 8080 & HTTP \\
81 & HTTP & 1900 & UPnP & 8129 & Snapstream \\
110 & POP3 & 2323 & Telnet & 8443 & HTTPS \\
119 & NNTP & 3306 & MySQL & 9200 & ElasticSearch \\
137 & NetBIOS & 3389 & RDP & 11211 & MemCache \\
143 & IMAP & 5000 & Synology & 27017 & MongoDB \\
161 & SNMP & 5001 & Synology & 28017 & MongoDB Web \\
443 & HTTPS & 5432 & PostgreSQL & & \\
445 & SMB & 5560 &acle & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Shodan documented service interrogation filters. Here, malicious code variants continue to make it more difficult to respond. Distributed Reflector Denial of Service (DRDoS) uses the vulnerability of Simple Service Discovery Protocol (SSDP), and is a highly likely form of attack in M2M intelligent network environments as shown in Figure 2.
## 4 Results and Discussion
### Experimental Results of DCGAN Augmentation
#### 4.4.2 Comparison of Loss Curves for Different Datasets and Different Conditions  
The training loss curves of ShuffleNetV2-1.0 with and without transfer learning and with and without the SE module were compared, as shown in Figure 12. Without transfer learning (red line), the final loss value was higher, and the fluctuations during training were more severe than with transfer learning (green line). ShuffleNetV2-1.0-SE had lower loss values than ShuffleNetV2-1.0 on both datasets with transfer learning, indicating a better fit and higher classification accuracy.
## 2 Method
### Feature Extraction Using Autoencoders  
Autoencoders are neural networks that are unsupervised (they can be thought of as self-supervised) learning models. Autoencoders were first introduced in [50] as neural networks that learn the compressed internal representation of some input while reconstructing the same input. An autoencoder tries to learn a function \(g_{W,~{}b}(x)=\hat{x}\) by minimizing the reconstruction error of the input so that \(\hat{x}\) is as similar as possible to \(x\). The autoencoder achieves this by encoding the input into the feature space using an encoder subnetwork, then decodes it back using a decoder subnetwork [49; 51]. The learned representation (latent features) can be extracted and used in different machine learning tasks [52; 53; 54; 55]. Following the same notion, we use autoencoders in our framework to extract features \(Z\) of samples \(X^{k}\) that are used as input in the next module of the framework for training the bridging network of MLP regressors.  
The mathematics of an autoencoder can be formulated as defined in [56] as follows:  
\[E:X\to Z, \tag{1}\]  
\[D:Z\rightarrow\hat{X}, \tag{2}\]  
Such that (1) and (2) satisfy:  
\[\operatorname*{argmin}_{E,~{}D}\mathbb{E}[\Delta(X,~{}D\circ E(X))], \tag{3}\]  
where \(\mathbb{E}\) is the expected value of the reconstruction error function \(\Delta\) over the distribution of \(X\). \(E\) is the encoder function that maps the original input \(X\) to a latent representation \(Z\) at the bottleneck of the autoencoder. The decoder function \(D\) does the opposite by mapping the latent features \(Z\) at the bottleneck to the output of the decoder network approximating the original input \(X\). Training the encoder and the decoder networks involves optimizing the networks' parameters \((W,~{}b)\) through backpropagating the reconstruction error between the decoder's output and the original input.  
In our framework, we train a deep autoencoder network on the training set \(D^{Tr}\), such that the autoencoder learns a latent representation \(Z\) for all samples in the dataset  
Figure 1: Architecture of the proposed framework.  
while minimizing the mean-squared error (MSE) (as the error function \(\Delta\)) of the decoder reconstructions.
## 2 Theoretical Background
### Correlation Algorithm Flow  
When using DIC to measure the displacement and strain of the object, it is necessary to spray spots on the surface of the object. The sprayed spots can be used as the background pattern in the BOS technique. The two methods can be combined. The flow chart of the proposed correction method is shown in Figure 3.  
At first, after spraying the spots, when there is no heat source, the object to be measured is photographed to get the reference image. Next, several images of the spot pattern through the heat waves are taken as the background images with disturbance information. The PIV calculation between the background images and the reference image is performed to get disturbance displacement maps. These disturbance displacement maps are averaged over time to get the displacement distribution diagram of the main distortion.  
Second, the measured object is loaded and then images of the object through heat waves are taken. These images using the displacement distribution diagram of the main distortion are remapped to eliminate the main distortion in the images.  
Third, the images whose main distortion have been removed are averaged to eliminate random distortion. After that, the image with the heat wave disturbance removed is obtained. Then, performing  
Figure 2: Schematic diagram of optical distortion correlation method based on the BOS technique.  
the DIC measurement using this image will give the displacement and strain fields of the specimen, which have corrected the heat wave disturbance.
# A Combinational Buffer Management Scheme in Mobile Opportunistic Network
## 3 Buffer Management
### Preliminaries  
Mobile nodes change their locations over time in MONs. In general, evaluating the performance of the routing algorithm is based on the movement trajectories and paths formed by the motion of the nodes. In this paper, we use a real trace, Korea Advanced Institute of Science and Technology (KAIST) [28] and a new mobility model, Self-Similar Least-Action Human Walk (SLAW) [29]. We use a dynamic graph \(G=(V,E)\) to simulate the movement model of the nodes in MONs, where \(V\) is a set of nodes and \(E\) is the connection between any two nodes. Nodes exchange information with each other when they are in the communication range. We randomly specify the source node and the destination node in MON. Figure 1 shows the movement trajectory of the node \(A\) to the node \(D\). We assume that the contact time between nodes follows the exponentially distributed [21]. In addition, we assume that there are the following properties in MONs:  
* Each node has a limited buffer.
* Mobility of nodes is independent and nodes have different contact rates.
* The links have the same bandwidth.
* A short contact duration or low data rate will not complete the message transmission.
# Overview of Methods for Large-Scale RNA Synthesis
## 2 Methods of RNA Synthesis
### Ribozymes for RNA Production with Homogeneous Ends
#### 2.3.1 Hammerhead Ribozyme  
The hammerhead ribozyme is one of the most studied catalytic motifs found in viroids and satellite RNAs [112]. It is relatively small (40-50 nucleotides), with a secondary structure consisting of three helices branching from the conserved junction region (Figure 6A). The length of the helical regions can be significantly altered without influencing ribozyme activity (one helix can be reduced to only two base pairs). The overall fold of the ribozyme resembles a Y-shape, and it is stabilized by loop-loop interactions [113; 114]. A cleavage site is located at the 3\({}^{\prime}\)-end of the target RNA (next to helix I). It occurs after a NUX triplet (N stands for any nucleotide, and X is any nucleotide except guanosine), and the optimal cleavage sequence is AUC, GUC, UUC [110; 115]. Another important factor influencing the cleavage efficiency is the presence of divalent cations such as Mg\({}^{2+}\) or Mn\({}^{2+}\) at millimolar concentrations. They are involved in the proper folding of the ribozyme and serve as co-factors during the cleavage reaction [116].  
Figure 5: Production of homogeneous RNAs using ribozymes located at (**A**) 5\({}^{\prime}-\)end, (**B**) 3\({}^{\prime}-\)end, and (**C**) both ends of the target RNA. Cleavage sites are indicated with red arrows.  
The hammerhead ribozyme has been widely used to generate RNA molecules with homogeneous ends. The efficient cleavage of RNAs ranging from 36 to 140 nt has been observed for ribozymes located at either the 5\({}^{\prime}\) or 3\({}^{\prime}\)-end, with optimum activity at a 24 mM concentration of Mg\({}^{2+}\) ions [61]. The hammerhead ribozyme has been successfully applied in crystallographic studies to synthesize part of the human 7 SL RNA (Protein Data Bank--PDB: 1L9A), 4.5S rRNA domain IV (PDB: 1 DUH), and tRNAIe (PDB: 6 UFH) [117; 118; 119].
## 2 Mathematical Model
### Six-DOF Motion Equations  
Substituting the 1 st and the 2 nd order wave exciting forces as well as other external forces into 6-DOF motion equations, the Cummins time-domain equation [15] can be written as below:  
\[\begin{split}\{m+A_{\infty}\}\ddot{\zeta}(t)&=F^{(1)}(t )+F^{(2)}(t)+F_{M}(t)-c\dot{\zeta}(t)-B|\ddot{\zeta}(t)|\dot{\zeta}(t)-K\zeta(t) \\ &\qquad\qquad\qquad\qquad-\int_{0}^{t}h(t-\tau)\ddot{\zeta}(\tau )d\tau\end{split} \tag{35}\]  
where \(F^{(1)}(t)\) is the 1 st order wave excitation force/moment; \(F^{(2)}(t)\) is the 2 nd order wave excitation force/moment; \(F_{M}(t)\) is the mooring force; \(c\) is the linear damping matrix including the linear radiation damping effects; \(B\) is the quadratic damping matrix including the equivalent linear damping values to compensate for the viscous and eddy drag effects; \(K\) is the total stiffness matrix including the hydrostatic term as well as the stiffness from mooring lines; \(m\) is the structural mass; \(A_{\infty}\) is the added mass matrix at the infinite frequency; \(h(t)\) is impulse response function; \(\zeta(t)\), and \(\ddot{\zeta}(t)\) represent the displacement, velocity and acceleration time-series vectors in translational or rotational motions, respectively.  
Since the 6-DOF motion responses were calculated in the time domain, the 4 th Order Runge-Kutta method was used for numerical simulation [10]. The total time length of each numerical simulation was set to be 8000 s with the time step of 0.1 s.
## 7 Discussion
### Practical Contributions
In scenarios where the data are sparse, noisy, or not fully representative of the system's operational range, the identification performance may be compromised. Real-time implementation challenges * While the method aims to reduce computational costs, the real-time implementation of the designed membership functions and fuzzy rules can still be challenging, especially for systems with stringent real-time processing requirements. The need for quick adjustments to membership functions in real-time scenarios might not be feasible with the proposed method. * Generalization to different types of systems
* The proposed method has been demonstrated to be effective for specific examples such as a thermal mixing process and a binary distillation column. However, its applicability and effectiveness for a broader range of nonlinear multivariable systems, particularly those with different physical properties and behaviors, need further validation. * Parameter tuning and optimization
* The method involves several parameters, such as the weighting factors and the design criteria for the membership functions. The process of tuning these parameters to achieve optimal performance can be complex and might require significant trial and error or advanced optimization techniques, which could limit its practicality. In summary, while the proposed multidimensional identification method provides notable advancements in terms of accuracy and efficiency, it is essential to recognize and address these limitations to ensure its broader applicability and effectiveness in various real-world scenarios.
## 3 The Proposed MMGAT Methods  
### Building Instance Correlation Graph for MMGAT  
MMGAT begins by constructing an instance correlation graph to represent musical instruments. While music audio is initially converted into a two-dimensional spectrogram, the relationship among these spectrograms remains a linear temporal connection. To directly capture the relationships between instruments within a musical piece, we create a graph structure, which allows for the identification of similarities between instances with similar instruments, as well as distinguishing unrelated instances.  
To build this instance correlation graph, we extract features from the spectrograms using a pre-trained deep neural network called VGGish. In this paper, we use VGGish, which has 128-dimensional feature representations. Then, a 1 s music clip is transformed into a 128-dimensional feature. Based on this VGGish model, every music audio is transformed into \(n^{\star}\)128-dimensional features, where \(n\) is the duration of the music, measured in seconds, and this matrix has a corresponding label vector which has multiple activated label components. In this paper, we use graph structures to represent the correlations between audio segments. Specifically, we use VGGish to map an audio clip into multiple audio features. Suppose an audio clip is n seconds long; VGGish converts the audio for each second into a 128-dimensional feature vector. Thus, an n-second audio clip is mapped into n*128-dimensional features, with each feature representing an instance. We then construct an instance correlation graph based on these instances. Each node in the graph represents an instance, and the node features are the 128-dimensional features of the instance. The edges of the instance correlation graph are computed based on the similarity between instances, thus forming the instance correlation graph. To construct the two-dimensional relation of instruments based on the feature matrix, we build an instance correlation graph; in this graph, a node expresses a music clip (the corresponding row in the feature matrix), and every graph has \(n\) nodes, which is equal to the number of clips in the corresponding music audio. The edges between node (i, j) are built based on the corresponding weighted similarity of (i, j) in the feature matrix; this similarity can be calculated as follows:  
\[Similarity_{cosine(\theta)}=\frac{f_{i}\cdot f_{j}}{\left|\left|f_{i} \right|\right|\cdot\left|\left|f_{j}\right|\right|}=\frac{\sum_{i=1}^{n}f_{i} \cdot f_{j}}{\sqrt{\sum_{i=1}^{n}{\left(f_{i}\right)^{2}}}\times\sqrt{\sum_{j= 1}^{n}{\left(f_{j}\right)^{2}}}} \tag{1}\]  
\[Weight\_Similarity_{i,j}=\frac{Similarity_{i,j}}{\sum_{\left(\text{ij} \right)}Similarity_{i,j}} \tag{2}\]  
where we use cosine similarity in this paper. In the experiments, we discuss the effectiveness of different types of similarity measures. Based on the constructed feature matrix and the corresponding weighted similarities using as edges, the instance correlation graphs for each music audio instance in the dataset are constructed. Next, we show the structure of the proposed MMGAT.
# Sustainable Additives for the Production of Hydrogen via Sodium Borohydride Hydrolysis
## 3 Results
### Critical Comparison between the Additives  
From the comparison between the gravimetric density and hydrogen generation rates depicted in Figure 4, it is possible to observe the general trends obtained with the four additives used in this study. Gravimetric density refers to the amount of a substance that can be contained per unit mass. Thus, in the context of hydrogen production using NaBH\({}_{4}\), gravimetric density determines how much hydrogen can be produced from a given amount of NaBH\({}_{4}\).  
As seen, the BCD admixture provided a poor performance, with lower values in comparison to the experiment without an additive. To this respect, the control experiment demonstrated that NaBH\({}_{4}\) hydrolysis leads to low reproducible values given that replicate tests reported high variability.  
For its part, SDS provided replicable experiments, reaching reasonable values of density, close to 1.5%. Still, the hydrogen rate was low, implying the necessity of either (i) increasing the quantity of this additive or (ii) exploring new routes of catalyst to promote a higher release rate. In the case of PAM, the results demonstrated its high gravimetric density and hydrogen rate. In addition, these experiments exhibited the lowest variations  
\begin{table}
\begin{tabular}{c c} \hline
**Additive** & **Lag Time (s)** \\ \hline SDS & 50 \\ \hline CMC & 70 \\ \hline BCD & 80 \\ \hline PAM & 115 \\ \hline None & 211 \\ \hline \end{tabular}
\end{table}
Table 2: Lag times for different additives.  
Figure 4: Comparison between gravimetric density and H\({}_{2}\) rate. \(\bullet\) Control, \(\bullet\) BCD additive, \(\bullet\) SDS additive, \(\bullet\) CMC additive, \(\bullet\) PAM additive.  
in terms of reproducibility. However, PAM required the longest time to achieve a high yield, implying that its use could be mainly recommended in processes in which low and controllable hydrogen requirements are needed. Finally, CMC, again, has been presented as the additive with optimal performance among the materials studied given that it allowed the highest values of velocity with acceptable values of gravimetric density.  
As a general trend, it is seen that the use of additives helps to achieve higher stability and replicability in the results. Nevertheless, future optimization works are necessary to make progress on the use of additives to promote NaBH\({}_{4}\) as a hydrogen carrier in order to obtain gravimetric densities close to the maximum values expected from sodium borohydrides (10.8 wt%).
# Determining the Safety Level of State Roads: An Italian Case Study
###### Abstract  
This study aims to establish an effective approach for evaluating the safety performance of road infrastructure. Road safety levels are typically quantified using safety performance indicators. However, due to the stochastic nature of accidents, many safety performance indicators cannot adequately and completely describe reality. Therefore, predictive methods based on regression models are widely used. This approach also allows for the identification of latent risk conditions in the infrastructure, even in the absence of accidents. Among available approaches, the Highway Safety Manual (HSM) methodology is chosen for its synthesis of validated highway research and best practices for incorporating safety into both new design and rehabilitation. For this study, a preliminary new version of HSM is used. The application of this method, which combines a predictive model with observed accidents through an empirical Bayesian approach, requires a calibration process that is crucial to tailoring this method to the specific study context. In this research, the predictive model is calibrated for single carriageway roads with one lane per direction across the Italian national network. Following calibration, the safety indicators are evaluated. The results obtained according to different indicators are compared to show the importance of adopting this method to counteract the regression to the mean of observed crashes. In fact, the method, supported by empirical Bayesian analysis, enables the identification of high-risk sections of the road network, selecting more sections that would be neglected by traditional indicators based solely on observed crashes. Finally, a possible approach to prioritizing sites for inspection based both on the excess of crashes and the Safety Potential (SAPO) is proposed. In addition, SAPO is adjusted to local conditions to account for the specific context and the decreasing trend of accidents over the years.  
road safety; road crashes; predictive methods; crash modification factors; safety performance indicators; field inspections; road management +
Footnote †: journal: Applied Sciences
## 4 Deep RL-Assisted Resource Allocation
### Proposed DQL-Based Approach
#### 4.2.1 State Space  
The wireless environment is denoted by a set of variables analogous to the resource allocation issue, this variable set with all available values are referred to as the state space, and it is denoted by \(S\). In our case, the state space comprises the resource allocation-related information of users and the channel gain from multiple cells, which follows a certain optimization strategy. More specifically, the state space \(S\) contains \(i\) states, i.e., \(S=\{S_{1},S_{2},\ldots,S_{i}\}\). As described above, the agent \(i\) constructs its state \(S_{i}^{(t)}\) using information from the direct channel gain and cross coupling links. We denote by \(S_{i}\triangleq\mathcal{U}_{i}\) the set of users connected to each base station and define \(S_{i}\triangleq\{\mathcal{U}_{1},\ldots,\mathcal{U}_{i}\}\). At the initial interaction with the environment, agent \(i\) sends its interference signal by using cross coupled power \(h_{j\to i}^{(t)}(k)p_{j}^{(t)}(k)\) from BS \(j\) at receiver \(i\). In this case the agent \(i\) uses the weights of sum-rate \(w_{i}\) to prioritize its interference signal. For direct information exchange, the agent \(i\) uses the direct gain \(h_{i\to i}^{(t)}(k)\) at each time step \(t\). For the coupling strategy, agent \(i\) uses the cross-coupling links \(h_{j\to i}^{(t)}(k)\) at each time step \(t\). For \(S_{i}\triangleq\mathcal{U}_{i}\) user set in \(m\) cells and \(n\) frequency tones, we will have \(m\times\mathcal{U}_{i}\times(n+1)\) state size. Hence, the DQN agent uses \(S(m,n)\) vector along with \(\mathcal{U}_{i}\) of all users in a network as state and then takes action.