esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Proposed Network  
### Architecture and Parameters of the Novel Short-Memory Sequence-Based Model  
The detailed information of the architecture is shown in Figure 5 Table. The architecture of the novel short-memory sequence-based model consists of three parts: (1) the feature extractor; (2) the sequence modeling module; (3) the decoding module.  
In order to extract the best possible representation of feature sequences from images and retain useful information at different levels, we design a skip connection structure based on CNN, consisting a plain CNN network and three shortcut connections, as shown in Figure 6. To be specific, a convolutional layer and a pooling layer are first designed to obtain low-level features and down-sample the feature dimension. The intermediate convolution part has six convolutional layers with three shortcut connections, which are implemented to reuse the activations of the previous layers. In addition, we perform downsampling directly by strided convolutional layers instead of the max pooling layers, which increases the number of network-trainable parameters and improves the expressiveness of the model. The main purpose of this part is to achieve high-level information extraction through the stacked convolutional layers. The network ends with two convolutional layers with appropriate parameters to achieve cross-channel information interaction and strengthen the connection among adjacent frames in the feature map.  
After the convolutional layers, in order to capture local dependencies from both the front and back directions and obtain a higher and more abstract feature level, we employ a deep bi-directional BasicRNN (Bi-BasicRNN) network with 2 layers for sequence modeling, whose output sequences are the probability distributions over 14 classes in \(N^{\prime}\). Finally, CTC is adopted to train the whole network and translate the per-frame prediction by the recurrent layers into a label sequence.
## 1 Introduction
However, this analytical approach towards designing optimal transmission strategy requires post-deployment feedback as well to justify its effectiveness. Another trend is observed in literature [17; 18] to address these pivotal issues related to data transmission within WBAN during routing data towards the sink. Here, optimal transmission power is chosen adaptively  
Figure 1: Three-tier architecture of WBAN. at runtime as part of the routing decisions. Hence, in this case the complex mathematical analysis to obtain optimal transmission power could over-burden such resource-constrained network. In this work, a transmission strategy for multi-hop intra-BAN communication has been formulated offline prior to network deployment based on the following input conditions: energy level of nodes, event occurrence depicted in terms of data packet generated by nodes to deliver to sink, packet transmission rate which reflects the effect of heat generation due to network activities and link quality. The designed policies are incorporated into the nodes and thereafter a multi-hop routing protocol has been devised where routing decision follows the pre-computed strategy. In this work the contributions are as follows. * An MDP formulation to develop the transmission strategy for multi-hop communication within WBAN which not only focuses on obtaining optimal transmission power subject to the input conditions but also reflects the necessity of multi-hop data transmission as well. * A routing algorithm is designed based on the effective transmission strategies obtained by solving MDP formulation prior to deployment. These transmission strategies correspond to the system states that the network may undergo after deployment in terms of energy level, event occurrence, packet transmission rate and link quality. The nodes route data following a simple but effective routing algorithm and make a decision to transmit via multi-hop or single-hop based on suitable transmission power. * The effectiveness of the designed solutions is verified with extensive simulations, and performance is evaluated with respect to the existing multi-hop protocol for WBAN. The paper is organized as follows. Section 2 provides an overview of the state-of-the-art works reported in literature followed by a discussion of MDP in Section 3. Section 4 presents the proposed work in detail along with the system model and MDP formulations. Section 5 illustrates the experimental particulars including the simulation setup and analysis of experimental results. Finally, concluding remarks are presented in Section 6.
## 2 Materials and Methods
### Calculating Sky Temperature Numerically  
Sky temperatures were needed for buildings simulation programs such as Autodesk CFD and EnergyPlus. To calculate sky temperature the horizontal infrared radiation intensity (Horizontal_IR) in Wh/m\({}^{2}\) is needed and can be calculated[29; 30] as following:  
\[\text{Horizontal\_IR}=\varepsilon\ \sigma\ \text{T}^{4} \tag{1}\]  
where;  
Horizontal_IR = horizontal infrared radiation intensity (W/m\({}^{2}\))  
\(\sigma\) = Stefan-Boltzmann constant = \(5.6697\times 10^{-8}\) (W/m\({}^{2}\cdot\)K\({}^{4}\))  
T = dry bulb temperature (K)  
\(\varepsilon\) = sky emissivity (0 \(<\varepsilon<\) 1) is given by  
\[\text{SKY}_{\text{emissivity}}=\left(0.787+0.767\times\ln\left(\frac{\text{ Temperature}_{\text{dewpoint}}}{273}\right)\right)+0.0224\text{N}-0.0035\text{N}^{2}+0.00028 \text{N}^{3} \tag{2}\]  
where;  
Temperature dewpoint = dewpoint temperature (K)  
Figure 6: Sky temperature and ground emissivity used in the computational fluid dynamics (CFD) analysis.  
\(N\) = opaque sky cover (tenths), where sky cover: the expected amount of opaque clouds covering the sky valid for the indicated hour where \(N\) equals 0 for clear sky and 10 for overcast sky.  
The default calculation for sky temperature can be calculated from the horizontal infrared radiation intensity as follows [31; 32]:  
\[\text{Sky Temperature}=(\text{Horizontal\_IR}/\sigma)\times 0.25-\text{Temperature}( \text{Kelvin}) \tag{3}\]  
where:  
\[\text{Sky Temperature}=\text{sky radiative temperature}\ (\text{${}^{\circ}$C}).\] \[\text{Horizontal IR}=\text{horizontal infrared radiation intensity (Wh/m${}^{2}$)}.\] \[\text{Temperature (kelvin)}=\text{Temperature conversion from kelvin to celsius (273)}.\] \[\text{Calculating sky temperature numerically for each month to compare it with CFD results. Daily sky temperature changes is minimum which can be presented in monthly bases, the calculation for August will be presented in detail here; clear sky (\(N=0.5\)), temperature dry bulb = 10 \({}^{\circ}\)C, temperature dewpoint = 4 \({}^{\circ}\)C.  
Now the sky temperature can be calculated.  
\[\text{Temperature dry bulb}=273+10=283\text{ K}\] \[\text{Temperature dew point}=273+5=278\text{ K}\text{:}\] \[\text{Sky emissivity}=0.8\] \[\text{Horizontal IR}=0.8\times 5.669710\text{-}8\times(2834)=291\text{ W/m${}^{2}$}\] \[\text{Sky Temperature}=(\text{Horizontal\_IR}/\sigma)\times 0.25-273=267.7-273=-5.3 \text{ ${}^{\circ}$C}\text{ (for August winter)}.\]  
The lack of specific sky temperature equations for all areas of the world leads to the impossibility in determining the best existing formula but the comparison among the models can be useful to address the choice of users in building energy simulations and engineering applications. It was found that heating and cooling annual energy demands can be affected by significant percentage differences, ranging from \(-10\%\) to \(+19\%\) for tropical climatic conditions, from \(-10\%\) to \(+13\%\) for dry climatic conditions, from \(-19\%\) to \(+28\%\) for mild temperate climatic conditions and, finally, from \(-43\%\) to \(+83\%\) for snowy conditions [16].
## 4 Results and Discussion
Based on the test results, the model evaluations for each method are as follows: ELNET has an RMSE of 274.217 Joule, SVR has an RMSE of 82.572 Joule, GBR has an RMSE of 51.213 Joule, LR has an RMSE of 43.438 Joule, RR has an RMSE of 41.258 Joule, and KRR has an RMSE of 38.758 Joule. These comparison results are also presented in Table 12, with the last row mentioning the results of the model with the combination of CNN-LSTM. Additionally, for the comparison between the actual and predicted values for each method, refer to Figure 20a for ELNET, Figure 20b for SVR, Figure 20c for GBR, Figure 20d for LR, Figure 20e for RR, and Figure 20f for KRR. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & **RMSE (Joule)** & **MAE (Joule)** & **MSE (Joule)** & **MAPE (\%)** \\ \hline Elastic Net Regression & 274.217 & 272.205 & 0.026 & 2.6 \\ \hline Support Vector Regression & 82.572 & 69.823 & 0.007 & 0.7 \\ \hline Gradient Boosting Regression & 51.213 & 41.882 & 0.004 & 0.4 \\ \hline Linear Regression & 43.438 & 34.700 & 0.003 & 0.3 \\ \hline Ridge Regression & 41.258 & 32.404 & 0.003 & 0.3 \\ \hline Kernel Ridge Regression & 38.758 & 29.841 & 0.003 & 0.3 \\ \hline
**CNN-LSTM** & **32.436** & **25.382** & **0.002** & **0.2** \\ \hline \hline \end{tabular}
\end{table} 
Table 12: The result of comparison with other methods. Figure 19: Losses during model training: (**a**) Model 1; (**b**) Model 2. Figure 18: Results of an energy prediction model with the CNN–LSTM combination: (**a**) Neuron 10; (**b**) Neuron 50.
## 3 Experiments
### Experiments on Inria Dataset
#### 3.3.3 Comparison with Other Filter Pruning Approaches  
We compared our approach with several state-of-the-art criteria to show the effectiveness of the importance weights as a pruning criterion in deep CNNs, which are briefly described below:  
1. \(l_{1}\)-norm: A score is computed for each \(i^{th}\) filter using the \(l_{1}\)-norm as \(s_{i}=\|F_{i}\|_{1}=\sum_{j=1}^{c}\lvert k_{j}\rvert\). Filters with a low score are considered weak filters and, hence, pruned [50].
2. Random pruning: Filters are removed randomly [41].
3. Entropy-based pruning: The filter importance is calculated based on [PERSON]'s entropy measure. If a filter has low entropy, this filter is considered unimportant and, consequently, removed [40].  
To have a fair comparison with the state-of-the-art methods, we report the following measures: Val. loss, drop in Val. Acc., reduction in FLOPs, and reduction in the number of parameters under the same pruning rate, i.e., 50%. Table 2 presents the results obtained with the Inria dataset. Obviously, the proposed approach hardly worsened the original accuracy (\(-0.04\%\) to 0.32% accuracy drop) when (72.25%-83.68%) of the network parameters and (64.18%-84.98%) of the FLOPs were removed. When compressing TeranusNet, the proposed approach with random initialization showed the largest FLOP reduction rate, i.e., almost 85%, in comparison with other filter pruning approaches with a drop of only 0.32% in the Val. Acc. In comparison with random filter pruning, we observed that our approach can reduce 10.68% more FLOPs with only a little larger drop in accuracy. When pruning the TeranusNet by entropy pruning, the accuracy drop was less than that of the proposed approach with random or chaotic initialization. Unfortunately, at the same time, the reduction in FLOPs was insignificant, indicating that many filters were retained despite their minor influence on the performance of the model. The \(l_{1}\)-norm induced a sparse representation and strongly diminished the FLOPs, yet the performance was still pure. This result specifies the weakness of the norm in pruning weak filters: it may force the network to pay too much attention to prune many filters, which is harmful for model generalization. Similar observations hold for U-Net, where the proposed approach outperformed the competing methods with a larger reduction in the FLOPs and parameters without sacrificing the model accuracy too much. Based on the overall performance, we can conclude that the proposed approach can obtain promising results with all initialization strategies.
## 4 Experiment
### Evaluation Metrics  
The most commonly used evaluation metrics in the semantic segmentation field are adopted, which are Mean Intersection over Union (mIoU), Mean Pixel Accuracy (mPA), Mean Dice Coefficient (mDice), Recall (Re), F1 Score (F1), Frames Per Second (FPS), and in terms of model complexity, Giga Floating-point Operations Per Second (GFLOPs) and Params. mIoU measures the average of the ratio of the intersection and union of the predicted mask and the ground truth mask for all types, mPA is the average pixel accuracy for all types, and mDice is used to measure the average overlap between the predicted segmentation and the true segmentation for each category. Re represents recall, F1 represents the F1-Score, FPS is the number of frames processed per second by the model, GFLOPs is the number of floating-point operations per second, and Params is the number of parameters produced by the model.
# MEMS-Based Vibration Acquisition for Modal Parameter Identification of Substation Frame
###### Abstract  
As a critical component of substations, the substation frames are characterized by significant height and span, which presents substantial challenges and risks in conducting dynamic response tests using traditional sensors. To simplify these difficulties, this paper introduces an experimental method utilizing MEMS sensor-based vibration acquisition. In this approach, smartphones equipped with MEMS sensors are deployed on the target structure to collect vibration data under environmental excitation. This method was applied in a dynamic field test of a novel composite substation frame. During the test, the proposed MEMS-based vibration acquisition method was conducted in parallel with traditional ultra-low-frequency vibration acquisition methods to validate the accuracy of the MEMS data. The results demonstrated that the MEMS sensors not only simplified the testing process but also provided reliable data, offering greater advantages in testing convenience compared with traditional contact methods. The modal parameters of the substation frame, including modal frequencies, damping ratios, and mode shapes, were subsequently identified using the covariance-driven stochastic subspace identification method. The experimental methodology and findings presented in this paper offer valuable insights for structural dynamic response testing and the wind-resistant design of substation frames.  
MEMS vibration acquisition; substation frame; modal parameters; stochastic subspace identification method +
Footnote †: journal: Applied Sciences
_Int. J. Mol. Sci._**2013**, _14_, 23597-23613; doi:10.3390/ijms141223597  
International Journal of  
**Molecular Sciences**  
**ISSN 1422-0067**  
www.mdpi.com/journal/ijms  
_Article_  
**New Mononuclear Cu(II) Complexes and 1D Chains with 4-Amino-4_H_-1,2,4-triazole**  
**[PERSON] 1, [PERSON] 1, [PERSON] 1, [PERSON] 1, [PERSON] 1, [PERSON] 1, [PERSON] 2, [PERSON] 2 and [PERSON] 1,* 1**  
Institute of Condensed Matter and Nanosciences, Universite Catholique de Louvain, Molecules, Solids and Reactivity (IMCN/MOST), [PERSON], 1, Louvain-la-Neuve 1348, Belgium; E-Mails: [EMAIL_ADDRESS] (M.M.D.); [PERSON]_ADDRESS] (Y.B.); [EMAIL_ADDRESS] (D.G.); [EMAIL_ADDRESS] (B.T.); [EMAIL_ADDRESS] (K.R.); [EMAIL_ADDRESS] (D.A.S.)  
Institute for Advanced Materials, Physicochemical Processes, Nanotechnology & Microsystems, NCSR Demokritos, Athens 15310, Greece; E-Mails: [PERSON]ADDRESS] (E.D.); [PERSON]ADDRESS] (Y.S.)  
Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +32-10-47-28-26; Fax: +32-10-47-23-30.  
_Received: 17 September 2013; in revised form: 29 October 2013 / Accepted: 30 October 2013 / Published: 3 December 2013_  
**Abstract:** The crystal structures of two mononuclear Cu(II) NH\({}_{2}\)trz complexes [Cu(NH\({}_{2}\)trz)\({}_{4}\)(H\({}_{2}\)O)](AsF\({}_{6}\))\({}_{2}\) **(I)** and [Cu(NH\({}_{2}\)trz)\({}_{4}\)(H\({}_{2}\)O)](PF\({}_{6}\))\({}_{2}\) **(II)** as well as two coordination polymers [Cu(\(\mu_{2}\)-NH\({}_{2}\)trz)\({}_{2}\)Cl]Cl\(\cdot\)H\({}_{2}\)O **(III)** and [Cu(\(\mu_{2}\)-NH\({}_{2}\)trz)\({}_{2}\)Cl] (SiF\({}_{6}\))\({}_{0.5}\)\(\cdot\)1.5H\({}_{2}\)O **(IV)** are presented. Cationic 1D chains with bridging bis-monodentate \(\mu_{2}\)-coordinated NH\({}_{2}\)trz and bridging \(\mu_{2}\)-coordinated chloride ligands are present in **III** and **IV**. In these coordination polymers, the Cu(II) ions are strongly antiferromagnetically coupled with \(J=-128.4\) cm\({}^{-1}\) for **III** and \(J=-143\) cm\({}^{-1}\) for **IV** (\(H=-J\Sigma S_{i}S_{i+1}\)), due to the nature of the bridges between spin centers. Inter-chain interactions present in the crystal structures were taken into consideration, as well as \(g\) factors, which were determined experimentally, for the quantitative modeling of their magnetic properties.  
**Keywords:** coordination polymers; copper; 1,2,4-triazole; magnetic properties; crystal engineering
## 5 Conclusions  
Many advanced wireless technologies deal with health management to monitor patients and predict risks regarding their condition. The medical data are transmitted to the care center, and a team of professional doctors receives alerts regarding their patients. This paper presents a reliable network management approach with bidirectional routing for Cloud-based networks using the internet of secured medical things, which aims to decrease the complexity factor in data transmission for medical applications with the least processing cryptosystem. It distributes the secured and verified keys between medical sensors using the STS algorithm, and performs the lesson overhead for achieving privacy and authentication. Additionally, it offers a bidirectional routing method and minimizes the time factor in transferring the medical data towards the cloud tier. Moreover, it also provides a hybrid security mechanism to protect and authenticate the medical store among cloud to physician communication. The proposed model was tested, and numerical results demonstrated its significant improvements in network resiliency and security. However, it has been observed that the proposed model lacks information about medical devices when the IoT network is mobile. In such situations, sensitive information can be easily or erroneously used, and physicians can obtain inaccurate readings of medical data. Thus, in future work, we aim to improve the proposed model's ability to predict channel error rates before forwarding data by using a robust mobility support scheme and further minimizing the computational overhead for both on-body and off-body communication. Moreover, a testbed will be needed to evaluate the proposed approach under realistic network technologies.  
Conceptualization, A.R., K.H. and S.M.F.; Methodology, A.R., K.H.; Software, A.R., S.M.F.; Validation, J.L., L.P.; Formal Analysis, J.L.; Investigation, A.R., L.P.; Resources, J.L.; Data Curation, S.M.F., K.H.; Writing--Original Draft Preparation, A.R., K.H.; Writing--Review & Editing, S.M.F., J.L.; Visualization, L.P.; Supervision, J.L.; Project Administration, A.R.; Funding Acquisition, A.R., J.L. All authors have read and agreed to the published version of the manuscript.  
There is no external funding for this research work.
## 3 Results and Discussion
### PST Removal from Bivalves
#### 3.3.1. Time 0 and Natural Depuration
possibility of GTX5 oxidation at N1, originating GTX6, is not ruled out, since this type of reaction has already been reported in mussels _Mytilus_ spp. [39; 45]. Based on previously compiled information [8], dcSTX could be formed from STX, GTX5 or GC3 (in all cases by hydrolysis). In the present work, STX was not quantifiable and GTX5 seems more likely to be involved in other reactions. On the other hand, the higher abundance of GC toxins (hydroxybenzoyl derivatives) produced by _G. catenatum_ has been reported [28; 50]. In the present work GC toxins were not quantified, since LC-MS is a more appropriate method for this. Nevertheless, some authors have reported a higher abundance of dcSTX in bivalves, to the detriment of a decrease in GC3, contrary to what was observed in _G. catenatum_ (higher relative proportion of analogous GC) [28; 51]. This suggests conversion of GC3 to dcSTX through hydrolysis involving the R4 group. This reaction has been attributed to the enzymatic activity of a carbamoylase, targeting this subgroup. Nonetheless, the possibility of converting GC3 into dcSTX under the acidic conditions of the extraction process (acid hydrolysis) should not be disregarded [51]. In our experiments, after 5 days of feeding with _G. catenatum_ all mussels analysed revealed cumulative levels of toxicity higher than those legally established--800 ug STX.2 HCI eqv/Kg [49]. Similar values for the total toxicity were obtained in both sets of experiments, with mean values of 973 and 981 ug STX.2 HCI eqv/Kg. The results from natural deprivation experiments suggest faster clearance in the second set of experiments than in the third, corresponding to 33 \(\pm\) 11% and 16 \(\pm\) 3%, respectively, after 48 h. Another observation was a greater variation of the results in the second set, compared to the third set. Several conditions may contribute to this, and it is practically impossible to control all of them, mainly because the experiments involved live bivalves. In addition to the conditions inherent to the mussels used in each set of experiments (e.g., physiology and well-being), there are other parameters that may affect the efficiency of purification, namely temperature, salinity, oxygen levels and the particles suspended in water [29]. _M. edulis_ are classified as rapid detoxifiers, the faster clearance observed in an initial phase being commonly attributed to the elimination of toxins not yet assimilated and incorporated by the tissues [48; 52].
## 4 Results and Discussion
### Perforation Pressure
On the other hand, some elements became weaker than others due to the Weibull probability distribution. The weaker elements inside the shale rock tended to break under the same stress field. When the four cases were compared, it was found that the perforation pressure of 15 MPa created more cracks and hence was beneficial to the SC-CO\({}_{2}\) hydro-jet fracturing (Figure 19). Furthermore, three more main cracks initiated at the front of the tip. Additionally, the major crack changed in growth and then turned in the direction of the perforation. This means that selecting the optimal perforation pressure was extremely critical to hydro-fracturing. In short, higher perforation pressure does not mean better fracturing. Figure 19: Initiation of cracks under different perforation pressures. (**a**) 3 MPa; (**b**) 10 MPa; (**c**) 15 MPa; (**d**) 20 MPa. Figure 20: Number of failure elements under different pressures.