esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Reinforcement Learning  
RL is an area of ML that aims to allow agents to take actions in an environment in order to maximize their expected benefits. The RL problems are often modeled by an MDP, which was first proposed by [PERSON] [22]. [PERSON] first attempted with MDP modeling in RL [23]. MDP is usually defined as a tuple \(<S,A,P,\gamma,R>\), where \(S\) denotes the state set with \(s\in S\) and \(s_{i}\) denotes the state at step \(i\); \(A\) denotes a set of actions with \(a\in A\) and \(a_{i}\) denotes the action at step \(i\); \(P\) is the probability of transition from one state of a system into another state; \(R\) is the reward function; and \(\gamma\) is the discount factor. The purpose of RL is to allow agents to learn an optimal policy or near-optimal policy that maximizes the expectation of the discount reward sum. The policy can be expressed by  
\[\pi(a,s)=\Pr(a_{t}=a|s_{t}=s) \tag{2}\]  
where \(\pi(a,s)\) represents the probability of choosing action \(a\) in state \(s\) at step \(t\).  
RL can be classified into two types: value-based (e.g., Q Learning [24]) and policy-based (e.g., TRPO [25]). Q Learning constructs a Q table to store Q values and updates it with a weighted sum of the expected values of over all future steps from this state after the execution of the action. It is clear that traditional Q Learning cannot handle tasks with too many states and optional actions. Deep Q Network (DQN) [26] combines DL models with RL to successfully learn policies from high-dimensional inputs directly. These value-based RL methods are relatively well used in many fields, but they have several limitations. For example, they do not have enough ability to handle continuous actions and cannot solve the stochastic policy problem. To solve these problems, policy-based methods with an estimated representation of the policy are introduced. However, selecting the appropriate step size for the policy-based methods is challenging because it is very sensitive to the step size. TRPO is able to find the right step size at each step and reliable performance, but it is relatively complicated. The proximal policy optimization (PPO) proposed in [27] takes some benefits of TRPO and makes the implementation simpler and more general by using first-order optimization, and has better sample complexity. Therefore, in this paper, we choose to enhance the PPO algorithm and apply it on HPO task.  
RL was previously used for neural architecture search (NAS). [PERSON] et al. [28] first used the policy gradient algorithm to train an agent; the agent samples architecture from the search space and receives reward from the environment, which refers to the neural network training procedure. More recently, RL was also used for HPO. [PERSON] et al. [29] used Q Learning to tune hyperparameters on 50 classification datasets. Ref. [30] adopted context-based meta-RL to help the agent to quickly identify task by learning from past experience. However, the problem of sparse reward was ignored in all these studies.
# ANN Sizing Procedure for the Day-Ahead Output Power Forecast of a PV Plant
## 3 Methodology  
### Early Assumptions  
As one of the goals of this work is to study how the number of neurons in the ANN's layers affects the forecast, and to consequently determine the optimal setup (that is, the solution that minimizes the errors), we will modify only the network settings, while keeping the same database. Since this is a stochastic method applied on the available dataset, an average performance must be evaluated, and often the \"best solution\" will not be unique.  
In fact, two networks will provide different outputs even if equally configured to follow the same training process. This is due to the random initialization of weights and biases, which is peculiar for each training run. Therefore, comparing two networks with different settings only by looking at their output cannot generally lead to comprehensive answers. A rigorous approach towards stochastic elements is specifically needed, especially in the comparison between various network layouts and settings, when searching for the best possible configuration. Hence, FFNN has been analyzed by studying the output of the network as a function of the number of neurons and their layout. In each study, the neuron activation function was kept unchanged, and we used the tan-sigmoid (\"_tansig_\" in Matlab Neural Network Toolbox(tm), R2016a, MathWorks(r), Natick, MA, USA [20]), which has the following expression, where \(x\) is the generic input and \(y\) the output of the neuron:  
\[y=\frac{2}{1+e^{-2x}}-1. \tag{1}\]  
Here, two \"similar networks\" are defined as two networks with the same topology (number of neurons and their layout), but with different weights and biases. Therefore, statistic tools are used to infer more tangible conclusions, giving more strength to the final settings adopted by the method.
# A Distributed Storage and Access Approach for Massive Remote Sensing Data in MongoDB
## 2 Background
### GridFS Mechanism  
MongoDB supports the storage of binary data through a lightweight file storage specification named GridFS. GridFS is a distributed file storage mechanism used to store large binary files, which splits the large file into many small file chunks, and each file chunk is stored as a document [24]. Under this mechanism, the large file is stored in two collections whose default names are fs.chunks and fs.files, with binary data stored in the fs.chunks collection and the descriptive information stored in the fs.files collection, which achieves the distributed storage of data.  
When storing a binary file, if its size is greater than the pre-set chunksize value, the file will be divided into several chunks. Each file corresponds to a document in the fs.files collection, which corresponds to one or more documents in fs.chunks.
# Adaptive Visual Saliency Feature Enhancement of CBCT for Image-Guided Radiotherapy
## 3 Experiments and Analysis
### Quantitative Analysis  
Tables 1-4 show the evaluation metrics of different methods on Pair 1 to Pair 4. The best results in these tables are highlighted in bold and the second-best results are highlighted in bold and italic. The proposed enhancement algorithm gives the best values of AG and EI on three image pairs and four of the second-best values of SF on all images. The average evaluation metrics on all the testing images of different methods are given in Figure 11. The proposed algorithm gives the best scores for AG and EI and the second-best scores for SF and UIQM. This means the enhancement results of the proposed algorithm contain more detailed texture and gradient information and are more consistent with the human visual system. The evaluation metrics in Tables 1-4 also prove the superiority of the proposed method, which is consistent with the conclusion obtained by the visual comparison.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **AG** & **SF** & **EI** & **UIQM** \\ \hline MSR & 4.4690 & 14.3406 & 41.3963 & 1.7695 \\ MSRCR & 4.3526 & 14.3373 & 40.4904 & 1.7174 \\ DCP & 3.0656 & 9.4439 & 27.2513 & 1.8540 \\ CBF & 3.7935 & 11.6488 & 30.5138 & 2.1783 \\ RRM & 5.4507 & 18.6302 & 48.4082 & 2.0375 \\ SMIPC & 4.7708 & 15.2181 & 43.5509 & 1.7413 \\ Proposed & 5.8681 & 16.1921 & 53.3944 & 1.9741 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Quantitative metrics of different methods on pair 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **AG** & **SF** & **EI** & **UIQM** \\ \hline MSR & 4.5997 & 14.8567 & 43.1076 & 1.7329 \\ MSRCR & 4.4881 & 14.8235 & 42.2025 & 1.6932 \\ DCF & 3.0383 & 9.3338 & 27.2469 & 1.8556 \\ CBF & 4.0720 & 12.3792 & 32.8049 & 2.2907 \\ RRM & 5.5267 & 18.9434 & 49.7821 & 1.9529 \\ SMIPC & 4.8789 & 15.6855 & 45.0936 & 1.7019 \\ Proposed & 5.9509 & 16.1871 & 54.7970 & 2.0232 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Quantitative metrics of different methods on pair 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **AG** & **SF** & **EI** & **UIQM** \\ \hline MSR & 3.6001 & 12.5238 & 33.6119 & 1.4794 \\ MSRCR & 3.4437 & 12.2078 & 32.3220 & 1.4792 \\ DCF & 2.3504 & 8.0239 & 20.6858 & 1.6847 \\ CBF & 3.5567 & 11.7906 & 28.6938 & 2.0258 \\ RRM & 5.0072 & 18.7826 & 44.6839 & 1.6157 \\ SMIPC & 4.0578 & 13.9605 & 36.8604 & 1.4853 \\ Proposed & 5.2918 & 15.1418 & 48.3878 & 1.8852 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Quantitative metrics of different methods on pair 3.
# Thermal Failure Propagation in Lithium-Ion Battery Modules with Various Shapes
## 3 Results and Discussion
### Rectangle Module  
Figure 7 shows the typical surface temperature curves of batteries in rectangular modules during tests. Similarly, the domino effect in the failure propagation of rectangular modules could be observed and it consisted of three phases, further illustrated in Figure 8. For the thermal failure propagation in rectangular modules with discharging treatment, the two batteries near the heater failed firstly at about 368 s which formed the first phase of failure, then it propagated to the battery in the middle at around 440 s. Finally, in the third phase, the two batteries farthest away from the heater took failure at about 568 s. In rectangular modules, the propagation distance of failure was two times the battery diameter, 36 mm. Thence, the propagation speed could be calculated as 0.063 mm/s. In addition, it could be seen that the three phases of failure for the rectangular module without discharging treatment appeared at 389 s, 480 s, and 583 s, respectively, in Figure 7b. All of them lagged behind the failure in modules with discharging treatment, and the propagation speed was 0.062 mm/s. Similar to the results in Section 3.1, the discharging treatment aggravated the thermal failure propagation in rectangular modules.  
Figure 7: The typical surface temperature curves of batteries in rectangular modules: (**a**) with discharging treatment; (**b**) without discharging treatment.  
Besides that, the typical curves of mass loss for rectangular modules in tests are presented in Figure 9, where there existed three obvious mass loss processes in the curve, which corresponded to the three phases of failure. The mass of modules with discharging treatment declined at first due to the earlier thermal failure. It had a larger mass loss of 51.6 g compared to that of the module without discharging treatment, which was 50.2 g. The detailed information is further listed in Table 3.
## 5 Results and Discussion  
This section describes the experimental findings and configuration of the proposed method over the Sectrust-RPL [1], SEHR [6], HBEER [6], and SEAMHR [9] approaches. The various network techniques are validated, tested, and verified using the MATLAB simulator tool to assess the results of the experiments. In terms of throughput, packet delay, defective routes, and energy consumption, the results are significantly better than the previous research's results. In addition, the performance of the linked methodologies and the proposed protocol is provided by taking into account variables such as end-to-end delay, fault pathways, throughput, packet drop ratio, and energy consumption. Table 2 displays the WSN parameter settings used in the simulation.  
Table 3 represents the comparative analysis of the proposed approach with the state of art approaches, and Figure 4 shows the graphical representation of the same.
## 2 Signal Processing and Height Inversion
### Height Inversion  
#### 2.2.1 Delay Estimation and Error Correction  
The delay of the reflected signal through the SP can be estimated from the power waveform. The delay estimation can be divided into two types: fixed-point tracking and model fitting. Fixed-point tracking involves tracking a given point of the waveform, such as DER, MAX, and HALF. The DER delay estimation is applied in this study, which can be expressed as [15]:  
\[\tau_{\rm DER}^{\rm obs,corr}=\tau_{\rm DER}^{\rm obs}+(\tau_{\rm sp}^{\rm m}- \tau_{\rm DER}^{\rm m}) \tag{2}\]  
where \(\tau_{\rm DER}^{\rm obs}\) is the delay corresponding to the maximum of the measured waveform's first derivative (Figure 2A), and \(\tau_{\rm sp}^{\rm m}\) and \(\tau_{\rm DER}^{\rm m}\) correspond to the nominal SP delay and the maximum of the simulated waveform's first derivative. The nominal SP \(\tau_{\rm sp}^{\rm m}\) is the minimum of the reflection path calculated from the WGS-84 reference coordinate when the positions of the transmitter and the receiver are known. The simulated waveform (Figure 2B) is generated according to the Zavorotny and Voronovich (Z-V) model, which takes into account the influence of geometry, instrument configuration, and ocean state [26]. The difference between \(\tau_{\rm sp}^{\rm m}\) and \(\tau_{\rm DER}^{\rm m}\) obtained from the simulated waveform is used as the deviation correction for DER tracking.  
The estimated bistatic delay information of the SP also needs error correction, including tropospheric error \(\rho_{\rm trop}\), ionospheric error \(\rho_{\rm iono}\), and antenna baseline error \(\rho_{\rm bl}\)[12; 14]. As the ionosphere is located above 60 km, both direct and reflected signals received by the airborne platform go through the same data path for downlink transmission, so the  
Figure 2: Examples of measured and simulated power waveforms. (**A**) Normalized measured power waveform processed from the IF data. The \(\tau_{\rm DER}^{\rm obs}\) is the maximum point of the first derivative. (**B**) Normalized simulated power waveform generated based on the Z-V model. The \(\tau_{\rm DER}^{\rm m}\) is the maximum point of the first derivative. The \(\tau_{\rm sp}^{\rm m}\) is the nominal SP calculated based on the WGS-84 reference ellipsoid.  
delay deviation can be ignored. The tropospheric error correction is derived from the Saastamoinen model [21]:  
\[\rho_{\text{trop}}=\frac{4.6}{\sin e}(1-e^{-H_{\text{R}}/H_{\text{trop}}}) \tag{3}\]  
where \(e\) is the satellite elevation angle, \(H_{\text{R}}\) is the receiver height, and \(H_{\text{trop}}\) is the average tropospheric height. The antenna baseline offset is calculated from the path difference between the zenith pointing antenna and the nadir pointing antenna relative to the SP under the known airborne position and attitude information. The corrected SP delay can be converted to the actual height from the receiver to the sea surface [15].  
\[H_{e}^{\text{obs}}=\frac{\tau_{\text{DER}}^{\text{obs,corr}}-(p_{\text{tono}} +p_{\text{trop}}+p_{\text{bl}})}{2\times\sin(e)} \tag{4}\]
## 3 Case Study
### Distribution Parameters and Condition Data Estimations
Figure 9 presents the absolute error percentages between the computed and predicted HIs that have been obtained based on SDM for 25 years. The overall average absolute error percentage in the training region is 0.65%, while, for the validation region, is 2.17%. The HI predicted using SDM for the transformers in validation region yields 97.83% accuracy. The application of SDM to predict HI of transformer population is a propitious approach for asset management in utilities. It is shown that, with limited historical condition parameters data, SDM is able to predict the transformers' HI. These findings can be further validated if direct HI data from utilities can be acquired in the future. The application can be extended to another fleet or unit, regardless of ratings/sizes, because it is a data driven model. In addition, it is interesting to examine the HI model based on SDM to represent the condition of transformer due to oil change or regeneration that can be carried out as part of the future study.
###### Abstract  
Fouling has been widely recognized as the Achilles' heel of membrane processes and the growing perception about the relevance of this critical issue has driven the development of advanced antifouling strategies. Herein, novel fouling-resistant ultrafiltration (UF) membranes for Cadmium (Cd) remediation were developed via a blending method by combining the flexibility of cellulose acetate (CA) with the complex properties of poly (acrylic acid) (PAA). A systematic characterization, based on differential scanning calorimetry (DSC) and Fourier Transform Infrared Spectroscopy (FTIR), confirmed the homogeneity of the blend favored by hydrogen interconnections between CA and PAA polymeric chains. The concentration of PAA with respect to CA played a key role in tuning the morphology and the hydrophilic character of the novel UF membranes prepared via non-solvent-induced phase separation (NIPS). UF experiments revealed the tremendous advantages of the blend since CA/PAA membranes showed superior performance with respect to the neat CA membrane in terms of (i) water permeability; (ii) Cd rejection; and (iii) antifouling resistance to humic acid (HA). Concisely, the increasing of the concentration of PAA in the casting solution was found to be beneficial to improve the flux recovery ratio (FRR) coupled with the decline of the total fouling ratio (Rt). Overall, PAA is an effective additive to prepare CA membranes with enhanced antifouling properties exploitable for the remediation of water bodies contaminated by heavy metals via UF process.  
ultrafiltration membrane; blend; cellulose acetate; humic acid; antifouling membrane; heavy metal removal +
Footnote †: journal: Applied Sciences
# Integrating the IoT and Blockchain Technology for the Next Generation of Mining Inspection Systems
## 4 Use Case Validation
### Blockchain Module  
The blockchain module groups the smart contract needed to support our proposed system. Based on the \"smart-twin\" architecture proposed in [26], we developed two types of smart contracts: _Twin_ and _Apps_. First, we used the _Twin_ contract to represent the sensing device and the assets, keeping identification data (i.e., model, manufacturer, owner, and certifications). Then, we used the _App_ contract to implement elements of the business logic, such as the result of the inspection and certifications. It is important to recall that these processes directly and automatically interact with the assets and the sensing tool (_Twin_ contracts).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Subsystems** & **Min/Max** & **Average** & **Deviation of** \\
**Machine Category** & **Per** & **Checkpoints** & **Checkpoints** & **Checkpoints** \\  & **Machine** & **Per Subsystem** & **Per Subsystem** & **Per Subsystem** \\ \hline Articulated mining truck & 38 & 1 / 9 & 4.13 & 2.21 \\ \hline Backhoe loader & 34 & 1 / 13 & 4.71 & 3.03 \\ \hline Bolting rigs & 37 & 1 / 9 & 4.11 & 2.46 \\ \hline Dozer & 30 & 1 / 18 & 4.47 & 3.77 \\ \hline Hydraulic power shovel excavator—back hoe & 31 & 1 / 12 & 4.16 & 3.00 \\ \hline Hydraulic power shovel excavator—front hoe & 34 & 1 / 15 & 4.47 & 2.98 \\ \hline LHD & 34 & 1 / 15 & 4.12 & 2.89 \\ \hline Rigid mining truck & 38 & 1 / 8 & 4.11 & 2.12 \\ \hline Tracked drilling rig & 35 & 1 / 12 & 4.11 & 2.67 \\ \hline Tracked loader & 30 & 1 / 20 & 4.47 & 3.88 \\ \hline Wheel loader & 34 & 1 / 12 & 4.50 & 2.69 \\ \hline Wheeled drilling rig & 37 & 1 / 9 & 4.11 & 2.46 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Number of measures/observations that need to be collected per subsystems (assumption: one measure/observation per checkpoint).  
Each asset stores its measurements, which are unequivocally linked to a particular tool. The assets also store the inspection results linked to tools and inspectors. Certifications are represented with tokens, managed by the certifiers, and assigned to tools and assets. The algorithms to determine the outcome of inspections can be defined by the OEM or by local authorities. To implement this logic, we chose the Ethereum blockchain [34] because it is considered as the reference public blockchain implementation for smart contracts and can also function as a permissioned network. Nonetheless, migrating our implementation to a different blockchain platform with scripting capabilities should not be an issue. Finally, in our implementation, we adopted industry-approved libraries (i.e., OpenZepellin ([[https://github.com/OpenZeppelin/openzeppelin-contracts](https://github.com/OpenZeppelin/openzeppelin-contracts)]([https://github.com/OpenZeppelin/openzeppelin-contracts](https://github.com/OpenZeppelin/openzeppelin-contracts)), accessed on 15 December 2021)) to reduce the security concerns deriving from the vulnerabilities that a smart contract may introduce in our system [35].
## 10 Concluding Remarks  
IoT-based M2M traffic-oriented applications do not fit into a narrowly defined set of requirements, but entail adaptable and wide ranges of coverage, capacity, bandwidth, cost, and other functional expectations. LPWAN solutions have been primarily targeted at a subset of these applications with an emphasis on wide coverage, large number of low cost devices, extremely efficient energy operations, and a comparatively narrow range of bandwidth. This paper has provided a framework for developing a set of design considerations for developing and accessing the suitability of an LPWAN technology to its targeted application.  
The powerful approach of using traceability and translations from application characteristics to requirements and then identifying a set of associated design considerations is a very effective way to capture architectural and design constructs for technology solutions. LPWAN is not a uniquely defined solution for a fixed set of requirements but has a range of characteristics that require tradeoffs and optimizations. For initiating new LPWAN solutions for existing and evolving smart applications, the LPWAN study in this paper provides an effective framework and context for carrying out tradeoffs for implementations. The approach also allows existing technologies to identify the gaps and assess the suitability of their implementation for their targeted applications. It enables them to develop modifications and updates consistent with the application characteristics.  
Major technologies like LoRaWAN, Sigfox, and NB-IoT, among others, have evolved to-date. As the interoperability and range of targeted applications has expanded, emphasis on more challenging attributes, especially bandwidth, priority structures, mobility, and adherence to standards, is gaining importance. This is resulting in a much wider set of solutions including updates to existing solutions and development of new technologies like LTE-M. Upcoming 5G cellular technology is expected to be a powerful and dominant technology as its key strength is in the area of effectively handling M2M traffic with significant improvements in performance, energy efficiency, and scalability. The future is likely to entail hybrid network solutions with co-existence of personal area networks (PAN), local area network (LAN), cellular wireless wide area networks (WWAN), and LPWANs.  
All the authors have contributed equally towards the conceptualization, methodology, analysis, investigation, and resources. The original draft and changes were also performed by all the authors. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Acoustic Signal Parameters
#### 2.3.2 Noise to Harmonic Ratio--NHR  
The NHR parameter quantifies the relationship between the aperiodic component (noise) and the periodic component (harmonic part).  
According to the representation of the normalized Autocorrelation represented in Figure 2, the signal \(S=1\), and the Harmonic component is the Autocorrelation value \(H=A\). \(NHR\) is the relationship between the noise (given by the difference between the total signal--\(S\), and the Harmonic component--\(H\)) and the Harmonic component. Therefore, Equations (4) and (5) provide the NHR. This relationship is not the one used by [PERSON] (2018), where \(NHR=1-A\). Equation (5) is close to \(1-A\) for \(A\approx 1\), which happens for most signals corresponding to the normophonic sustained vowels.  
\[NHR=\frac{N}{H}=\frac{S-H}{H} \tag{4}\]  
For \(S=1\), and \(A=H\),  
\[NHR=\frac{1-A}{A} \tag{5}\]