esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Metal Interaction Experiments  
Stock solutions of cupric ions (cupric sulfate pentahydrate, CuSO\({}_{4}\cdot\)5H\({}_{2}\)O), ferric ions (ferric chloride hexahydrate, FeCl\({}_{3}\cdot\)6H\({}_{2}\)O) and ferrous ions (ferrous sulfate heptabydrate, FeSO\({}_{4}\cdot\)7H\({}_{2}\)O) were prepared in water (Milli-Q RG, Merck Millipore, MA, USA), while that of cuprous ions (cuprous chloride, CuCl) was prepared in an aqueous solution of 0.1 M HCl and 1 M NaCl. The corresponding fresh working solutions (0.25 mM) were prepared by dilution either in DMSO (BCS method) or distilled water (hematoxylin and ferrozine method). Hydroxylamine hydrochloride, ferrozine, and bathocuprofinedisulfonic acid disodium salt (BCS) were dissolved in distilled water. Hematoxylin was dissolved in DMSO and its working solution (0.25 mM) was usable for no longer than 90 min. All tested xanthones were dissolved in DMSO. Experiments were performed in the following buffers: acetate (pH 4.5 and 5.5; 15 mM sodium acetate salt, and 27.3 and 2.7 mM acetic acid, respectively) and HEPES (pH 6.8 and 7.5; 15 mM sodium HEPES, and 71.7 and 14.3 mM HEPES, respectively). DMSO was purchased from Avantor Performance Material, (VWR International s.r.o., Stribrna Skalice, Czech Republic), while all other chemicals were purchased from Sigma-Aldrich (St. Louis, MO, USA).
## 3 Results and Discussion
### Study of the Hydrophobic Recovery Following the Plasma Treatment of the Medical Grade PVC
The parameters of fitting are supplied in Table 4.  
The kinetics of hydrophobic recovery is satisfactorily described by the exponential fitting, suggested in [28]:  
\[\theta(t)\ =\ \widetilde{\theta}\left(1-e^{-\frac{t}{\tau}}\right)+\theta_{0}\ =\ \theta_{sat}-\widetilde{\theta}e^{-\frac{t}{\tau}} \tag{5}\]  
where \(\theta_{0}\) is the initial apparent contact angle, taken immediately after the plasma treatment of PVC catheters, \(\tau\) is the characteristic time of restoring the contact angle, \(\widetilde{\theta}\) is the fitting parameter, and \(\theta_{sat}\ =\ \widetilde{\theta}+\theta_{0}\) is the saturation apparent contact angle. The numerical values of the parameters of the exponential fitting for the investigated plasma discharges are supplied in Tables 2-4. A number of common features are recognized for the hydrophobic recovery of PVC tubing exposed to various plasma discharges: (i) The hydrophobic recovery following the plasma treatment is well described by the exponential fitting for all kinds of the studied discharges and (ii) the saturation contact angle \(\theta_{sat}\) is slightly lower than the initial apparent contact angle, in other words, the hydrophobic recovery following the plasma treatment of PVC is never complete. On the other hand, the characteristic time of the hydrophobic recovery is largest for the radiofrequency plasma discharge, namely \(\tau\cong\) 3.0-6.0 days; it is intermediate for the Corona discharge, i.e., \(\tau\cong\) 1.0-2.5 days; and it is small for the DBD discharge, namely: \(\tau\cong\) 0.5-1.5 days. Thus, the hydrophobic recovery following the cold plasma treatment is the slowest for the radiofrequency plasma discharge and it is rapid for the DBD plasma treatment. At this stage of our investigation we are far from the microscopic interpretation of these experimental findings, which are of a primary importance for the applications of the plasma processing of PVC, however some conclusions may be made from the energy-dispersive X-ray spectroscopy (abbreviated further EDS spectroscopy) of the plasma-treated PVC catheters, discussed in the following section.
# Blockchain-Assisted QoS-Aware Routing for Software-Defined Wide Area Network
## 4 Blockchain-Assisted Trading
Following that, these trading services locate a suitable seller(s) for each interested cloud user with a trading price based on resource requirements. The identity and payment services of the smart contract handle secured financial transactions between participants. The cloud user submits the buying requests to the smart contract with a specified bidding price. The smart contract then evaluates these requests and executes bidding. The algorithm then finds the ISPs that can ensure the QoS requirements between the cloud user and the cloud provider. If the chain of ISPs provides the required QoS-aware services, then the cloud user also pays the ISPs through the smart contract for the support service. Figure 3: Interactions with smart contract. ```
1 ForEach CloudServiceProvider[i] in CloudProviderList:
2 IF CloudServiceProvider[i].Capability > CloudUser[m].QoSRequirement
3 SellerList = UpdateSellerList(ExistingSellerList,CloudServiceProvider[i])
4 //SellerList satisfies CloudUser[m] requirements
6 Trade[m,n].Price = FixingCost(CloudUser[m].BiddingPrice, FindMinimum(SellerList[n].AskingPrice))
8
9 IF CloudUser[m].Balance > Trade[m,n].Price:
10 BalanceTransfer(CloudUser[m], Seller[n], Trade[m,n].Price)
11
12 ISPList = FindISPs(CloudUser[m], Seller[n])
13
14 ForEach ISP[k] in ISPList:
15 IF ISP[k].ProvidedQoSSupport(CloudUser[m], Seller[n]) == \"Satisfactory\":
16 BalanceTransfer(CloudUser[m], ISP[k], QoSCost[m,n])
17 ELSE
18 ReportAndRecordIncidence(ISP[k])
```  
**Algorithm 3** Smartcontract-based trade algorithm.
# Estimation of the Ultimate Strength of FRP Strips-to-Masonry Substrates Bond
## 5 Summary  
This paper presents the ANN, ANN-KFCV, MARS, and M5 MT techniques to estimate the ultimate strength of FRP strips applied on masonry substrates. Firstly, knowledge and possible solutions were reviewed. In the next stage, a set of experimental data (134 cases) was gathered and normalized. Finally, ANN, KFCV, MARS, and M5 MT techniques were applied to offer new models predicting the ultimate bond strength of FRP strips to masonry joints. The \(R^{2}\) values in the ANN method amount to 0.99293, 0.99896, 0.99572, and 0.99382 for training, validation, testing, and all data, respectively. Furthermore, this value is 0.9935, 0.9919, and 0.9901 for the KFCV, MARS, and M5 MT methods. The results prove that the established techniques have an excellent coincident with the experimental values. This finding confirms the accuracy of the proposed formulas in forecasting the ultimate strength of FRP strips applied to masonry elements. In this regard, we tried to decrease the computational cost by introducing new parameters to reduce the number of input parameters. It should be noted that the proposed formulas are valid for externally bonded FRP-to-masonry members. The results show that the presented formulas are more precise than the existing formulas.  
Conceptualization, R.K. and H.E.K.; methodology, R.K., H.E.K., A.J.-G. and R.J.; software, R.K. and H.E.K.; validation, R.K., H.E.K., A.J.-G. and R.J.; formal analysis, R.K. and H.E.K.; investigation, R.K. and H.E.K.; writing--original draft preparation, R.K. and H.E.K.; writing--review and editing, A.J.-G. and R.J. All authors have read and agreed to the published version of the manuscript.  
This research received no funding.  
Not applicable.  
The datasets generated and analyzed during the current study are available on request.  
The authors declare no conflict of interest.
## 3 Rolling Bearing Remaining Useful Life (RUL) Prediction Method
### Overall Method Process  
The bearing RUL prediction process proposed in this paper is shown in the figure, and its specific process is shown in Figure 5.  
1. Data Preprocessing: The IU-Net model is utilized to process and denoise the original time-domain and frequency-domain signals. Multi-scale signal features are extracted using one-dimensional convolution and pooling, followed by deconvolution and concatenation techniques to produce the denoised signals.
2. Health Indicator Construction: The processed signals are utilized to compute both time-domain and frequency-domain feature values. A multi-dimensional hybrid gated attention mechanism is implemented to generate multi-dimensional weight information, followed by feature fusion to construct the health indicator.
3. RUL Prediction: This section describes the construction of an IU-Net-MHGAM-BiGRU model for predicting the remaining useful life of bearings. The health indicators, which were developed in the previous step, serve as inputs to this model to facilitate accurate RUL predictions.  
Figure 4: Input and output of the processed signal. (**a**) Time-domain signal; (**b**) frequency-domain signal.
## 2 CEMs--State of the Art
### Transition Metal Compounds  
The successful preparation of coordination energetic materials relies on the use of transition metal compounds. Despite there existing reports of CEMs containing heavy metals, the majority of reports focus on transition metals belonging to the fourth period of the periodic table of elements [14; 15]. Those elements form relatively small ions with a high charge and typically exhibit little to no toxicity and environmental impact. In some cases, the desired compounds are found in the environment as minerals. Among the CEMs reported in the literature, the most frequent mentions are made of compounds in which the role of the central atom is fulfilled by cations such as Cr\({}^{3+}\), Mn\({}^{2+}\), Fe\({}^{3+}\), Co\({}^{2+}\), Ni\({}^{2+}\), Cu\({}^{2+}\) and Zn\({}^{2+}\)[16; 17; 18]. Period 4 metal compounds are characterized by high spatial packing. These metals are characterized by an unfilled d-substrate, so they can exist on many different oxidation levels. The coordination number of the abovementioned cations is 6, except for the copper cation, whose coordination number is 4. Site coordination in the case of multidentate ligands provides the possibility of coordinating larger numbers of ligand molecules relative to metal salt molecules; the structures of these compounds are mostly octahedral and, except for mercury and copper, are characterized by a flat-coordinated geometry [16]. Compounds based on some of these elements, such as copper and cobalt, are not necessarily completely \"green\" as some forms of those elements show toxicity, but despite this, they are far more desirable than compounds based on lead.
# Ni/Zn Layered Double Hydroxide (LDH) Micro/Nanosystems and Their Azorubine Adsorption Performance
## 3 Adsorption Studies
### Isotherm Models  
The adsorption isotherm was used to represent the adsorption capacity at different solution concentrations that helps to have idea on the adsorbent and adsorbate interaction. The experimental results were fitted using [PERSON] [33] and [PERSON] [34] models. The Langmuir isotherm (Equation (3)) assumes a monolayer adsorption of the adsorbate on the adsorbent surface using a given relation:  
\[\frac{1}{q_{e}}=\frac{1}{q_{m}}+\frac{1}{K_{L}*q_{m}}+\frac{1}{C_{e}} \tag{3}\]  
where \(q_{m}\) is the maximum adsorption capacity (mg g\({}^{-1}\)) and \(K_{L}\) is the Langmuir constant (L mg\({}^{-1}\)).  
Nevertheless, the Freundlich adsorption isotherm (Equation(4)) as an empirical equation often used to describe the chemisorption on heterogeneous surface is given by the following relation:  
\[Ln\;q_{e}=Ln\;K_{F}+\frac{1}{n}\;Ln\;C_{e} \tag{4}\]  
where \(K_{F}\) is the Freundlich constant and 1/nis the heterogeneity factor. In this Freundlich equation, 1/n values between 0 and 1 indicate that the adsorption is favorable.
## 2 Brief Introductions to SDAE
### Denoising Autoencoder  
Given a training input pattern \(X\), a corrupted version \(\widetilde{X}\) is first generated. The corrupted \(\widetilde{X}\) is generated in a way of random mapping: \(\widetilde{X}\)-\(p\left(\widetilde{X}\middle|X\right)\), where refers to a conditional distribution. For the input matrix \(X\), a determinate number \(d\) of points are selected randomly, the value of which is forced to 0, whereas the others remained unchanged.  
Thereafter, the corrupted input \(\widetilde{X}\) is projected to the succeeding hidden layer:  
\[Y=f_{\Theta}(\widetilde{X})=\text{sigmoid}(W*\widetilde{X}+b), \tag{1}\]  
\[\theta=\{W,b\}, \tag{2}\]  
where \(\theta\) is the weight and bis the bias parameters from the input to the hidden layers. Thereafter, the input is reconstructed as:  
\[Z=g_{\theta^{\prime}}(y)=\text{sigmoid}(W^{\prime}*Y+b^{\prime}), \tag{3}\]  
\[\theta^{\prime}=\{W^{\prime},b^{\prime}\}, \tag{4}\]  
where \(\theta^{\prime}\) is the weight and \(b\) is the bias parameters from the hidden layer to the output layer. To minimize the number of parameters, tied weights are used as follows:  
\[W^{\prime}=W^{T}. \tag{5}\]  
The joint function is defined as follows:  
\[p^{0}(X,\widetilde{X},Y)=p^{0}(X)p_{d}(\widetilde{X}\Big{|}X)\sigma_{f_{ \theta}(\widetilde{X})}(Y), \tag{6}\]  
The computation \(\sigma_{f_{\theta}(\widetilde{X})}(Y)\) indicates placing mass 0 if \(Y\
eq f_{\theta}(\widetilde{X})\), and the parameters of \(p^{0}(X,\widetilde{X},Y)\) is determined by \(\theta\). Thereafter, the loss function to be minimized can be defined as follows:  
\[\underset{\theta,\theta^{\prime}}{\text{argmin}}E_{p^{0}(X,\widetilde{X})}[ L_{H}(X,Z)]. \tag{7}\]  
The cross-entropy loss is computed as follows:  
\[L_{H}(X,Z)=X\log Z+(1-X)\log(1-Z). \tag{8}\]  
The gradient of the loss is computed to update the parameters as follows:  
\[\left[\theta,\theta^{\prime}\right]^{k+1}=\left[\theta,\theta^{\prime}\right] ^{k}+\alpha\frac{\partial L_{H}(X,Z)}{\partial\left[\theta,\theta^{\prime} \right]^{k}}, \tag{9}\]  
where \(\alpha\) denotes the learning rate of the training process. The process of encoding and decoding is illustrated in Figure 1.  
After pre-training, the autoencoder is robust to the particular corrupted input and other signal deficiencies; thus, the denoising autoencoder is able to \"fill in\" these \"blanks\" of input. When the dimension of \(Y\) is constrained shorter than that of input \(X\), the intermediate representation may be served as various coordinate system transformations for points. Denoising training can only be possible because of dependencies between dimensions in high dimensional distributions, thereby  
Figure 1: Encoding and decoding process of denoising autoencoder.  
possibly determining these dependencies. This process also explains why this approach is appropriate in fault diagnosis, which has high dimension input signals.
###### Abstract  
Redirection and protocol techniques are key components of the infrastructure for Content Delivery Networks (CDNs) that aid in the delivery of Multimedia Internet services to end-consumers. Redirection methods are used to route the user's request to the nearest edge server, minimizing distance, improving delivery times, and lowering latency. Protocol mechanisms, such as HTTP Live Streaming (HLS), Dynamic Adaptive Streaming over HTTP (DASH), and Real-Time Messaging Protocol (RTMP), are used to deliver adaptive video streaming. These protocols are designed to transfer the adaptive streaming and provide high-quality video playback. They also allow the system to adjust the video quality based on the network conditions of the Quality of Service (QoS). Inadequate transmission protocols and poorly designed redirection algorithms are two major challenges that might degrade Cloud-CDN performance. These challenges lead to excessive latency, poor quality of service, and significant packet loss that have potential influences on the user experience. In this paper, firstly, three protocols are proposed by preparing a case study on selecting the optimal protocol for replicating adaptive video streaming content. Secondly, a redirection algorithm based on the Modified Cuckoo Search Algorithm (MCSA) is proposed to provide an accurate redirecting process of selected edge servers to end-users. Test results indicate that, when hybrid FASP/HTTP protocols were chosen (from original server to replicate server and to end-users), the delivery of adaptive video streaming segments was fast with lower latency. The average estimated time needed for replicating video content based on FASP is 25% better than that needed for File catalyst and Sipianit protocols. Therefore, the Cuckoo search method presents more efficient results for selecting the optimal edge server for 100 servers, which is 0.296 s, compared to the conventional algorithm, which was 13 s.  
Cloud-CDN; redirection algorithm; protocol; edge servers; adaptive video streaming; MCSA 202333344  
Furthermore, a significant amount of downstream Internet traffic is being produced by the highly successful deployment of Over-The-Top (OTT) services and CDNs for multimedia streaming applications. In addition, video usage increased by 24% in 2022, accounting for 65% of all internet traffic. For the first time, Netflix surpassed YouTube as the individual application generating the most traffic, with TikTok, Disney+, and Hulu [4]. However, according to its analysis of current usage data from over 177 service providers worldwide, Facebook, Amazon, Google, Apple, Netflix, and Microsoft continue to generate nearly half of all internet traffic, with Google and Netflix accounting for the highest volumes [5].
# Minimum Interference Channel Assignment Algorithm for Multicast in a Wireless Mesh Network
## 3 Minimum Interference Channel Assignment  
The ultimate goal of the minimum interference channel assignment (MICA) algorithm is to assign communication channels to wireless devices properly for the purpose of minimizing network interference among nodes. Therefore, MICA achieves maximum throughput and minimum end-to-end packet delay for multicast in wireless mesh networks. In this section, we describe how the MICA algorithm works.  
The MICA algorithm accepts a multicast tree as an input and produces a channel assignment for communicating interfaces of each wireless node in the tree. There are three steps in MICA. The first step is to calculate channel separation of all pairs of nodes in the network in order to avoid network interference. The second phase is to find whether there is a channel separation of zero (\(CS_{zero}\)) among wireless nodes. This is why we could assign the same channel to those pairs of nodes as many times as possible. Lastly, MICA assigns an optimal channel numbers to all of the nodes in the network.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Channel Separation** & **2 Mbits/s** & **5.5 Mbits/s** & **11 Mbits/s** \\ \hline
0 & 2.5 & 2.2 & 2.0 \\
1 & 1.6 & 1.5 & 1.2 \\
2 & 1.2 & 1.0 & 0.7 \\
3 & 0.9 & 0.8 & 0.5 \\
4 & 0.5 & 0.3 & 0.2 \\ \(\geq\)5 & 0.0 & 0.0 & 0.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Interference factor in an IEEE 802.11b network.
# Ten Years of Active Learning Techniques and Object Detection: A Systematic Review
## 4 Active Learning and Object Detection: A Literature-Driven Multi-Perspective Analysis
### (Rq5) Other Techniques
#### 4.5.5 Other Proposals  
A method for AL applied to OD in video, using temporal coherence, was presented in [9], which proposes an acquisition function called Temporal Coherence that resorts to an object tracker between consecutive frames.  
For remote sensing applications, [69] propose a new weighted classification regression (WCR) uncertainty sampling method, which consists of selecting images according to its size, using regression and adapting the classification uncertainty according to its informativeness. Also, the proposed method deals with class imbalance by considering the objects count by class, thus guaranteeing a better performance on classes with fewer instances. The least confident metric (\(U_{c}\)) for classification is calculated using the average number of objects for each class in one image and the class ratio. For regression, the Uncertainty (\(U_{r}\)) is calculated, taking into consideration the size of the bounding box of the detected object. Finally, the image uncertainty is calculated as \(U_{s}=\sum(U_{c}\cdot U_{r})\).  
To deal with the problem of data bias on uncertainty-based AL methods, the Weighting filter (W-filter), custom-made for OD, is proposed in [60]. This method (see Figure 15) filters similar images from the training dataset in the first epoch guaranteeing better performance and stability in the learning process. Also, some classical uncertainty methods are adapted for the OD task. W-filter focuses on high-frequency analysis (i.e., edges of objects in images) and the removal of similar ones. After the first epoch, the method reduces the bias in the training dataset by removing images with high similarity in the frequency domain.  
In [55], the authors proposed an AL uncertainty-based framework, which includes a method to measure the sampling scale of the different defects in a defect detection context. Another way to select unlabelled images is according to the detector's confidence [56; 64]. Instances with high-confidence prediction can be automatically pseudo-labelled via a self-learning process. If instances have low confidence, the images are manually annotated after being selected by the AL strategy. The authors consider an image annotation between different oracles with ambiguities to have a noisy sample and proposed a suitable method to deal with it via the switchable criteria. For instance, in [64], the authors proposed  
Figure 15: Procedure comparison between W-filter and other methods in the first epoch, retrieved from [60].  
a Self-Supervised Sample Mining approach for OD, while in [56], the active sampling mining (ASM) framework was chosen instead. Also, a Contextual Diversity approach was proposed in [65], presenting two AL frameworks--the first based on a core-set strategy and the second based on reinforcement learning.
## Appendix A General Considerations in RFE Design  
### Photon Density in a Binary Optical OOK Signal  
The energy of one photon is calculated by \(E_{Ph}=hc/\lambda\), and the average number of photons per bit (Ppb) \(\langle N\rangle\) in a binary data stream (with rate \(r\)\([bit/s]\) and average power \(\langle P_{Rx}\rangle\)) is thus \(\langle N\rangle=\langle P_{Rx}\rangle\cdot\lambda/\left(hcr\right)\).  
Assuming an IM/DD of OOK with a 50% mark-zero ratio, we see twice the power within an on-bit as compared to the whole data stream on average, i.e., \(\widehat{P}_{1}=2\cdot\langle P_{Rx}\rangle\). Sensitivity in photons per bit, as well as the signal power, is always stated for the _average_ power value in this document. The following Figure A1 signifies the relation concerning the mean photons per bit and the average received power for three different data rates.