esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background and Related Work
### WSN Deployment
#### 2.4.2 Random Deployment  
This deployment distributes the sensor nodes randomly within the area of interest using a probability density function (PDF) [36]. This approach is often used when a site is too large or dangerous to place manually or when the exact need for surveillance is not known in advance. Random deployment is generally faster and cheaper than deterministic deployment but can differ in coverage and connectivity.
# Mechanical Properties of Cement-Treated Soil Mixed with Cellulose Nanofibre
## 2 Materials and the Mixture Method
### Characteristics of Soil and Cement  
Two types of soil were used as base materials: Kasaoka clay, which is powdered clay manufactured from a mudstone in Okayama Prefecture, Japan, and Kawasaki clay, which is marine clay collected from the seabed of the Port of Kawasaki. The properties of each sample are listed in Table 2. Kasaoka clay is classified as high-liquid clay with a liquid limit of 62.1%. Kasaoka clay has few impurities and is uniform. Kawasaki clay is classified as low-liquid clay with a liquid limit of 41.0%. Kawasaki clay is a natural marine clay collected by dredging and contains impurities (e.g., small amounts of shells and gravel). A small amount of water was added as a pre-treatment to liquefy it, and it was passed through a 2 mm sieve to remove impurities before the cement treatment. Artificial seawater was added until the liquid limit ratio became 1.55.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & \multicolumn{2}{c}{**TEMPO-CNF**} & \multicolumn{2}{c}{**ACC-CNF**} \\
**Identifier** & **TEMPO-CNF** & **TEMPO-CNF** & **ACC-CNF-S** & **ACC-CNF-B** \\ \hline Raw material & Wood pulp & Wood pulp & Softwood pulp & Bamboo pulp \\ State & Gel & Powder & Gel & Gel \\ Content rate & 2.0\% & 90\% & 1.8\% and 2.0\% & 2.0\% \\ Defibrating method & Chemical fibrillation & Chemical fibrillation & Physical fibrillation & Physical fibrillation \\ Manufacturing method & TEMPO-mediated & TEMPO-mediated & Aqueous counter & Aqueous counter \\ oxidation & oxidation & collision & collision \\ Appearance & & & & \\ \hline \end{tabular}
\end{table}
Table 1: Cellulose nanofibre used for tests.  
Three types of cement were used for the solidification process: ordinary Portland cement, high-early-strength Portland cement, and blast-furnace cement type B. Portland cement was used to determine the basic properties of the treated soil mixed with CNF. In Section 4, the results of the study on the strength variability of the treated soil are discussed by making an assumption about the treated soil in the field. Here, blast-furnace cement type B was used, assuming the soil in the field was treated. In Japan, this type of cement is frequently used for ground improvement in port and in harbour engineering. Blast-furnace cement type B is a relatively inexpensive cement that is prepared by adding blast-furnace slag powder to cement. It also has high durability against seawater and chemical substances, and low leaching of hexavalent chromium.
# A Deep Learning Approach for Short-Term Electricity Demand Forecasting: Analysis of Thailand Data
## 1 Introduction
### Challenges  
Short-term load forecasting plays a vital role in the energy industry, as precise predictions of future electricity demand are essential for maintaining the reliable and efficient operation of power systems [6]. Weather conditions, economic trends, consumer behavior, and unforeseen events such as natural disasters all have an impact on load data, leading to fluctuations that make accurate forecasting difficult, especially over long periods [7]. Deep learning models can independently learn detailed patterns and relationships while adjusting to variations in the data-generating process. However, the training and deployment of these models demand substantial amounts of data and computational resources [8]. Energy systems are constantly evolving, influenced by policy changes, technological advancements, and market fluctuations. Researchers are consistently attempting to enhance existing forecasting models to mitigate the occurrences of both under and over forecasting, depending upon the effects of these influential parameters [9]. In this context, researchers face significant challenges in minimizing prediction errors. Their task involves developing sophisticated models and thoroughly testing them across diverse datasets, including commercial, residential, and collective loads [10].
## 3 Experimental Setup
### Hyperparameter Settings  
The speech from N-zhvoice and NR-zhvoice are used as input for the pre-trained model. Meanwhile, the k-means clustering value of MFCC from zhvoice/reverberation zhvoice dataset serves as the iterative target. A sub-dataset of the N-zhvoice/NR-zhvoice is used as the training data for the speaker verification model, with the speaker classification labels designated as the objective for learning. The pre-trained model is trained for 15 epochs, after which the speaker verification network is further trained for an additional 60 epochs. The proposed method, implemented in Python 3.8 using PyTorch 2.0.0, was executed on an Ubuntu 20.04 system equipped with two pieces of RTX 4090 GPUs and one piece of Intel(R) Xeon(R) Gold 5218R CPU (Intel, Santa Clara, CA, USA).  
1. Training details and settings of the pre-trained model: For MFCC, the frame-length is 25 ms, the frame-shift is 10 ms, the sampling frequency is 16 kHz, and the MFCCs are 39. The CNN encoder utilized a downsampling rate of 1/320. The values for both mask-prob and mask-channel-prob are both set to 0.9, with an approximately 1:1 ratio of masked to non-masked features. The mask length is set to 10 and the learning rate is set to 5 \(\times\) 10\({}^{-4}\), weight decay to 1 \(\times\) 10\({}^{-2}\), and the optimizer employed is Adam with beta parameters randomly sampled from the range (0.9, 1.8). The dropout rate is fixed at 0.1.
2. Training details and network settings of TDNN: The model is trained with a learning rate of 1 \(\times\) 10\({}^{-3}\) and weight decay of 1 \(\times\) 10\({}^{-6}\), using Adam as the optimization algorithm, and 512 feature channels. The batch size is set to 12, and the maximum duration of audio clips is limited to 3 s. For the loss function, the Angular Additive Margin (AAM) [57] is employed with a margin of 0.2 and a scale factor of 30.
## 4 Metal-Oxide Nanoparticles (MONPs) in Water Treatment
### Silver Oxide Nanoparticles  
Silver nanoparticles (AgNPs) are capable of degrading dyes and organic pollutants by adsorption and catalytic/photocatalytic activity [143]. They have a narrow band gap (HOMO to LUMO) between 1.2 and 1.6 eV and behave like p-type semiconductors. This narrow bandgap helps them absorb the full range of solar light and works as an excellent photocatalytic agent to break down organic pollutants in water [144]. In addition, their specific surface area affects the effectiveness of silver ions that are released from their surfaces. [PERSON] et al. [137] and [PERSON] et al. [138] achieved 99% of malachite green dye and 97.57% of MB dye removal using AgNPs, respectively.  
Ag\({}_{2}\)O NPs can be produced by a variety of techniques: solid phase, like energy ball milling and physical pulverization of the metal; gas phase, like electrochemical processes and evaporation/vapor condensation; and liquid phase, like chemical reduction and irradiation-assisted methods [36]. Ag\({}_{2}\)O NPs can be customized for specific applications using a variety of synthesis techniques, utilizing their special qualities for environmental remediation. Green synthesis is a novel trend in Ag\({}_{2}\)O nanomaterials synthesis where the sustainable nature and uniformity of the nanostructures are observed compared to conventional methods [145; 146]. Table 6 summarizes the applications of Ag\({}_{2}\)O NPs in the field of water treatment.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Nanomaterial** & **Modification** & **Synthesis** & **Voluntum Type** & **Mechanism** & **Removal Time** & **Removal** & **Ref.** \\ \hline CuO NPs & NA & Co-precipitation & NO\({}_{3}\)– ions & Adsorption & 120 min & 66.9–71.56 & [137] \\ \hline CuO NPs & NA & Simple & Acrylic acid & Adsorption & 180 min & 75-78 & [138] \\ \hline CuO NPs & NA & Bio-reduction & Co, Pb, Ni, Cd, Cr (VI) & Adsorption & 20 min & Co: 73.2, Pt: 80.8, & \\  & & & Cr (VI), Pt: 9.4 & & & \\ \hline CuO NPs & Ag doped & Bio-reduction & Cr and Cd & Adsorption & 72 h & Cr: 2.11 & [140] \\ \hline CuO NPs &
\begin{tabular}{c} Loaded into nano \\ porous activated carbon \\ \end{tabular} & Co-precipitation & MB dye & Adsorption & 180 min & 93.51 & [141] \\ \hline CuO NPs & NA & CuO NPs & Ni\({}^{2+}\), Cr\({}^{2+}\) ions & Adsorption & 30 min & Ni\({}^{2+}\), Cr\({}^{2+}\) & [142] \\ \hline \hline \end{tabular}
\end{table}
Table 5: Recent findings on the application of CuO NPs in water treatment.
## 2 Dual-Channel Transformer Model
### Quaternion SoftMax-like Activation Function  
In order to improve the expression ability of the model, activation functions are often added to the neurons, and the features are treated nonlinearly to improve the expression richness of the model. However, in the SPEED dataset, the orientation quantity of the satellite pose is represented by the quaternion, which makes the traditional activation function unable to meet the characteristics of the label information. In order to predict the model precision more effectively, a certain quaternion is no longer determined as the maximum value, but a probability value is assigned to the quaternion of each output classification, representing the possibility belonging to each category. In this way, the output quaternion is converted to its sum of squares being 1, and the probability distribution of the output value ranges from 0 to 1. Based on SoftMax activation function and batch normalization [39], we design a quaternion SoftMax-like activation function which is similar to the SoftMax function in form:  
\[f(q_{i})=\frac{exp(-\frac{1}{2}q_{i})}{\sqrt{\sum\limits_{i=0}^{3}exp(-q_{i})}} (i=0,1,2,3) \tag{9}\]  
where \(q_{i}\) denotes the quaternion.  
The advantage of this construction is that the output of the activation function lies within (0, 1), the output range is limited, the optimization is stable, and it can be used as the output layer. At the same time, the activation function is continuous, which makes it smooth and easy to differentiate. In addition, the constructed quaternion SoftMax activation function satisfies the following quaternion constraints:  
\[\sum\limits_{i=0}^{3}f^{2}(q_{i})=1 \tag{10}\]
## 3 Materials and Methods
### Parameters  
The simulations conducted for this research can be split into two parts: in the first part of tests, the K value is gradually increasing ((5,7,9,11), and in the second part, the N value is increasing ((5,13,34,89)). All tests are executed with L value from the range: {2,4,6} and QBER value from the range {3,5,7}, which corresponds to the following percentage of synchronization of victims' machines at the start of the synchronization: {97%,95%,93%}. The QBER values that have been chosen were selected to reflect a realistic synchronization process in quantum cryptography, where QBER is typically a few percent (as mentioned in Section 2.1.2).  
The tests for each set of parameters have been repeated with the following update algorithms:  
* All parties: hebbian;
* Victims: dynamic_rows, attacker: hebbian;
* Victims: dynamic_matrix, attacker: hebbian;
* All parties: random walk;* Victims: dynamic_rows, attacker: random walk;
* Victims: dynamic_matrix, attacker: random walk;
* Victims: dynamic_rows, attacker: random rule;
* Victims: dynamic_matrix, attacker: random rule.  
Each test has been repeated 500 times. Part of the results is presented in the paper, while all of the results, as well as the source code, are available in a dedicated GitHub repository [42]. The values used for analysis were the total number of updates between the victims and attackers' synchronization percentage (as well as the 68% confidence intervals of these values). Throughout the paper, the leading zeros of the values have been omitted.  
Although only some of the test results will be presented in this work, an analysis of all of the results has been conducted based on several questions.  
* In how many cases did the dynamic algorithms result in the attacker retrieving less information when the attacker chose the hebbian rule?
* In how many cases did the dynamic algorithms result in the attacker retrieving less information when the attacker chose the random walk rule?
* In how many cases did the dynamic algorithms result in the attacker retrieving less information when the attacker chose a random update rule at each iteration?  
The algorithm-specific analysis includes a direct comparison to the baseline scenarios in which all parties utilized the same update algorithm. The final category of analysis, involving random rule selection by the attacker, has been further divided to include comparisons against both hebbian-only and random walk-only reference cases.  
During the analysis, the upper limits of the confidence intervals for the dynamic algorithms' results were compared to the lower boundaries of the confidence intervals for the corresponding standard algorithms. For example, if the percentage of synchronization for a dynamic algorithm was 63.52 \(\pm\) 0.3, and for a standard algorithm it was 65.09 \(\pm\) 0.31, the comparison was made between 63.82 (i.e., 63.52 + 0.3) and 64.78 (i.e., 65.09 - 0.31). Additionally, for convenience in presentation, a one-hundredth value has been added to the largest partial result to ensure that the entire pie chart sums to exactly 100%.
## 1 Introduction
[16], carried out three ballistic tests with samples in the shape of brick walls fixed in steel frames. In samples one and two, the wall panels were coated with polyurea only in the inside face, while the third panel took a coating on both sides. The performance tests showed that applying the polyurea coating only on one side of the wall can effectively increase its resistance against shock waves. [PERSON] et al. [17] studied polyureas formed by the reaction of isocyanate Isonate 143 L from Dow Chemical and amine-terminated resin blend Versalink(r)P-650 or Versalink(r)P-1000 from Air Products. The chemical composition of the polyurea samples was altered to provide different hard segment ratios. The sample PU605, made with Versalink P-650 with 105% molar equivalent Isonate 143 L, is stiffer than the sample PU105, developed by mixing Versalink P-1000 with 105% molar equivalent Isonate 143 L.  
[PERSON] et al. [18] studied the blast response of lightly attached concrete masonry unit walls with several different types of retrofits:  
1. A one mm thick Fiber Reinforced Polymer (FRP) with a modulus of elasticity of 26.13 MPa, a tensile strength of 600 MPa, a 2.24% elongation at break, and a density of 0.915 kg/m\({}^{2}\). 2. A polyurea with an elastic modulus of 234 MPa, secant modulus of 165 MPa, yield strength of 11.5 MPa, and tensile strength of 13.8 MPa. The elongation at yield and rupture are 4.7 and 89%, respectively. 3. A one mm thick sheet hot-dipped in A-36 galvanized steel placed behind the wall. The three retrofit types were tested at the same loading level, which caused the nonretrofitted wall to fail at a velocity of 8.2 m/s. The results show that the FRP and polyurearetrofits prevented the debris from entering the structure, therefore, were considered successful retrofits. In this study, two commercial polyurea were compared to determine their capability to be used as a coating for explosion protection on building facades. An extensive plan of tests was designed and performed to evaluate this possibility. The procedure of tests included physical, chemical, thermal, and mechanical characterization of the polyureas samples.
# Noise Reduction Mechanisms of an Airfoil with Trailing Edge Serrations at Low Mach Number
###### Abstract  
Trailing-edge serrations have proven to be valid applications of trailing edge noise mitigation for an airfoil, while the physical noise reduction mechanism has not been adequately studied. We performed simulations employing Large-eddy simulation and the Lighthill-Curle method to reveal the variation in the hydrodynamic field and sound source due to the trailing edge serrations. The grid resolution and computational results were validated against experimental data. The simulation results show that: the trailing edge serrations impede the growth of spanwise vortices and promote the development of streamwise vortices near the trailing edge and the wake; the velocity fluctuations in the vertical cross-section of the streamwise direction near the trailing edge are reduced for the serrated airfoil, thereby obviously reducing the strength of the pressure fluctuations near the trailing edge; and the trailing edge serrations decrease the distribution of the sound source near the trailing edge and reduce the local peak value of sound pressure level in a specific frequency range as well as the overall sound pressure level. Moreover, we observed that, in the flow around the NACA0012 airfoil, the location where the strong sound source distribution begins to appear is in good agreement with the location where the separated boundary layer reattaches. It is therefore effective to reduce trailing edge noise by applying serrations on the upstream of the reattachment point.  
trailing edge serrations; trailing edge noise reduction; airfoil; computational aeroacoustics Article
## 2 Seawater Channel Modelling Elements
### Scintillation and Turbulence
#### 2.5.1 Scintillation  
Scintillation refers to rapid fluctuations in the intensity and phase of a light signal caused by random variations in the refractive index of the medium.  
Scintillation can affect the AOP of the water by causing fluctuations in the received signal intensity. While scintillation is primarily a consequence of turbulence, it can contribute to variations in the apparent intensity of the received signal, which can be considered when analysing AOPs, such as signal attenuation and optical SNR.
# A Framework for Security Transparency in Cloud Computing
## 4 Cloud Security Transparency
### Why do We Need Security Transparency in Cloud?  
One of the most considerable obstacles to successful cloud migration is the management of security that is relatively aggravated by the non-transparent nature of CSP to disclose security related information associated with their offerings [23]. Users are driven by the fear of unrevealed occurrences cognate to ongoing CSP control procedures, and they always endeavour to make informed decisions by relying on CSP disclosures to achieve optimum security goals and operate in compliance with necessary requirements. A successful adoption of cloud technology by corporate users, businesses and organisations requires a clear-cut disclosure of the security policies, designs and practices of CSPs, including ongoing visibility of relevant security measures. These requisites for transparency constitute the pathway for users to assess the possible risks of CC and its potential impact on assets. For example, a CSP may choose to outline the policies and procedures being employed to ensure the availability of user data by disclosing information on the architectural setup of backup plans, business continuity and redundancy strategies that ensure continuous data availability. Security transparency in public clouds is considered as demanding a substantial magnitude of interest in comparison to other deployment models, due to its characteristics of being open to the public and serving a broad customer base. In contrast, other deployment models such as private clouds are specifically designed for individual organisations thereby offering customised functionalities that do not necessitate transparent operations.
# A Novel Wideband Splitter for a Four-Element Antenna Array
## 3 Proposed Antenna Array Splitter
### Synthesis Procedure of the Proposed Antenna Array Splitter  
The characteristic impedance of the symmetrical transmission line \(Z_{0s}\) is significantly higher than the characteristic impedance \(Z_{0}\) and has a significant effect on the electrical parameters of the proposed structure. The greater the value of the characteristic impedance \(Z_{0s}\), the greater the bandwidth of the proposed splitter. On the other hand, the greater value of the characteristic impedance \(Z_{0s}\) can cause unwanted parasites that could disturb the phase symmetry of the proposed splitter. For a system with \(Z_{0}=50\)\(\Omega\), the value of \(Z_{0s}\) ranges from 80 \(\Omega\) to 160 \(\Omega\), and for a system with \(Z_{0}=75\)\(\Omega\), the value of \(Z_{0s}\) ranges from 120 \(\Omega\) to 240 \(\Omega\).  
In addition, the value of the characteristic impedance of the symmetrical transmission line \(Z_{0s}\) is also influenced by the copper cover-supporting and shielding part of the proposed structure. The copper cover decreases the value of the characteristic impedance \(Z_{0s}\). The consequence of this decrease in the impedance \(Z_{0s}\) is primarily a reduction in the bandwidth of the splitter. In the design of the combiner, it is necessary to respect the described effect of the shielding cover.  
For this purpose, it is necessary to define an impedance decrease correction factor \(\xi\). This correction factor can take values ranging approximately from 0.5 up to 0.95. Values closer to 0.5 represent a significant influence of the shielding cover, and on the other hand, values closer to 0.95 indicate a negligible influence of the shielding cover. These values depend on the mutual arrangement of the shielding cover with the proposed structure. The characteristic impedance of the symmetrical transmission line, taking into account the influence of the shielding cover, is subsequently calculated as a product of \(\xi\times Z_{0s}\).
## 4 Experimental Analysis
### Experiment #1  
In the initial experiment, we conducted training sessions for several CNN architectures aimed at classifying ear images, utilizing two datasets, namely, MAI and AWE. These datasets were employed as input for four pre-trained CNN architectures: AlexNet [32], VGG-16 [33], VGG-19 [33], and ResNet-50 [29]. The initialization of the pre-trained CNNs involved setting their weights to the corresponding weights of the ImageNet models [34]. Subsequently, fine-tuning of the CNNs on the ear datasets ensued, employing lower learning rates to enable the networks to adapt their weights to the specific characteristics of ear images. Data augmentation techniques were implemented to enhance the training of the CNNs.  
The primary objective of this experiment was to discern the most robust architecture among the considered models, tailored for the task of ear classification. It is crucial to note that in this experiment, neither the suggested pre-processing nor the Mean-CAM techniques were applied. The outcomes of this experiment are delineated in Table 1, with the optimal results being highlighted in bold.  
The outcomes presented in Table 1 unequivocally indicate that ResNet-50 outperforms other competitive architectures across the three employed performance metrics. ResNet-50's performance on the MAI dataset is particularly noteworthy, achieving a Rank-1 recognition rate of 98.66%. However, the corresponding Rank-1 rate for the AWE dataset was considerably lower, at 57.75%. MAI is a simple and undemanding dataset, whereas AWE is considered as one of the most challenging datasets for ear recognition. This discrepancy underscores the imperative for an alternative approach beyond mere augmentation of the training set and the fine-tuning of weights.