esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Proposed Method
#### 3.3.3 Dataset Balancing  
The last step of the proposed method is the dataset balancing process. The _filtered dataset_ still presents a highly unbalanced multi-class distribution. Resampling and data augmentation techniques are usually employed for handling unbalanced datasets. Data statistics are analyzed to avoid losing relevant data (downsampling) or generating too many artificial samples (upsampling) that may affect the data features. A target number of images per class close to the median value of the _filtered dataset_ has been selected, with a lower bound equal to 75% of the median and an upper bound equal to 150% of the  
Figure 4: Analysis on the effects of the varying filtering threshold on the _clean dataset_. The classes having a number of images below the corresponding threshold that will therefore be removed from the _filtered dataset_ are shown in red. The remaining categories to be included are shown in blue. Yellow and green, respectively, highlight the mean and the median computed based on the cardinality of the remaining classes. median. These two values define the \"acceptance window\" and therefore the classes whose cardinality is within this window and do not need modifications. An excessively high lower bound leads to the generation of a large number of synthetic samples that may introduce biases; on the contrary, a lower bound that is too small prevents the usage of data augmentation for oversampling. Given the high number of classes with a cardinality below the median, it has been found that 75% offers the right trade-off between classes that undergo data augmentation and unchanged classes. The value selected for the upper bound has to guarantee that each subcategory coming from the splitting of a class with a significant number of images is still significantly populated for the purpose of the experiment. A median value of 150% ensures that if a class is selected for splitting, all the generated subclasses will have at least 50% of the target images and it is therefore significant for the balancing process. After selecting the boundaries of the acceptance window, the following rules have been employed:  
* Classes with a number of images within the above defined range are left unchanged. * Classes with a cardinality below the lower bound of the range are upsampled. * Classes with a cardinality exceeding the upper bound undergo a downsampling process generating new subclasses. The pseudo-code and the corresponding flow-chart for the balancing procedure are indicated in Algorithm 1 and Figure 5, respectively.
## 2 Related Works
### Architecture of VCC  
The following description of VCC architecture is based on studies such as [1; 11; 12; 13]. VCC architecture is composed of five layers: Physical, Communication, Cloud, Cloud Service, and VCC Application Layers.  
The _Physical Layer_ is divided into the traffic control center and transportation infrastructure that is installed on the road. The traffic control center manages the relationship between the vehicle and infrastructure and determines traffic conditions through periodic communication. In addition, it maintains a friendly relationship with services such as police, fire, and emergency services and copes quickly with any traffic accidents that may occur. The transportation infrastructure enables communications between the vehicle and itself. It collects traffic information such as the location of the vehicle or volume of traffic and transmits this collected information to the traffic control center and other vehicles. In addition, Wireless-Fidelity (Wi-Fi) is provided for drivers and pedestrians.  
The _Communication Layer_ includes all of the communication required by the VCC for communication between vehicles and between vehicles and the traffic infrastructure, as well as inside the vehicle. In addition, it manages periodic communication between objects. Particularly for communication inside the vehicle, the Communication Layer sends speed and location information, fuel status, driver behavior, driver health status, external environment information, _etc._ obtained from the sensors mounted in the vehicle to the Cloud Layer.  
The _Cloud Layer_ is composed of Cloud Storage to store data sent from the Communication Layer and the Cloud Server to process the data. Cloud Storage stores road traffic information, vehicle location, surrounding vehicle location information, and driver or user personal information. The Cloud Server processes the data in Cloud Storage for use in applications.  
The _Cloud Service Layer_ is combined of cloud computing technology to manage and enable services in the VCC environment. Cloud-based services in the vehicle computing environment are: Network as a Service (NaaS), Storage as a Service (SaaS), Cooperation as a Service (CaaS), and Computing as a Service (CompaaS).  
The _VCC Application Layer_ is the layer that manages the Cloud Service-based applications that can be applied to the VCC environment. Based on the internal and external information of a vehicle, it measures traffic volume and provides functions to prevent the vehicle concentration that often occurs in inner-city areas. In addition, it prevents accidents and manages emergency situations, parking, public transportation, _etc._
# The Effect of Countermeasure Readability on Security Intentions
## 2 Theoretical Background
### Text Readability
#### 2.2.2 Limitations of Readability Metrics  
There are a few limitations to traditional readability metrics. Firstly, in general, they focus solely on internal features of the texts in question and ignore the familiarity (or lack thereof) that readers might have with terms (prior knowledge) [41].  
Secondly, they do not take semantics into account. This means texts could have issues concerning their meaning, affecting their readability. Thirdly, these formulas presuppose people are similar in interest, motivation, maturity, reading skill, and characteristics [35, 42]. Fourthly, formulas do not score textual organization and design [35].  
Lastly, they are language-specific, being optimized only for one language [43]. This implies usage for other languages is not possible without tweaking and tuning them.  
Fortunately, the field of natural language processing (NLP) is catching up, meaning algorithms with higher accuracy have already emerged. These can take into account as many as 70 features and can classify sentences far more precisely.  
However, it should be noted that work by [PERSON] et al. [40] concluded that basic features, such as sentence length and word length, are still the most important in both sentence and document readability classification. Nonetheless, since traditional formulas take in basic internal features (e.g., average sentence/word length or average syllables per word), measurements become increasingly inaccurate as text length decreases.
# CT-RPL: Cluster Tree Based Routing Protocol to Maximize the Lifetime of Internet of Things
## 4 Results and Discussion  
The purpose of the simulation is to examine the effectiveness of CT-RPL. Contiki COOJA simulator version 3.0 was utilized for the simulation. In this simulation, the number of RPL routers is taken to be 100 and DODAG root node to be 1. The simulation's duration is one hour. The simulation network area is \(200\times 200\) m\({}^{2}\). The value of the RPL parameter is 256, which is the default value. Table 3 displays the simulation settings for the research.
# Performance Comparison of ANFIS Models by Input Space Partitioning Methods
## 4 Experimental Method and Result Analysis  
In order to evaluate the prediction performance of the ANFIS model using each of the input space partitioning methods described in Section 3, we conducted an experiment to predict the electric energy generation of a combined power plant using the CCPP database.
## 3 The Lossless Adaptive Data Compression Method Based on the Binomial Number System (BNS)  
### Description of the Data Compression Method with BNS  
According to the proposed method, compressible binary sequences, by determining the number \(k\) of units contained in them, are first converted into equilibrium binomial code of the binary BNS with code length \(m<n\). Then, by removing ones to the LSB to the first zero or zeros to the first LSB unit are converted by a binomial numerical (numbering) function into numbers of one of the natural number systems (natural numbers), as a rule, of length \(l<n\) (see Figure 2).  
In practice, the binary number system numbers are usually used as such natural numbers. Conversely, restoring a compressed number is transferred from it using a binomial numbering function to a binomial number and then by adding zeros or ones to the least significant digits to the original binary sequence of length \(n\).
# Design and Performance of a XBee 900 MHz Acquisition System Aimed at Industrial Applications
## 4 Experimental Tests of the Monitoring System
### Reception of Simple Sampling Distributed Frames from Several SMNs  
This test is aimed at reproducing the behavior of Figure 2. The Coordinator Node sends a broadcast measurement order and collects the measurements issued by three Smart Measuring Nodes, each acquiring the values of four attached sensors. Frames sent by each Smart Measuring Node had 29 bytes as in Figure 4. The measuring order was sent periodically every 60 ms. Slots of 15 ms were defined for every Smart Measuring Node to send its values. Table 4 shows the time instants in which the Coordinator Node received the data from every Smart Measuring Node. This test was executed for 10 min, so that 10,000 frames were issued by every Smart Measuring Node. It could be observed that messages from the Smart Measuring Nodes arrived in a timely manner. The maximum value-reception value for the last Smart Measuring Node was less than 56 ms, enabling an overall sampling period of 60 ms. Results shown in Table 4 proved that the system could acquire the values of up to 12 sensors (four sensors connected to every Smart Sampling Node) at a frequency of 60 ms.
## 2 Related Work
### URL-Based Methods  
URL-based methods have been widely explored in the literature for phishing website detection. These approaches typically analyze the features and patterns in URLs to identify phishing attempts. Some common URL features include the length of the URL, the presence of special characters, and the use of subdomains. Techniques such as lexical and token analysis can be employed to identify patterns within the URL structure. Researchers have used machine learning algorithms, such as Support Vector Machines (SVMs) and Decision Trees, to classify URLs based on the extracted features [15; 16].  
Recently, deep learning methods such as convolutional neural networks (CNNs) and natural language processing (NLP) have been applied to automate cybersecurity tasks [17; 18; 19; 20], including the automatic extraction of URL features and the detection of phishing websites. [PERSON] et al. [21] proposed an end-to-end deep learning framework called URLNet, which uses a convolutional neural network to directly learn non-linear URL embeddings, overcoming the limitations of traditional machine learning methods that only rely on the lexical properties of URL strings and require manual feature engineering. [PERSON] et al. [22] proposed the Texception network, which utilizes character-level and word-level information to predict whether a URL is part of a phishing attack. [PERSON] et al. [23] proposed an online malicious URL and DNS detection approach based on a character-level deep neural network, which maps URL and DNS strings into vector forms and uses a CNN network framework to automatically extract malicious features and train classification models. Experimental results on real-world URL and DNS datasets demonstrate that this method outperforms several state-of-the-art baseline methods in terms of efficiency and scalability.  
Although there are numerous URL-based methods available [24; 25; 26; 27; 28; 29; 30], all of these methods may struggle to detect newly created phishing websites or those employing advanced obfuscation techniques, such as masquerading URL addresses. Additionally, phishing attackers frequently change their website URLs to evade detection, posing challenges for URL-based methods.
## 3 Proposed Model
### Proposed Architecture  
The proposed architecture integrates WGAN and Skip-GANomaly, as shown in Figure 3. WGAN avoids the various training challenges inherent to the conventional  
Figure 2: Discriminator architecture.  
GAN and uses the EM distance smoothness to completely resolve the vanishing gradient issue. In addition to producing satisfactory results for the anomaly detection task, SkipGANomaly uses three loss functions to improve the generator's performance in identifying anomalous objects [36]. We also converted the hard labels of the GAN network into smooth soft labels and reduced the optimizer's learning rate at specific iterations of the training process to determine the optimal weights of the neural network. Furthermore, LeakyReLU was applied in each activation layer to prevent gradient vanishing. In detecting anomaly data, we use the anomaly score that was proposed by [32; 33]. We evaluate the new image data \(x\) as being normal or abnormal images. The anomaly score is defined by Equation (2).  
\[S(x)=\lambda R(x)+(1-\lambda)\mathcal{L}_{lat}(x) \tag{2}\]  
where \(\lambda\) is the weight for controlling the importance of the score function, \(S(x)\) is the anomaly score function, and \(R(x)\) is the reconstruction score that measured the contextual similarity between the input and generated image. In the testing dataset \(D_{test}\), we will obtain the anomaly score vector \(S\) such that \(S=\{\ S_{i}:S(x_{i}),x_{i}\in D_{test}\}\). Finally, we scale the \(S_{i}\) anomaly scores within the probabilistic range from 0 to 1. The hyperparameters are set as same as the reference [36].
## 1 Introduction
In recent years, researchers have modified natural zeolites with functional-group compounds to increase the adsorption capacity and selectivity [21; 22; 23; 24; 25; 26; 27; 28], with a few studies on improving the adsorption capacity of the Tsagaantsav natural zeolite. On the other hand, Mongolia has abundant copper reserves, including the Erdenet, Oyu Tolgoi, and Tsagaan Suvarga deposits. Water pollution has attracted attention due to the rapid development of mining in Mongolia. Researchers have concluded that mining wastewater pollutes the groundwater around mining plants [29; 30]. The adsorption properties of the zeolite-bead composite material and modified zeolite obtained from the Tsagaantsav deposit for Pb(II) and Cr(VI) were investigated [31; 32]. However, studies are lacking on the Cu(II) adsorption properties of modified zeolites of Mongolia. There are several reports in the literature on the adsorption properties of heavy metals onto silicates modified with organosilicon compounds [25; 33; 34; 35; 36]. However, there are not many reports regarding the rate-limiting step of the heavy metals onto modified zeolite. Determining the rate-limiting step of the adsorption process gives considerable information when considering the mathematical modeling equations used in the control of the process. The objective of this study was to obtain the adsorption properties of Cu(II) on the zeolite from the Tsagaantsav deposit in the Dornogovic province of Mongolia, modified with 3-Aminopropyltriethoxysilane (APTES). We processed the pretreatment method based on the chemical and mineral compositions of the Tsagaantsav deposit. In addition, we focused on determining the rate-limiting steps of the adsorption process based on the kinetic parameters of the process.
# Study on Active Tracking of Underwater Acoustic Target Based on Deep Convolution Neural Network
## 3 Simulation and Verification of Real World Signal
### Simulation Target Data Set Generation and Model Training  
We complete the programming under the TensorFlow 2.0 framework. The neural network model built by TensorFlow 2.0 can realize cross-platform model deployment and is more flexible than TensorFlow 1.0. TensorFlow 2.0 has certain requirements for hardware configuration. We use GPU for model training and use a small server in the laboratory to complete the above work. The graphics card is configured with two Tesla T4s and the GPU memory is 2 \(\times\) 15 Gb.  
In the simulation, according to the data set generation method given in Section 2.1, we generated a data set of moving targets with different echo characteristics. The number of samples for each target in the data set is 1800, and samples of ocean background noise are generated at the same time, the number is 1500, and the total number of samples in the data set is (1800 \(\times\) num + 1500), where num is the number of tracking targets. Some sample images in the data set are shown in Figure 12. The order of the samples is randomly shuffled.  
Figure 11: Simulation of time domain and frequency domain waveforms of the transmit signal.  
Then the data set is input to the DCNN model designed in Section 2.2 for training, and the accuracy of the model is tested. When training the model, 60% of the data in the data set are used as the training sample, and 40% are used as the test sample. Part of the training parameters are set as follows, the batch size is 40, the training optimizer is \"Adam\", and the loss function is the multiclassification loss function \"categorical_crossentropy\". The accuracy and loss of the DCNN model after 50, 100, and 200 trainings are shown in Figures 13-15.  
In Figure 13, after the DCNN model is trained 50 times, the accuracy rate rises to about 0.95, and the loss decreases to about 0.26.  
In Figure 14. After the DCNN model was trained 100 times, the accuracy rate gradually increased to 0.97, and the loss decreased to 0.18.  
In Figure 15, after the DCNN model was trained 200 times, the accuracy rate increased to 0.986 and the loss decreased to 0.06. Through training and testing, it is shown that the DCNN model designed in this paper can efficiently learn the characteristics of the target echo signal, and the model recognition accuracy rate is high.  
Figure 12: Partial samples of the data set. The data set contains echo signals of two different targets and ocean background noise.  
Figure 14: Accuracy and loss after training 100 times.  
Figure 13: Accuracy and loss after training 50 times.
###### Abstract
The temperature selected was based on the thermal decomposition temperatures of LDHs and carbonates. In the case of Mg-carbonates, the primary components of ancient magnesium mortars, an endothermic decomposition process occurs, releasing water and CO\({}_{2}\) within a temperature range varying approximately between 220 and 550 \({}^{\circ}\)C. The thermal decomposition mechanisms of LDHs have been extensively studied, and typically exhibit a three-step mass loss pattern, namely, dehydration (25-280 \({}^{\circ}\)C), dehydroxylation (280-400 \({}^{\circ}\)C), and anion expulsion (>400 \({}^{\circ}\)C), resulting in the gradual collapse of the double-layered structure [13; 16; 17; 18; 19]. By successfully eliminating the contaminant fractions, we achieved reliable radiocarbon ages that align with the historical timeline of the San Salvatore complex. Furthermore, our findings shed light on the viability of radiocarbon dating for magnesium mortars, a previously overlooked aspect of archaeological research. Through this investigation, we aim to contribute to a deeper understanding of the construction history and material compositions of the San Salvatore complex, offering valuable insights into the architectural structures. Additionally, our study contributes to further research into radiocarbon dating of magnesium mortars, potentially refining the approaches used in archaeological dating methods for similar historical sites worldwide.
# Effect of Aggregate Type on the Shear Behavior of Reinforced Lightweight Concrete Beams
## 5 Test Results and Analysis
### Measured Strain Values  
Concrete and steel strains were measured at every load increment. At service loads, the concrete compressive strains ranged between 400 and 1300 \(\times\) 10\({}^{-6}\). The measured strain values on the concrete surface were quite similar regardless of aggregate type, with no significant differences observed. The measured concrete and steel strain values ranged from 2000 to 5000 \(\times\) 10\({}^{-6}\) and 2000 to 6000 \(\times\) 10\({}^{-6}\), respectively. Strain gauge values attached to the reinforcement were also consistent, regardless of aggregate type. This consistency is logical because all specimens had a uniform arrangement. The homogeneity in strain gauge readings, both on the concrete surface and the reinforcement, indicates that the structural behavior under load was uniformly distributed. This demonstrates that the type of coarse aggregate used did not significantly affect the strain distribution within the RC beams. The uniformity in strain values suggests that the reinforcement detailing and concrete properties were the dominant factors in the structural performance of the beams.
## 4 Concluding Remarks  
We have investigated the high-speed OAM multiplexed FSO communication system, enabled by AO based wavefront correction and LDPC coding. The inter-mode crosstalk was first compensated by the wavefront sensorless AO setup, and the residual mode crosstalk induced data interference was later solved by sufficiently strong LDPC coding.  
We also presented a crosstalk-resistance solution in an OAM multiplexed FSO link based on SO and coded modulation. More than 1 dB SCTR improvement has been shown for the used OAM modes. Moreover, the 5/9-QAM schemes exhibit a better crosstalk tolerance than regular QPSK and 8 QAM schemes.  
Sometimes, more advanced modulation formats are used to further increase channel capacity. When high-order modulation formats are used, FSO communication systems are more sensitive to atmospheric turbulence. Some turbulence compensation solutions may not work well. Stronger FEC coding may be used to protect systems reliability. Optical domain turbulence compensation solutions, like adaptive optics, are suggested for implementation before mode detection. Nonuniform coded modulation may still work, but should not bring much performance improvement like 5/9 QAM formats. MIMO processing solutions will face a more severe challenge, due to the increasing computation complexity and the reduced robustness of the MIMO equalization. This research was supervised by I.B.D. All laboratory work was done by Z.Q. This research was funded by ONR grant number [N00014-13-1-0627]. The authors declare no conflicts of interest.