esa-sceva/satcom-chunk-collection huggingface dataset:
# Blind Phase Search with Angular Quantization Noise Mitigation for Efficient Carrier Phase Recovery
## 1 Introduction  
Carrier phase recovery (CPR) is an essential block to estimate and compensate for the phase noise introduced by the free running lasers in coherent optical communication systems. Two approaches for CPR are conventionally employed based on either the M-\(th\) power operation [1] or on the conventional blind phase search (C-BPS) algorithm [2]. The M-\(th\) power operation approach provides a poorer phase noise tolerance for high order constellations as only a small percentage of constellation points can be employed for phase noise estimation. Additionally, the accuracy of symbol amplitude discrimination required for symbol classification or partitioning in this approach decreases for low optical to signal noise ratios (OSNRs) or high order constellations [3; 4; 5]. On the other hand, the C-BPS approach provides high phase noise tolerance at the expense of a high computational complexity. The number of required test phases to achieve high phase noise tolerance largely increases with the modulation order which increases the overall computational complexity of the algorithm and becomes a burden for its practical implementation. Alternatively, multi-stage CPR schemes employing both approaches in different stages have also been proposed with the aim of providing good phase noise tolerance while maintaining a relatively low overall computational complexity [3; 4; 5; 6; 7; 8; 9; 10; 11; 12]. However, the implementation complexity reductions achieved by the proposed multi-stage algorithms are not the minimum achievable and could be further reduced by overcoming the inherent limitations of the C-BPS stage provided in this manuscript. High phase noise tolerant CPR schemes at low implementation complexity levels would make it possible to use smaller integrated DFB lasers, drivenat low power for high order modulations or DBR lasers with electronic tuning via carrier injection instead of thermal tuning.  
In this paper, we demonstrate the inherent limitation of the C-BPS algorithm due to angular quantization noise and propose a cost effective solution to mitigate it, using a post-BPS low pass filter (LPF). The proposed filtered BPS (F-BPS) scheme increases the phase noise tolerance while it reduces the computational complexity compared to that of the C-BPS. The performance of the F-BPS algorithm is evaluated for 16 QAM and 64 QAM through simulations. Experimental validation of the F-BPS scheme on a 28-Gbaud 64 QAM is also performed. Results show a noticeable enhancement in the phase noise tolerance of the proposed F-BPS scheme compared to C-BPS and an achievable drastic reduction of its computational complexity for its practical implementation.
## 3 Methods for Calculating the Proppant Crushing Rate and Fracture Conductivity  
### Crushing Rate  
For a proppant particle, its arrangement structure is extremely chaotic, with irregular and irregular characteristics. Therefore, to calculate the proppant particle crushing rate, there are three steps involved: Firstly, the 2D proppant simulation results are required for mechanical analysis and linear equation solving to obtain the force values of each proppant particle in the crack under a given closure pressure. Secondly, follow up with sample curves and numerical algorithms to get the crushing rate \(\eta_{i}\) of each proppant particle [30; 31]. Finally, the average value is taken as the crushing rate of the proppant under a given closure pressure.  
\[\overline{\eta}=\frac{\sum\limits_{i=1}^{n_{p}}\eta_{i}}{n_{p}}. \tag{20}\]  
where \(\eta\) is the average crushing rate, dimensionless; \(\eta_{i}\) is the crushing rate; \(n_{p}\) is the total number of proppant particle, dimensionless; and \(i\) is the number of proppant particle, i = 1, 2,..., \(n_{p}\).
# Applying Topological Information for Routing Commercial Vehicles Around Traffic Congestion
## 4 Methodology and Model Development
### Crash and Incident Data Analysis  
Crash and incident datasets were investigated using the ArcGIS hotspot analysis tool and ArcGIS network analyst extension. A spatiotemporal analysis was conducted to analyze the characteristics of crashes and classify hotspot trends over time to identify the location with the highest crash rate. Crash and incident spatial and temporal attributes were linked with road segments into a network dataset that contains road edges, junctions, and traffic profiles. The following section explains the procedure for designing and creating a network dataset.
Sensors_**2014**, _14_, 22811-22847; doi:10.3390/s141222811  
\begin{tabular}{c}  \\ _series_ \\
**ISSN 1424-8220** \\ www.mdpi.com/journal/sensors \\ _Article_ \\ \end{tabular}  
On the MAC/Network/Energy Performance Evaluation of Wireless Sensor Networks: Contrasting MPH, AODV, DSR and ZTR Routing Protocols  
**[PERSON] 1,*, [PERSON] 1, [PERSON] 2, [PERSON] 2,**  
**[PERSON] 2 and [PERSON] 2**  
\({}^{1}\) Department of Electrical and Computer Engineering, Tecnologico de Monterrey, Ave. Eugenio Garza Sada #2501 Sur, Monterrey NL, 64849, Mexico; E-Mail: [EMAIL_ADDRESS]  
\({}^{2}\) Center for Research and Advanced Studies of IPN, Communications Section, Av. IPN No.2508, Colonia San Pedro Zacatenco, CP. 07360, Mexico D.F., Mexico;  
E-Mails: [EMAIL_ADDRESS] (A.O.-L.); [EMAIL_ADDRESS] (M.L.);  
[EMAIL_ADDRESS] (G.M.G.-T.); [EMAIL_ADDRESS] (O.O.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +52-81-840-4620.  
External Editor: [PERSON]  
_Received: 1 September 2014; in revised form: 17 November 2014 / Accepted: 25 November 2014 / Published: 2 December 2014_  
**Abstract:** Wireless Sensor Networks deliver valuable information for long periods, then it is desirable to have optimum performance, reduced delays, low overhead, and reliable delivery of information. In this work, proposed metrics that influence energy consumption are used for a performance comparison among our proposed routing protocol, called Multi-Parent Hierarchical (MPH), the well-known protocols for sensor networks, Ad hoc On-Demand Distance Vector (AODV), Dynamic Source Routing (DSR), and Zigbee Tree Routing (ZTR), all of them working with the IEEE 802.15.4 MAC layer. Results show how some communication metrics affect performance, throughput, reliability and energy consumption. It can be concluded that MPH is an efficient protocol since it reaches the best performance against the other three protocols under evaluation, such as 19.3% reduction of packet retransmissions, 26.9% decrease of overhead, and 41.2% improvement on the capacity of the protocol for recovering the topology from failures with respect to AODV protocol. We implemented and tested MPH in a real network of 99 nodes during ten days and analyzed parameters as number of hops, connectivity and delay, in order to validate our simulator and obtain reliable results. Moreover, an energy model of CC2530 chip is proposed and used for simulations of the four aforementioned protocols, showing that MPH has 15.9% reduction of energy consumption with respect to AODV, 13.7% versus DSR, and 5% against ZTR.  
keywords: wireless sensor networks (WSNs); performance metrics; energy consumption +
Footnote †: journal: Energy Communications Letters
# Hyperparameter Optimization of Ensemble Models for Spam Email Detection
## 3 Methodology
### Dataset Cleaning  
To improve the quality of classification, it is important to get rid of unwanted characters or features that constitute noise from the data. The cleaning activities and functions used are presented in Table 1.  
The noise constituent of the dataset such as non-ASCII characters, HTML tags, extra white spaces, URLs, punctuations, numbers, and stop words were removed in steps 1-7 (Table 1). Stop words are a collection of words in any language that occur with a high  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Steps** & **Data Processing Activities** & **Functions** \\ \hline
1 & Removal of non-ASCII codes & email\_text = gsub(“[’\textbackslash x01-\  
frequency but convey considerably less meaningful information about the significance of an expression. The removal of stop words and other noise constituents shrinks the size of the data and reduces the burden of computational expenses in model training, with the potential of improving model performance since there are only meaningful words left to learn from. The essence of stemming is also to reduce the data size by reducing words to their root word. The text-mining map function, defined in the text-mining (tm) package [30], received the parameters specified in steps 5-8 to execute each cleaning activity. The DocumentTermMatrix() and removeSparseTerms() functions are also defined in the tm package in R.  
On completion of the data cleaning process, the data was divided into two; the train set and the test set. The train set, which was composed of 70% (23,107) of the original dataset, has 11,282 legitimate emails and 11,825 spam emails (Figure 1). The test set, which is composed of 30% (9753) of the original dataset, has 4744 legitimate emails and 5009 spam emails.  
Baseline models of random forest and extreme gradient boost (XGBoost) were developed by training and testing each model independently with 70% and 30% of the preprocessed dataset (Figure 2). All the parameters were set to their default values during the training and testing of the baseline models. The performance of these models on the test data was recorded as the baseline performance to be improved via hyperparameter tuning. To reduce the computation time, only the important predictors were passed to the random forest. The random forest has an inbuilt feature for ranking variables or features based on their importance in arriving at a prediction [31].  
Figure 1: Distribution of ham and spam emails in train and test dataset.
## Security Analysis in Spatial and Frequency Domains
### Relative Entropy  
Relative Entropy (or Kullback-Leibler Divergence) is the measure of the distance between two probability distributions on a random variable. Given two discrete probability distributions \(p(i)\) and \(q(i)\) over histograms of the digital image, the relative entropy of one with respect to another is then defined as the sum of all possible states, which is formulated as (8).  
\[d=\sum_{i=1}^{k}p(i)\log_{2}\frac{p(i)}{q(i)} \tag{8}\]  
Intuitively it is the expectation of a logarithmic difference between two probabilities, which measures quantitatively how one probability distribution differs from another. Divergence acts as a convex function on the domain of probability distributions. The relative entropy reaches zero when probability distributions \(p(i)\) and \(q(i)\) are identical with each other.  
All three cases of digital image chaotic watermarking and dual chaotic watermarking were taken into account with the relative entropy data to the source images listed in Table 4. The relative entropies between dual chaotic watermarked images and source images were much greater than those of between single chaotic watermarked images and source images. It shows that dual chaotic watermarking was more secure and more robust than chaotic watermarking itself against various attacks (e.g., Ciphertext Attack).
## 3 Classification of Organic Matter in Shale and Its Morphological Characteristics
### Depositional Organic Matter  
Depositional organic matter primarily consists of original organic material and its alteration products, which remain enriched in situ and have not undergone migration, preserving their sedimentary characteristics. This includes kerogen, bitumen generated during thermal evolution, solid bitumen, and pyrobitumen [26]. The boundaries between depositional organic matter and surrounding inorganic minerals are distinct, with close contact but no associated authigenic minerals [56]. In this study, based on the identification of organic matter sources and pore characteristics, we classified bioclasts and in situ remnants from post-hydrocarbon generation within the depositional organic matter. Based on maceral characteristics, we identified alginite, zooclasts, and acritarchs as subtypes of bioclasts. Additionally, through microscopic morphology and pore characteristics, we identified compacted kerogen. As a result, by integrating organic matter sources and their microscopic occurrences, we classified the depositional organic matter in highly to over-mature marine shales into three categories: bioclasts, compacted kerogen, and in situ remnants from post-hydrocarbon generation. The bioclasts can be further subdivided into alginite, zooclasts, acritarchs, and encapsulated organic matter.
# Resource Management and Secure Data Exchange for Mobile Sensors Using Ethereum Blockchain
## 4 Methodology
### Network Model  
Mobile sensors initiate data transmission after validating the identity, as discussed in the previous section. The source mobile sensor \(s_{j}\) communicates with the target sensor, which is characterized by a higher trust score, by forwarding a request for initiating data dissemination by \(s_{j}\). After the validation, the system initially chooses the N2N mode of communication between the two mobile sensors. This operation is followed by disseminating data to the source sensor \(s_{j}\) obtained from the target sensor \(s_{o}\). The system records the positional information for the transmitting sensor \(s_{o}\) and receiving sensor \(s_{j}\), followed by evaluating an adequate distance between them. To obtain quality data, it is necessary to consider the possibility of interference when mobile sensors attempt to communicate concurrently with the AP and different mobile sensors. Potential interference will negatively affect the outcome of the experiment. Hence, it is necessary to determine the proportion of information that can be extracted from a given set of traffic-flow information. The signal quality is expressed by Equation (10).  
\[Q_{sig}=\frac{h_{10}}{G_{p}+h_{11}} \tag{10}\]  
Equation (10) shows that the signal quality \(Q_{sig}\) is computed using three dependable attributes: \(h_{10}\), \(G_{p}\), and \(h_{11}\). The attribute \(h_{10}\) represents the product of the transmittance power and channel gain, whereas the second attribute, \(G_{p}\), represents the Gaussian noise power. The third attribute, \(h_{11}\), is the product of the decision coefficient and channel gain. The _decision coefficient_ represents whether the mobile sensor \(s_{j}\) chooses to use the N2N mode of communication or the N2S mode of communication. The proposed scheme considers the _decision coefficient_ to be one in the case of N2N, or else it is zero in the case of N2S.  
Finally, the model computes the cumulative duration required for data transmission using Equation (11).  
\[dur=arg_{max}\{d\} \tag{11}\]  
In Equation (11), the cumulative duration \(dur\) is computed by exploring the maximum \(d=(d_{1},d_{2})\) score, where \(d_{1}\) and \(d_{2}\) represent the durations required for the N2N and N2S communication modes, respectively. Both \(d_{1}\) and \(d_{2}\) are computed using the respective parameters of the side of the data and transmission rate.
# A Performance-Consumption Balanced Scheme of Multi-Hop Quantum Networks for Teleportation
## 2 Preliminary
### Noisy Channel  
The main noisy channels in this paper are the bit-flip channel, phase-flip channel and depolarizing channel in [26]. The bit-flip channel flips a qubit from \(|1\rangle\) to \(|0\rangle\) with probability \(p\), \(0<p<1\). Its operation elements are:  
\[E_{0}=\sqrt{1-p}I,E_{1}=\sqrt{p}X. \tag{7}\]  
For the phase-flip channel, the operation elements are:  
\[E_{0}=\sqrt{1-p}I,E_{1}=\sqrt{p}Z. \tag{8}\]  
For the depolarizing channel, with error probability \(p\), the state can be described as  
\[\epsilon(\rho)=(1-p)\rho+\frac{p}{3}(X\rho X+Y\rho Y+Z\rho Z), \tag{9}\]  
where \(X\), \(Y\), and \(Z\) are Pauli operations.  
There are also other noise models, such as Gaussian noise, but they are hard to realize on the NISQ platform. So, we choose the three most commonly used quantum noise models for the channel environment simulation. Since purification results under bit-flip and phase  
Figure 3: (**a**) Entanglement swapping based on Bell pairs and (**b**) circuit in node B. \(|\Phi^{+}_{AB}\rangle\) is shared between nodes A and B. \(|\Phi^{+}_{BC}\rangle\) is shared between nodes B and C. Once two states in node B are measured, entanglement between nodes A and C is established.  
flip noise are similar, we only provide simulations under bit-flip and depolarizing noise in Section 4.
# Application of Riemannian Seismic Ray Path Tracing in Salt Dome Prospecting
## 5 Discussion
### Assessment of the Ray Tracing in the Previous Geophysical Setups
#### 5.1.3 Modelling of Irregular Interfaces between Strata  
Although the modelling of the interface between two homogeneous strata is ideal, the essence of the irregularities is fully captured by this example. As can be observed in Figure 3, a very slight difference in speed initial conditions of just one degree between adjacent emitted rays results in large differences in the reflected rays, which would be received in positions separated several kilometres at the surface. This is due to the nature of the irregularities introduced in the model at the separation of the strata, which presents different flanks in order to simulate real interfaces which are highly irregular.  
This example shows the great flexibility of this tool to model highly complex interfaces between geological structures. It probably remains the most difficult task to make realistic models of analytic speed models of underground in order to find their corresponding seismic ray pattern.  
Figure 6: Ricci scalar for the simplified model of the mantle as a function of depth.
## 4 Experiments and Results
### The Power Estimation of the Solar Cell  
This section estimates the solar cell's power source from Table 4 given an IoT device with a current consumption of 3.20 A over 40 times. Hence, it consumes 0.08 A per time. To run an IoT device that continuously consumes 0.08 A per hour for seven days, we would need a battery with a capacity of approximately 15.81 Ah (\(\approx\)20 Ah in the market). This calculation considers a total operational time of 168 h over the week and includes an efficiency factor of 0.85 to account for potential system inefficiencies.  
To ensure adequate energy production for an IoT device consuming 0.08 A per hour, a solar panel with a capacity of approximately 8.53 W (\(\approx\)10 W in the market) is recommended. This calculation is based on the device's daily energy consumption of 7.104 Wh, an average of 5 sunlight hours per day, a panel efficiency of 20%, and a 20% safety margin to account for inefficiencies and variability in sunlight.  
Figure 7: The average current consumption (A) for 40 rounds per interval.  
Thus, to sustain the operation of this IoT device for a full day using a 12 V battery, a step-down regulator, and a solar panel, a system incorporating a 12 Ah battery and a 10 W solar panel is required, considering 5 h of operational sunlight per day. These specifications may vary depending on the device's location, environmental conditions, and specific power consumption profile (Figure 8).
# Evaluating Machine Learning and Remote Sensing in Monitoring NO2 Emission of Power Plants
## 3 Materials and Methods
### Preprocessing & Post-Processing
#### 3.3.4 TROPOMI/EPA Value Pairs  
This section discusses combining TROPOMI and EPA data as value pairs associated with each power plant by each daily emission product for each day of the year. Iterating through each power plant in our dataset, an EPA NO\({}_{2}\) value was associated, based on the selected facility id and coordinates passed to the EPA (eGRID) dataset. If data existed for the plant and was not empty, a TROPOMI NO\({}_{2}\) value was associated with the pair by passing the coordinates of the power plant to TROPOMI images for clipping to obtain their values extracted from the raster object.  
Representing the data by pairs of our most impactful features simplified the reshaping and scaling performed in preprocessing before the ML models were fed the data. Obtaining TROPOMI values of the power plant image, the corresponding EPA data for the power plant, monthly averaged MERRA-2 features, and the date string before separation, a CSV file was generated that contained the six features.
# Deriving a Dilution of Precision Indicator for GNSS Factor Graph Optimization Solutions +
## 3 Results
### Validation of the FGO DOP Formula in Constrained Urban Case  
In this section, we study the case where the constellation geometry is changing in time due to the masking by building as a GNSS receiver is moving across a city. The considered trajectory comes from the Google Smartphone Decimeter Challenge 2021 [9], and trajectory \"2021-04-28-US-SJC-1/Pixel4\" has been chosen as it passes through an urban center. Figure 3 shows the 2D trajectory. Frequent satellite masking occur leading to frequent snapshot HDOP degradation.  
Figure 4 shows the exact HDOP and the HDOP computed using Equation (16) for \(K=1009\), i.e., all available epochs at 0.5 Hz, and various values of \(q/\sigma\). Again, the HDOP values obtained with the formula are close to the exact values. The largest difference corresponds to epochs where fewer satellites are available. At those epochs, sparse GNSS observations results in larger correlation between the east and north position estimation errors, and therefore, larger approximation error of the proposed formula.  
The FGO solution improves the accuracy of the solution, even in the epochs without enough satellites to compute a solution. For those epochs, the snapshot uncertainty has been clipped to a value of 100 m for each position state. Again, the temporal DOP variations are attenuated in the case of highly accurate motion uncertainties.  
Figure 3: Trajectory ”2021-04-28-US-SJC-1/Pixel4” from the GSDC2021 dataset.