esa-sceva/satcom-chunk-collection huggingface dataset:
# A Capacity Enhancement Method for Frequency-Hopping Anti-Jamming Communication Systems
## 3 Results
### BER Performance Analysis  
#### 3.2.1 Information A BER Analysis  
Without using channel coding, an information rate of 1 kbps for the Information A signal is generated; this signal is mapped using the BPSK modulation method and then subjected to eightfold sampling square-root ascending cosine-forming filtering to form the Information A baseband signal. The Information A baseband signal and the frequency-hopping carrier signal are multiplied to form the FH capacity boost signal. The FH carrier signal is created using Information B by selecting different frequency carriers according to the keying rule. The simulation specific parameters are shown in Table 4.  
After passing through a Gaussian white noise channel, Information A is demodulated and then adjudicated bit by bit. After that, the BER is calculated.  
Since the demodulation performance of Information A depends on Information B for carrier reconstruction, the BER of Information B influences the carrier reconstruction effect, which in turn affects the demodulation performance of Information A.  
The BER performance of Information A is simulated under seven cases of J values such as 1, 2, 4, 8, 16, 32, and 32 encoded, as well as coding with different Eb/N0 conditions for Information A. The simulation results are shown in Figure 15. To simplify the simulation algorithm, eight frequency-hopping points are selected, and to ensure signal orthogonality in the Information B code element cycle, the same frequency-hopping points are used in each Information B code element for J values of 8, 16, and 32.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter Name** & **Parameter Value** \\ \hline Information A modulation & BPSK \\ Information A information rate & 1 kbps \\ Information A channel coding & unencoded \\ Carrier hopping speed & 1k hops/s \\ Number of carrier hopping points & 8 \\ Value of J & 1, 2, 4, 8, 16, 32 \\ \hline \hline \end{tabular}
\end{table}
Table 4: BER performance simulation parameters.  
Figure 14: Variation in carrier recovery quality with J for different signal-to-noise ratios.  
As shown in Figure 15, the BER curve of Information A gradually converges to the BPSK theoretical value as the value of J increases. At J = 32 plus coding, the theoretical value of Information A is consistent when the BER of Information A at an order of \(-2\) magnitude (the operating point of Information A after adding channel coding).
# Home Automation System Based on Intelligent Transducer Enablers
## 1 Introduction
This paper presents the design and implementation of a Home Automation System based on Intelligent Transducer Enablers (HASITE), whose transducers are automatically configured and added to the sensor network. HASITE is capable of controlling and automating house appliances through an easily-manageable web interface and presents the following features:  
* The system is able to interact with each transducer of a home automation network through a scalable wireless or wired infrastructure. * Transducers can be added to the network in a simple way. Each device is automatically registered by indicating its parameters (i.e., type, number of inputs/outputs, message format, measurement units and refresh rate). * The system has a simple Application Programming Interface (API) to access basic configuration parameters and the input/output of each transducer. Moreover, the access to the API can be performed ubiquitously through the Internet. * The system has a central server that manages the sensor network and that is responsible for granting access to the API. Thanks to the protocols implemented, the creation of new nodes and the addition of those to the system only requires the software programming of the transducers. The processes involved in obtaining the final network configuration, in detecting new nodes and in controlling such nodes are performed automatically. Therefore, any end user is able to add a new node quickly and easily by just powering it up. The remainder of this paper is organized as follows. Section 2 reviews the state of the art of wireless home automation communication technologies and briefly describes recent home automation systems. Section 3 introduces the most relevant plug-and-play mechanisms for transducer networks. Section 4 presents an overview of HASITE's architecture. Section 5 details the core of HASITE: the intelligent transducer enablers and the plug-and-play protocol created to detect and configure the transducer nodes. Section 6 describes the API along with a usage example. Section 7 details the hardware used to implement the different subsystems, including the transducer nodes. Section 8 presents results of different tests developed to evaluate the performance of HASITE. Finally, Section 9 is devoted to the conclusions.
# Anti-Sexism Alert System: Identification of Sexist Comments on Social Media Using AI Techniques
## 5 Phase 2: Model Definition and Training Process  
We decided to use an NLP/ML model to build the the _anti-sexism alert_ system, based on a supervised learning approach. Instead of creating an NLP/ML model from scratch, we  
Figure 3: Characteristics of the training data set.  
decided to apply a transfer learning approach and use a pre-trained model, obtained from the Hugging Face model repository: the Hate-speech-CNERG/dehatebert-mono-spanish, which was pre-trained to detect hate speech in Spanish [6]. This technique, using a pre-trained model, provides good results, and it is especially interesting in our case, because we do not have a massive tagged data set (in Spanish). So it allowed us to obtain a good performance, since there are strong similarities between the language structures of hate speech and offensive sexist comments. Therefore, using this model as a base, we proceeded to perform a second training (or fine-tuning) of its parameters, using our training set of labeled comments.  
The training process was performed using the manually labeled comments (4389). The majority (86.44%) were manually labeled as \"Yes (sexist comment)\" or \"No (sexist comment)\", whereas 8.48% were labeled as \"Depends on the context\", and the last 5.08% was labeled as \"Discard\". The training process, performed using only the comments labeled as \"Yes/No\", was performed with a total of 3794 comments. They were split into two sub-sets: a training set (80% of the tagged data) to train the NLP/ML model, and a test set (20% of the tagged data), to evaluate the model. These percentages reveal a scenario of imbalance between labeled classes. To deal with this issue, on the one hand, we used an NLP model pre-trained on a context such as hate speech, which also naturally presents imbalanced classes. On the other hand, when retraining and fitting this model with our database, we used an approach based on penalized models, so that class weights were taken into account.  
Once the model was trained, we used the test data set to check the performance of the _anti-sexism alert_ system. For this evaluation, we used metrics described in Table 1, where \(TP\) stands for true positives (sexist comments correctly classified), \(FP\) means false positives (no sexist comments incorrectly classified), \(TN\) stands for true negatives (no sexist comments correctly classified), and \(FN\) means false negatives (sexist comments incorrectly classified). Consequently, accuracy represents the total percentage of cases that were correctly classified (identified) by the _anti-sexism alert_ system. Precision, measures the proportion of cases that, having been classified as positive, actually were positive. Recall, assesses the proportion of actually positive cases that were correctly classified, and \(F_{1}score\) corresponds to the harmonic mean of precision and recall.
## 4 Discussion  
This paper discusses the WPT system with the MMs using a Class-E RF power amplifier. It elaborates that the system transfer efficiency remains high in relatively long distances. Research of the WPT system and the MMs have been analyzed separately in recent years. Most studies have explained that the MMs are applied in the WPT system based on the physical mechanisms and simulated analysis, such as the microwave theory and transmission line theory. To explore an effective method to combine the WPT system with the MM is the focus of future research, such as the equivalent circuit model. This method can help to analyze the inner relation between the WPT system and the MMs.
# Stochastic Modeling and Optimal Time-Frequency Estimation of Task-Related HRV
## 3 Results and Discussion
### Regression Analysis
#### 3.3.2 Parameter \(d\)  
The significant covariates in simple regression models for the parameter \(d\) are the same as for \(m\), a fact that relates to the correlation between the two chirp parameters. None of the variables is still significant in multivariate models including the variable Age, therefore the selected model for \(d\) includes only the variable Age, with \(R^{2}_{\rm adj}=0.59\) (Table 4).  
For both \(m\) and \(d\), even more explanatory than the numerical variable Age is the categorical variable Age Groups, that alone achieves \(R^{2}_{\rm adj}=0.79\) and \(R^{2}_{\rm adj}=0.73\) for \(m\) and \(d\) respectively. According to these models, being over the age 40 is what matters in affecting the value of the chirp parameters. However, only 10 TPs are older than 40 years, limiting the reliability of the last result.  
To understand the meaning of the chirp parameters, a person older than 40 years would typically have the chirp parameter \(m\) approximately the double of the average values (i.e., \(m\approx 0.0006\)) and the parameter \(d\), representing the chirp delay, about half of the average value (i.e., \(d\approx-200\)). These values correspond to an angular coefficient of the estimated linear chirp twice as large of the typical one, as a result of a steeper instantaneous frequency increase of the respiratory signal and consequently of the HRV.
## 6 Discussion, Implications, and Future Work
Moreover, we utilised a conventional machine learning algorithm as the exemplar to demonstrate the feasibility of this approach. The clustering algorithm can be replaced by deep learning models under different application scenarios to improve the performance of the proposed approach, for example, Convolutional neural networks (CNN) for ECG image data [21; 43]; Long short-term memory (LSTM), as smartwatch data is time-series data [6]; Multilayer Extreme Learning Machines (ML-ELMs) for accelerated deep learning development [44]; and LSTM autoencoders to reduce the noise in data. In addition, integrating smartwatch data into EMR presents significant infrastructure challenges, including secured and scalable data storage, standards and protocols for ensuring interoperability, connectivity and resource allocation, ethical and legal frameworks that protect patient privacy, and ensuring data ownership. Addressing these infrastructure challenges requires a multidisciplinary approach that involves healthcare providers, technology providers, researchers, policymakers, and patients. Future research can help address these challenges by developing innovative solutions that are scalable, interoperable, secure, and patient-centred. Finally, future research can also focus on developing reimbursement models that recognize the clinical value of using smartwatch data and support the reimbursement of healthcare providers for the time and resources required to use smartwatch data reports.
# An Improved Spider Wasp Optimizer for Green Vehicle Route Planning in Flower Collection
## 2 Literature Review
[21] considered logistics costs in the cold chain distribution of fresh agricultural products from two aspects: carbon tax and preservation investment. They developed a green vehicle routing optimization model for fresh agricultural products aimed at minimizing total costs, which was solved using an improved ant colony algorithm. [PERSON] et al. [22] developed a long-distance cold chain logistics route optimization model that incorporates the damage costs due to distribution vibration and the carbon emissions costs from refrigerants. They proposed an improved ant colony algorithm to solve this model. Compared with previous studies, they paid more attention to the details of the actual transportation process. [PERSON] et al. [23] investigated the optimization model for pharmaceutical cold chain logistics. They improved the Grey Wolf Optimizer by incorporating large neighborhood search (LNS) and utilized a greedy algorithm along with enhanced nonlinear factors to improve convergence speed. Although the study primarily focused on pharmaceutical cold chain distribution, its optimization methods provided a valuable reference for cold chain vehicle routing planning. Many scholars have conducted in-depth research on cold chain vehicle routing problems, but their focuses differ. Current studies primarily concentrate on vehicle routing for fresh produce distribution under soft time window constraints, with less attention given to vehicle routing for flower collection under hard time window constraints. Some studies consider only one or more of the following factors: customer satisfaction, transportation costs, and carbon emissions costs. It is observed that most research employs improved genetic algorithms or ant colony optimization algorithms to solve these problems. However, the Spider Wasp Optimizer (SWO) has been proven suitable for solving complex optimization problems, and few studies have applied the SWO algorithm to VRP problems. Therefore, this paper integrates existing optimization approaches and targets practical collection scenarios to propose a more comprehensive and efficient low-carbon cold chain routing planning model for flower collection. Furthermore, this paper also designs an improved SWO algorithm to address low-carbon cold chain routing planning problems.
## 1 Introduction
The traditional optimization design is usually performed without considering the uncertainty of input variables, which may lead to failure of the optimization results in practical application. Therefore, reliability optimization has attracted much attention recently. [PERSON] et al. [18] proposed a reliability-based multi-objective optimization design method to optimize the engine mount. They obtained a more reliable optimization solution than the deterministic optimization based on the NSGA algorithm. [PERSON] et al. [19] presented a new method for hybrid reliability-based design optimization under random and interval uncertainties, and used a ten-bar truss example and a design of piezoelectric energy harvester to verify the accuracy and effectiveness of the method. Combined with the response surface method and a multi-island genetic algorithm, [PERSON] et al. [20] applied an efficient reliability-based design optimization process to optimize the reliability of lattice boom. The results showed that it not only met the reliability requirements, but also ensured safety and economy. In this paper, a multi-objective reliability optimization method by integrating MCS with NSGA-II algorithm coupled with entropy weighted GRA is proposed, and the effectiveness of this method is verified by the lightweight optimization of the lower control arm of Macpherson suspension. Firstly, the finite element model of the control arm is established and the modal and stiffness analysis are carried out. Then, the inertial release method is used to calculate the stress distribution of the control arm under unit load. The load time histories of the control arm are extracted by dynamic simulations of the rigid-flexible coupling vehicle model. Accordingly, the nominal stress method is used to calculate the fatigue life of the control arm. On this basis, six design variables are defined for describing geometric dimension of control arm based on mesh morphing technology. The optimal Latin hypercube method is applied to generate the sample points for constructing Kriging surrogate models, which are used to describe the relationships between design variables of control arm and its structure performance including mass, fatigue life, stiffness, and mode. The multi-objective reliability optimization of the control arm is further performed by combining the NSGA-II algorithm and the Monte Carlo simulations. Finally, the entropy weighted grey relational analysis is adopted to determine the optimal solution from Pareto set for realizing lightweight design of the control arm.
## 3 Results and Discussions
### Color Separation  
According to the fact that a red color was induced by irradiation of PAISiN, it was presumed that the red component was less sensitive to radiation than other color components. Actually, it was reported that a higher-degree saponification of PVA lowered the absorbance of the red color (around 490 nm) [35; 36], which implies that the red component of a PAISiN image is attributable to the formation of the residual acetyl group of polyvinyl acetate in PVA. With this consideration, the inverted images were split into three color components: blue, green and red; then, the dose responses of each component were examined using the same data of Figure 2. As seen in the results shown in Figure 4, it was confirmed that the intensities of the red components were notably lower than those of the blue and green components. While the blue component showed the highest intensity level, the variation at 20 Gy was found to be quite large. Based on these findings, it was decided to use the green component for further analyses in this study.
# Feasibility, Design, and Deployment Requirements of TCR for Bistatic SAR radiometric Calibration
## 2 Requirements of Calibration Target for Radiometric Calibration
### The Beamwidth Requirement  
For an antenna or reflector pattern, the beamwidth is a physical quantity that characterizes the angle range between certain power points (generally half power or so-called 3 dB) of the main lobe, when referenced to the peak effective radiated power of the main lobe. The wider the 3-dB beamwidth of a calibration target, the easier it is to align to the SAR antenna. The beamwidth requirement is that the beamwidth of the calibration target should be larger than the SAR antenna beamwidth, especially the azimuth beamwidth of the calibration target. Using the Sentinel-1 SAR calibration as an example, the first criterion can be expressed mathematically as follows [18]:  
\[BW_{cal}>\phi_{a}+\Delta\phi_{y}=BW_{SAR} \tag{1}\]  
where \(BW_{SAR}\) is the beamwidth of the Sentinel-1 SAR antenna given by the sum of the SAR antenna azimuth beamwidth \(\phi_{a}\) and the yaw attitude stability of antenna \(\Delta\phi_{y}\); and \(BW_{cal}\) is the monostatic azimuth beamwidth of the calibration target, which should be larger with respect to the \(BW_{SAR}\). Obviously, Equation (1) is still valid for a bistatic situation, when \(BW_{cal}\) stands for the bistatic azimuth beamwidth of the calibration targets.  
In fact, the TCR has a wide monostatic pattern (TCR RCS versus incident angle), but a very narrow bistatic pattern (TCR RCS versus bistatic angle) that is different from the pattern we usually define [8], and the 3-dB bistatic beamwidth can be written as:  
\[\phi\approx\frac{14.5^{\circ}}{a[\mathrm{m}]\cdot f[\mathrm{GHz}]} \tag{2}\]  
where \(a\) is the length of the hypotenuse sides of the TCR, and \(f\) is the frequency. The 3-dB bistatic beamwidth is less than \(4^{\circ}\) for a 0.4-m TCR at X-band. It should be noted that the beamwidth and bistatic pattern discussed in this paper used the incident angle as an independent variable, and not the bistatic angle (angle between the incident and backscattered wave), if not specified.
# The Microwave Temperature and Humidity Profiler: Description and Preliminary Results
## 2 Instrument Description  
MTHP is the second version of the Microwave Temperature Profiler [15; 16] (MTP). The instrument was conceived in late 2013 to add water vapor information (183 GHz channels) to the MTP [15; 16; 17]. Both MTP and MTHP are atmospheric spectroradiometers at millimeter wave frequencies and are conceived to be integrated into airborne campaigns. In the case of MTHP, it was deployed in the NCAR-led TI3 GER campaign on board a HIAPER Gulfstream-V (GV) airplane. For the 2022 TI3 GER campaign, MTHP underwent a major refurbishment, which included an overall update and the substitution of the 60 GHz backend for a hyperspectral detector.  
The instrument is divided into two parts. The first part is the MTHP canister, which includes all the necessary components to perform the radiometric measurements. The scanning servo motor and controller are configured to start automatically upon power on of the 28 V line, which is activated with the airplane take off. This entire module is mounted below the airplane wing. The second part is the instrument control, which is a computer together with a power distribution unit that is installed in a rack inside the airplane cabin.  
The high-level block diagram of MTHP is shown in Figure 1. As can be seen, both receivers are completely independent to prevent failures in one frequency band from affecting the other. The control and telemetry units are also independent. Both channel command and data handling (CDH) units are Linux-based computers that are connected through an Ethernet (i.e., IP) switch to the main control computer on board the cabin. In the next sub-sections, the instrument design is detailed for the Antenna Cylinder Assembly, the 60 GHz receiver, the 183 GHz receiver, and the control computer.
# High Performance Computing in Satellite SAR Interferometry: A Critical Perspective
## 4 High Performance Computing: Fundamentals Concepts and Models
### Performance Metrics  
For a given number of computing elements, the _speedup_ factor of a parallel program is defined by the ratio of the sequential execution time and the parallel execution time [154; 158; 159; 160]. As not all the parts of a given problem can be parallelized, _Amdahl_'s law can be used to estimate the improvement efficiency due to parallel computing. It states that the potential theoretical speedup is related to the fraction of code that can be parallelized (parallel portion). According to the graph sketched in Figure 8, the speedup as a function of the number of processing elements is depicted for different values of the parallel portion (50%, 80%, 90%, 95%, and 97%). This law theoretically predicts the achievable speedup; however, more refined performance models also exist, thus incorporating additional parallel inefficiencies [154; 155; 156; 158; 159; 160; 162; 164; 165].  
Finally, it is worth stressing that the _scalability_ analysis is also an important issue, which is aimed at investigating how actual performance changes when the number of used processing elements (or the size of the problem) increases.