esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theoretical Analysis of Cavity-Based WPT Systems
### Analysis of the WPT System's Sub-Models
As will be shown later, the main benefit of adding a director to the dipole antenna is not the increase in the coupling coefficient. Instead, the director changes the mode phases in a way that cannot be accomplished by changing the position of the dipole antenna, as phase delay resulting from different phase velocities of the modes cannot be compensated for by increasing or decreasing the wave travel path. Another essential factor characterizing the transmitting antenna behavior is the coupling coefficients' dependence on the feed line length. The coupling coefficients show how the generator power delivered by the TEM mode of the feed line is distributed among the main three waveguide modes (TE\({}_{01}\), TM\({}_{21}\), and TE\({}_{21}\)). The dipole sub-model is contained in an empty waveguide section terminated into a PEC wall at the rear end. The coupling coefficients for the three modes, along with the reflection coefficient of the TEM mode, are plotted against the feed line length in Figure 10b. It is seen that all three coupling coefficients vary slowly with the length of the feed line, the smallest variation being exhibited by the TE\({}_{21}\) mode. The other two modes demonstrate comparable variation with \(L_{\text{feed}}\). The strongest coupling is observed for the TE\({}_{01}\) mode, while the TM\({}_{21}\) mode has just a slightly smaller amplitude. The weakest coupling to the Tx antenna in terms of power is observed for the TE\({}_{21}\) mode, which is entirely in keeping with the above results.
## 2 The Proposed Framework
### Problem Description  
The overview of the proposed optimal scheduling for the MCMG coupled with P2G and CAES technologies aiming at minimizing daily cost and emission is depicted in Figure 2. The system operator purchases the electricity and natural gas from energy and gas markets under power market price variations. Then, P2G and CAES's operation set points along with DRP are determined to capture the uncertainty of the wind power and load demand, using the stochastic programming approach. Meanwhile, the operator simultaneously governs the tradeoff between economic and environmental targets [56]. Hence, the proposed structure is modeled as a multi-objective optimization problem and solved using weighted sum and fuzzy-based decision-making methods. Lastly, the model determines the optimal hourly purchased gas and power, optimal set points of CAES and P2G, wind spillage, and leverage DRP, as well as finding the optimal Pareto set pertained to minimum values of two conflicting objective functions. The mathematical problem formulation and methodology will be described later on.  
Figure 1: P2G schematic.
# On-the-Fly Olive Tree Counting Using a UAS and Cloud Services
## 3 Materials and Methods
### Data Processing
#### 3.2.2 Stereo Vision-Based Segmentation  
In every pair of consecutive aerial images, we have two different views of the same but displaced scene. Stereo vision consists of using these two views to obtain information about the distance of the objects in the scene to the cameras. The two views, after being projected on a common plane using the epipolar geometry, will have the same trees in the same horizontal line (see Figure 6a). Then, by comparing the distances between pixels corresponding to the same object, the depth information can be calculated using well-known algorithms [46]. The disparity map is a new generated image in which pixel values are inversely proportional to the depth of the scene. Hence, high value pixels belong to objects closer to the cameras. Analyzing these disparity maps will allow us to locate separate trees on them.  
Image rectification is the process of projecting two images of the same scene on a common plane. Two consecutive images are taken, and common keypoints are located by a feature-matching algorithm using SURF [47]. Based on the keypoints matching, homography matrices are calculated for both images. Semiglobal matching [48] was used to produce the disparity map from two rectified images. To obtain the information about separate trees from the disparity map, two more steps are required: separating trees from the ground and detecting trees in the resulting image. To separate trees from the ground, we tested the adaptive threshold and the median filter algorithms [49]. While they produced similar results, the adaptive threshold was much faster. To detect each separate tree, we used the contour detecting algorithm [50] (see Figure 6b).  
Figure 6: Images corresponding to the intermediate steps when applying the stereo vision-based segmentation algorithm to the image DJI_0225. (**a**) Rectified images using epipolar geometry (epipolar lines are shown in green). (**b**) Adaptive threshold on the disparity matrix. (**c**) Detected trees.  
The result of stereo vision-based segmentation can be seen in Figure 6c. Yellow crosses and red circles show the detected trees. The straight red lines delimit the overlapping area of this image with its peer image in the stereo pair. As expected, the part of the image with no overlap has no trees detected. This is because of the lack of stereo information for it. With the exception of the first image, these omissions will be recovered by processing the successive pair of images as explained in the next Section 3.2.3; thus, we do not consider them as a problem. On the contrary, when observing the overlapping area, we found more undetected trees. As shown in Figure 6b, the trees close to the borders are also not captured by the adaptive threshold. Other undetected trees are due to the implementation details of the process of rectification, which loses some parts of the image close to the corners.
# Mechanisms Underlying the Changes in Sporadic E Layers During Sudden Stratospheric Warming
## 2 Data and Methods  
### Data  
This study analyzed sporadic E layer data obtained from ionosondes located in Japan, accessible at [[https://wdc.nict.go.jp](https://wdc.nict.go.jp)]([https://wdc.nict.go.jp](https://wdc.nict.go.jp)) (accessed on 9 September 2024). The positions of the three instruments are depicted in Figure 1. Parameters recorded by the ionosondes include the critical frequency (foEs), blanketing frequency (fbEs), and virtual height (h'Es). Given that foEs is instrumental in reflecting the variability patterns of the sporadic E layer, this research primarily utilized foEs data. Additionally, the F10.7 index, Kp index, and reanalysis data pertaining to temperature and zonal wind were sourced from the National Oceanic and Atmospheric Administration (NOAA), available at [[https://www.noaa.gov](https://www.noaa.gov)]([https://www.noaa.gov](https://www.noaa.gov)) (accessed on 9 September 2024).  
To examine wind field variations at the altitudes corresponding to the sporadic E layers, data from the Thermosphere-Ionosphere-Mesosphere Energetics and Dynamics(TIMED) satellite were also employed. Launched in December 2001, TIMED orbits at an altitude of 625 km, with an inclination of 74.1 degrees, completing 15 orbits around Earth daily [25]. The satellite is designed to observe only one specific location at a time and can resume observation of the same site after a period of 60 days. The data utilized in this study were generated by the software \"VECTOR\" and encompass parameters such as latitude, longitude, altitude, wind speed, and data errors. Optimal data quality is attained when the altitude falls within the range of 92.5 to 115 km, which closely corresponds to the height of Eslayers; thus, these data were particularly well-suited for this research.
## 2 Materials and Methods
### Sentinel Satellites Imagery  
Besides Landsat 8 OLI imagery launched by National Aeronautics and Space Administration (NASA) in 2013, a pair of identical satellite (Sentinel-2A and -2B) was launched in 2015 and 2017, respectively by ESA's European Space Operations Centre (ESOC) Darmstadt, Germany under same orbit in Wide Swath mode providing greater extent in distribution of vegetation over the coastal region [29]. The cloud free Sentinel-2 imagery with 13 acquired spectral bands was rectified in terms of radiometric and atmospheric correction. In addition to the temporal resolution, with a spatial resolution of 10 m, Sentinel-2 products are appropriate as they enable us to map mangrove vegetation at a scale of 1:20,000 [43]. This study proposed Sentinel-2 imagery for the characterization of mangrove coverage at district level.
# Reliable Positioning and mmWave Communication via Multi-Point Connectivity +
## 5 Conclusions  
In this paper, we explore the viability of using coordinated multi-point reception to provide better positioning accuracy and higher data-rate in harsh channel conditions. Our proposed approach is scalable to arbitrary mmWave based multi-point configurations and dense deployments. For the evaluation of localization accuracy, we adopted CRLB and provided its dependence of different parameters using the closed-form expressions. It was shown that coordinated multi-point reception provides better positioning quality. By numerical simulations, we verified our analysis and observed that joint reception not only provides high user rate but also stable connectivity and superior localization performance.  
For the future work, interest is to consider multi-point sub-set selection and users assignment problem for more reliable and robust joint positioning and communication with fewer BSs.  
[PERSON] and [PERSON] devised the work and the main conceptual ideas. [PERSON], [PERSON] and [PERSON] designed the methodology of communication part, and [PERSON] and [PERSON] developed the positioning part. [PERSON] performed the simulation tests and results were analyzed by all authors. The paper was mainly written by [PERSON], [PERSON] and [PERSON], with the help of [PERSON] and [PERSON] on the revision of the manuscript.  
The research leading to these results received funding from the Academy of Finland projects: Positioning-aided Reliably-connected Industrial Systems with Mobile mmWave Access (PRISMA) and supported by Academy of Finland 6 Genesis Flagship (grant 318927).  
The authors declare no conflict of interest.
# Secure Quantum Communication Technologies and Systems: From Labs to Markets
## 3 Devices for QKD Systems
### Photon Sources  
Solid-state, single-photon sources are reviewed in [67]. Ideally, telecommunication-relevant photon sources should work at room temperature, emit information at commonly used wavelengths (850, 1300, or 1500 nm), and be easily integrable with other devices (e.g., silicon photonics modulators) at the cheapest cost. Electrically driven photon emission systems appear to be more suitable for telecommunication, as they are more compact and controllable on chips. Both single photons and entangled photons pairs are suitable, depending on the system requirements and protocol. Room temperature III-V semiconductor lasers integrated on silicon are a short-term viable option. Their main merit is their readiness, but the production process may be expensive for large-scale production.  
To realize entangled photon sources at 810 nm or 1550 nm, one may opt for silicon nitride or silicon photonic integrated circuits, respectively, where ring resonators are used for entangled photon-pair emission based on spontaneous four-wave mixing [68].  
An integrated weak coherent transmitter at 1550 nm was proposed in [69], based on a monolithically fabricated Indium Phosphide (InP) device comprised of a wavelength-tunable laser, an electro-optic phase modulator (EOPM), and a photodiode. The EOPM is used in multiple interferometers to create intensity modulated and phase-encoded weak coherent signals for multiple protocol QKD.  
At 850 nm, the Micius satellite [70] implements space-to-ground BB84 QKD using eight fiber-based laser diodes, of which four are used for signal states and four for decoy states.  
In the future, two dimensional materials deposited on silicon may provide true single-photon emissions at room temperature [71].  
Regarding weak coherent sources of photons at 1550 nm, III-V lasers, used for single-photon sources or to pump Si microrings for the generation of entangled photons on chips, may be replaced in the future with full group IV photon sources, such as germanium microcavities [72] or Er in silicon or silicon oxide chipsets [73; 74], including coupling with resonant rings or cavities.  
Single photons can also be generated by defects in materials, such as nitrogen vacancy (NV) centers in diamonds [75] and silicon vacancy (SiV) centers [76].  
At 810 nm, one may in the future explore colloidal quantum dots, similar to those used for commercial screens, and 2D materials combined with photonic cavities in silicon nitride.
# On the Feasibility of Wireless Multimedia Sensor Networks over IEEE 802.15.5 Mesh Topologies
## 5 Performance Evaluation  
The simulation results for every scenario (Figure 7) are presented below. Different metrics of interest have been plotted, including latency, jitter, throughput, message delivery ratio, power consumption/network lifetime and PSNR. These metrics are commonly assessed in other studies related to multimedia traffic or WSNs. It should be noted that, for all scenarios, power consumption and lifetime were computed for node 1, as will be explained later in the text. To conclude this section, a scalability study is also included. It is intended to show the impact of the aforementioned metrics in networks composed of a large number of nodes and different node densities.
## 4 Experimental Results and Analysis  
### Modeling of Initial Health Assessment Model Based on MSET  
#### 4.1.1 Brief Description of Case Western Reserve University Bearing Data Set  
The validity of the health assessment method proposed in this paper is verified by using the bearing dataset from the Case Western Reserve University in the United States [27]. The vibration test bench of Case Western Reserve University mainly consists of a 1.5 kW motor, a torque sensor/decoder, a power tester, and an electronic controller. The object to be tested is the motor bearing, which is divided into the drive end bearing and the fan end bearing. In this paper, the rolling bearing at the drive end is selected as the object for health assessment. The sampling frequency is 12 kHz, and the sampling duration for each working condition is about 10 s. Each working condition sample contains 120,000 data points. The fault location is the outer ring of the bearing, and the fault type is a single-point damage caused by an electric spark. The normal base vibration acceleration data operating at four speeds are input into MSET to construct the historical memory matrix to characterize the normal operating state of the bearing. The specific fault severity is shown in Table 17, and the speed conditions of the bearing operation are shown in Table 17.  
Based on the fault degree and speed condition of the driving end rolling bearing, 12 kinds of vibration acceleration fault data can be obtained. Under each working condition, 120,000 data points are rounded up, and the vibration acceleration peak, kurtosis, and root mean square (RMS) values are computed for every 2000-sample segment of the acquired data. The steps of data application are as follows: Firstly, the peak value, kurtosis and root-mean-square (RMS) values of normal base data under four speed conditions are extracted to form an observation vector, input into MSET to build the memory matrix, and then the fault data are divided into three groups for verification according to the algorithm verification requirements, which are as follows: data with different fault degrees at the same speed, data in the same fault degree at different speeds, and data with different fault degrees at different speeds.
# Heart Failure Prediction Based on Bootstrap Sampling and Weighted Fusion LightGBM Model
## 3 Mathematical Model
### Parameter Optimization of Model
#### 3.4.1 The Optimization Thought  
The parameter optimization strategy adopted in this paper is to explore the hyperparametric space and evaluate the performance based on cross-validation. A multi-dimensional hyperparameter space is formed by defining different value ranges of multiple hyperparameters, such as learning rate, number of leaf nodes, sample weight of leaf nodes, data sampling ratio, and bootstrap sampling times. The multi-layer nested loop structure is used to iteratively explore all possible combinations within the hyperparameter space. The purpose is to explore the influence of different hyperparameter values on the model performance, without leaving any unchecked potential excellent combination, so as to determine the best parameter configuration for the given data set and task.  
KFold cross-validation and 50% cross-validation are used to evaluate the performance of the model under each hyperparameter combination. This concept is based on the understanding that the data set is divided into several subsets, and the performance of the model in different data distributions can be understood more comprehensively and objectively by training and verifying the models of these different subsets. By doing this, it helps to prevent overfitting the evaluation data, thus producing more reliable performance evaluation results. In each cross-validation segmentation process, the complete cycle of model training, prediction, and evaluation index calculation is performed for each hyperparameter combination. Then, the advantages and disadvantages of each hyperparameter combination are determined by comprehensively analyzing the results from multiple folds.
## 2 Theoretical Background
### Objective Functions
#### 2.1.7 Regularization in Model Updating Using Evolutionary Algorithm (EA)  
Regularization techniques in the context of model updating have been conventionally applied by various researchers using SM as explained in the literature review. However, in the context of this paper, where EA has been used to better explore the search domain, a regularization technique was combined with EA. Regularization usually attempts to control the parameter variations. In this research, the parameter values with respect to the original model are kept small during subsequent iterations. In other words, the physical assumption of the original model has been considered [37] such that the parameter values remain close to the original FE model. Therefore, the minimization term can be modified to give preference to a solution as:  
\[\prod_{\text{damaged.reg}}=\prod_{\text{damaged}}\;+\tau\|x-x_{o}\| \tag{15}\]  
where \(x\) refers to the parameter values in the current iteration and \(x_{o}\) refers to the initial parameter values. \(\tau\) is the tradeoff parameter between the two parts of the objective function. For a small \(\tau\), the updated parameters are almost unrestricted, and the solution resembles the original ill-posed problem. However, for a large value, the updating parameters remain limited in size and may have larger errors in the fitting of the data.
# Towards Cognitive Recommender Systems
## 4 Experimental Settings and Analysis
### Users' Personality Acquisition  
Personality as \"consistent behavior pattern and interpersonal processes originating within the individual\" [110], which can be detected either explicitly by filling a questionnaire or implicitly through observing users' behavioral patterns. According to psychologists, personality can introduce a person's \"patterns of thought, emotion, and behavior\" [111]. Among all traits, we select Five Factor Model (FFM) which is a well-known personality model. FFM can describe people personality into five main factors as follows [112]:  
* \"Openness to Experience: creative, open-minded, curious, reflective, and not conventional\".
* \"Agreeableness: cooperative, trusting, generous, helpful, nurturing, not aggressive or cold\".
* \"Extroversion: assertive, amicable, outgoing, sociable, active, not reserved or shy\".
* \"Conscientiousness: preserving, organized, and responsible\".
* \"Neuroticism (Emotional Stability): relaxed, self-confident, not moody, easily upset, or easily stressed\".  
In this experiment, we detect users' personality types implicitly through analyzing their provided textual contents. It is shown by psychologists that people with similar personality types tend to share similar interests. If we take movies domain as an example, due to its strong relations with users' preferences, the Openness personality type users may prefer to watch comedy and fantasy genre of movies, while Neurotic personality type users are more likely to watch romantic movies [113]. Therefore, we select a movie-related subset of the Amazon dataset to test our model. This dataset contains a huge amount of reviews that mining them can help us to better understand users' behaviours. Since people may differ in their selected words because of their personality characteristics, we first collect all users' reviews and then feed them to Linguistic Inquiry and Word Count (LIWC), which is a popular text-analysis tool. LIWC can categorize the words related to more than 88 psychological. LIWC is able to find the percentages of words related to each of which 88 categories like positive emotions, cognitive process, and social processes. Next, based on the relation of each of extracted category from LIWC and FFM personality traits, we employ a linear regression model to compute the score of each user's personality type, named PerSc, as follows:  
\[PerSc=\alpha A+\beta B+\gamma C+..., \tag{1}\]  
where in Equation (1), \(A\), \(B\), and \(C\) are the LIWC categories, and \(\alpha\), \(\beta\), and \(\gamma\) are their corresponding weights.