esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of Radio Communication Links of 4G Systems
## 2 Background
### Study and Simulation of the Radio Links of a 4G System  
The radio link quality of 4G LTE systems depends on a set of parameters such as propagation model, variation of the distance between transmitter and receiver, the height of the eNB, power supply, and others. This part presents the simulation results obtained by varying these different parameters and their influences on the radio communication links. In this study, we are interested in a Maximum Path Loss (MAPL). From the MAPL can calculate the radius of the cell to be discovered, we have already given an example of the calculation of coverage radius in the downlink interface. In this example, we have chosen the propagation model Cost 231 Hata, which estimates the total number of sites to avoid a communication cut.
## 2 Mathematical Models  
This section first presents the single-index model, then examines parametric optimization methods and DEA, before formally introducing the proposed model. Some assumptions are as follows:  
1. \(\chi\) is a set of existing investment opportunities.
2. \(\Re_{i}\) is the return of \(i\)-th asset on the probability space \((\Omega,F,P)\).
3. \(\chi^{P}=\{\Re_{i}:\;i=1,...,n\}=\{\sum_{i=1}^{n}\Re_{i}\varepsilon_{i}:\; \sum_{i=1}^{n}\varepsilon_{i}=1,\;\varepsilon_{i}\in\{0,1\}\}\). \(\varepsilon_{i}\) is the weight of each stock in the optimal portfolio.  
**Definition 1**.: _Branda [20] Functionals \(v:L^{2}(\Omega)\to[0,\infty]\) are designated as general deviation measures predicated upon their satisfaction of the following axiomatic properties: translation invariance, positive homogeneity, subadditivity, and non-negativity. These criteria are formally articulated as follows:_  
1. \(\forall\;Y\in\chi,\forall\;C\in\mathbb{R},v(Y+C)=v(Y)\)_._
2. \(\
u(0)=0\)_, and_ \(\forall\;Y\in\chi,\forall\;\lambda>0,\
u(\lambda Y)=\lambda\
u(Y)\)_._
3. \(\forall\;Y_{1},Y_{2}\in\chi,\
u(Y_{1}+Y_{2})\leq\
u(Y_{1})+\
u(Y_{2})\)_._
4. \(\forall\;Y\in\chi,\
u(Y)\geq 0\)_, with_ \(\
u(Y)>0\) _for non-constant_ \(Y\)_._  
_It is pertinent to note that the axioms (2) and (3) jointly imply the convexity of such deviation measures. Prominent instantiations of this class include the standard deviation, the mean absolute deviation, and semideviations._  
**Definition 2**.: _Branda [20] Functionals \(\varphi:L^{2}(\Omega)\to(-\infty,\infty]\) are formally designated as return measures if and only if they satisfy a specific set of axiomatic properties: translation equivariance,positive homogeneity, superadditivity, and monotonicity. These conditions are rigorously defined as follows:  
\[\begin{array}{ll}1.&\forall\ Y\in\chi,\forall\ C\in\mathbb{R},\varphi(Y+C)= \varphi(Y)+C.\\ 2.&\varphi(0)=0,and\ \forall\ Y\in\chi,\forall\lambda>0,\varphi(\lambda Y)=\lambda \varphi(Y).\\ 3.&\forall\ Y_{1},Y_{2}\in\chi,\varphi(Y_{1}+Y_{2})\geq\varphi(Y_{1})+\varphi( Y_{2}).\\ 4.&\forall\ Y_{1},Y_{2}\in\chi,\,if\ Y_{1}\geq Y_{2}\ (\ in\ the\ pointwise sense), then\ \varphi(Y_{1})\geq\varphi(Y_{2}).\\ \end{array}\]  
It is pertinent to observe that axioms (2) and (3) jointly imply the concavity of the functional \(\varphi\). The expectation operator is a fundamental example that demonstrably fulfills these axiomatic requirements. Furthermore, coherent risk measures, when subjected to scalar multiplication by a negative constant can be interpreted within the framework of return functionals.
## 3 CFD Simulation Results
### In-Cylinder Flow Turbulence  
The TKE is typically used to examine the in-cylinder flow turbulence of the ICEs. In fluid dynamics, TKE is the mean kinetic energy calculated for one unit of mass in turbulent flows. Based on the Reynolds and Navier-Stokes equation expressions, TKE can be computed by CFD turbulence models. Physically, TKE is half of the sum of component variances of turbulent velocities, as presented in Formula (1) [52].  
\[k=\frac{1}{2}\Big{(}\overline{\left(u\right)^{2}}+\overline{\left(v\right)^{2 }}+\overline{\left(\omega t\right)^{2}}\Big{)} \tag{1}\]  
Figure 5: Comparisons of simulation results and shop test data.  
In Formula (1), \(k\), \(u\prime\), \(v\prime\), and \(\omega\prime\), respectively, are the TKE and the velocity components, according to the horizontal directions (\(x\)- and \(y\)-axis) and vertical direction (\(z\)-axis) of turbulent flows. As can be seen, TKE is proportional to the velocity components squared. In another word, it indicates the turbulence level of the in-cylinder flow of the engine. Higher TKEs imply higher fluid flow turbulence levels and vice versa.  
The mean TKEs of fluid flow inside the engine cylinder for the three piston bowl types are presented in Figure 6. The computed results pointed out that the TKE of the flow in the engine cylinder when using the re-entrant and \(\omega\)-type piston bowls was higher than that of the U-type bowl, and the re-entrant type of bowl produced the highest TKE. This means that the re-entrant piston bowl produced a higher flow turbulence intensity compared to the \(\omega\)-type and originally designed U-type bowls. This is because the inclined surfaces along with the circular corners on the piston surfaces of the \(\omega\)-type and re-entrant piston bowls created more vortices (turbulence) inside the engine combustion chamber than in the originally designed U-type piston bowl when the fuel was directly injected into the engine cylinders. The increase in TKE enhances the mixing quality between the injected fuel and in-cylinder air, which improves the combustion quality and thus engine performance [17; 18; 19; 20; 22; 25; 26; 28; 30].
# Influence of Fatigue and Defensive Pressure on Three-Point Jump-Shot Kinematics in Basketball
## 4 Discussion
### Impact of Fatigue on Shooting Performance and Kinematic Adjustments  
Fatigue had a pronounced effect on several kinematic parameters. Notably, the height of the jump increased in the fatigued conditions (CON3 and CON4), which contrasts with the expectation that fatigue would reduce physical output. This increase might be attributed to compensatory strategies employed by the players, who likely adjusted their mechanics to overcome the fatigue-induced decline in lower limb power. This adaptation is consistent with  
Figure 2: The shooting performance among each condition.  
previous research suggesting that athletes may alter their movement patterns under fatigue to maintain performance [32; 33]. While fatigue generally reduces physical performance, the combination of adrenaline, increased focus, psychological pressure, and muscle compensation often leads players to jump higher in critical moments during a shot, especially when facing defensive pressure (CON4). Furthermore, when tired, players may activate core and upper body muscles more intensely to aid the jump. This finding highlights the need for coaches to design game-situational drills that help players adapt to fatigue and defensive pressure, which can minimize the decline in their performance.  
Additionally, the reduction in shoulder angle and release height under fatigue indicates a biomechanical adjustment that may compromise shooting accuracy over time. Previous studies have suggested that a lower shoulder angle during the release phase reduces the optimal shooting arc, potentially decreasing shot success [34; 35]. However, interestingly, our study found no significant decline in shooting percentage between CON3 (43%) and CON1 (39%). This may suggest that while the players altered their mechanics under fatigue, their familiarity with game-like fatigue conditions allowed them to maintain shooting accuracy.
## 3 Proposed Method
### Data Preprocess
#### 3.1.3 Data Segmentation and Downsampling  
In real-life scenarios, different sampling devices and sensors have varying sampling rates. To accommodate most sensor devices, we need to downsample the data that are sampled at a higher rate. For datasets, matching the sampling rates across all data allows for a more accurate comparison of model performance on different datasets. In this case, we downsample the PAMAP2, Skoda, and USC-HAD datasets to approximately 33 Hz to match the sampling rate of the Opportunity dataset.  
In this paper, our proposed model is to perform feature extraction for each action window after segmenting the sensor data sequence. The two dimensions of an action window are the timestep and the number of sensor features, respectively. Suppose the sensor data sequence is segmented using a sliding window of width \(W\) and a certain overlap rate. Each window obtained can be denoted as \(V=[v_{1},\ldots,v_{t},\ldots,v_{W}]\), where \(v_{t}=[v_{1}^{t},\ldots,v_{K}^{t}]\) represents \(K\) features of the sensor at timestep \(t\). In addition, the action ground truth label for each window is defined as the label with the most occurrences of each sensor data within the window. Window-wise and Sample-wise are two methods used to segment action data [26]. In our study, we uniformly use the Window-wise method on the training, validation, and test sets to ensure consistent results.  
Window size and sliding window overlap rate are important factors in action recognition because different actions can vary in duration and complexity. To better evaluate and explore the impact of these factors on our model's overall effectiveness, we deploy window size and window overlap rate as hyperparameters in our project. We specifically evaluate and explore optimal hyperparameters in Section 5.
# Multi-Templates Based Robust Tracking for Robot Person-Following Tasks
## 4 Evaluation  
To evaluate the performance of our method, we tested it on two collections of datasets: the UGV dataset and a public dataset. The UGV dataset includes 17 image sequences of outdoor person-following tasks recorded by a small unmanned ground vehicle (UGV). The purpose of the person-following system is to reduce the workload of the teleoperator. To conduct a more comprehensive evaluation, we further selected 27 image sequences that involve person tracking from the OTB and UAV [29] datasets. Unlike other popular public tracking datasets, the sequences of the UAV dataset were captured from an aerial viewpoint of low-altitude UAVs.  
In the experiments, we applied the designed algorithm to two representative Siamese trackers: SiamRPN and SiamRPN++. SiamRPN adopts AlexNet as the backbone and takes the feature of the final layer for the correlation. SiamRPN++ uses ResNet50 as the backbone and outputs features by fusing the outputs of multiple layers. We applied our framework to these two approaches and observed the performance improvement. The applied networks and pre-trained weights were obtained from [[https://github.com/STVIR/pysot](https://github.com/STVIR/pysot)]([https://github.com/STVIR/pysot](https://github.com/STVIR/pysot)) (accessed in 20 April 2021). We further applied DaSiamRPN ([[https://github.com/foolwood/DaSiamRPN](https://github.com/foolwood/DaSiamRPN)]([https://github.com/foolwood/DaSiamRPN](https://github.com/foolwood/DaSiamRPN)), accessed in 20 April 2021) and its update-based variation UpdateNet ([[https://github.com/zhanglichao/updatenet](https://github.com/zhanglichao/updatenet)]([https://github.com/zhanglichao/updatenet](https://github.com/zhanglichao/updatenet)), accessed in 20 April 2021) for comparisons. Therefore, the SiamRPN and our improvement, SiamRPN++ and our improvement, and DaSiamRPN and UpdateNet shared weights respectively and can be seen as three comparing groups.  
As the evaluation method, we used one pass evaluation (OPE) [11]. The OPE criterion scores tracker performance using center location error and the bounding box overlap, which yield a precision plot and a success plot according to the threshold, respectively. The success plots are calculated as the percentage of frames with an intersection-over-union (IOU) overlap exceeding a threshold and scored using the area under the curve (AUC) score.  
Since our approach provides improvements in terms of robustness instead of localization accuracy, the precision plots were omitted.  
The experiments were conducted on a desktop with an NVIDIA RTX3090 GPU and an Intel i7 CPU. We set the number of GMM fitting components to six. The KL divergence threshold of setting adjacent matrix was two. The threshold parameter \(\tau_{t}=0.19\), \(N_{pos}=2\), \(N_{neg}=3\).
# Novel Cuckoo Search-Based Metaheuristic Approach for Deep Learning Prediction of Depression
## 1 Introduction
### Proposed Novel Work  
The majority of earlier works on diagnosing depression have mostly focused on machine learning algorithms and optimization techniques, such as support vector machines, K-nearest neighbors, and logistic regression. However, the deep learning technique, which provides fast and precise results, has been relatively underexplored. Deep learning has outperformed numerous algorithms, including LSTM, in various disciplines. With the objective of avoiding overfitting difficulties, this study proposes a deep learning methodology that relies on a novel PS-CS optimization algorithm to accurately identify depression based on user posts and phrases. The study will conduct a comprehensive comparison with commonly used algorithms and use hyper parameter tuning to enhance the proposed model's performance. The presented model can predict depression with good accuracy and low memory usage. Figure 1 illustrates the study's workflow.
## 1 Introduction
That is to control the amount of fertilizer by adjusting the opening of the fertilizer outlet and the speed of the fertilizer shaft. [PERSON] et al. [17] realized automatic adjustment of the opening of the fertilizer outlet through the air cylinder. Based on this, a two-variable fertilization control system was developed. [PERSON] et al. [18] designed a dual-variable (fertilize outlet opening, fertilizer shaft speed) adjustable fertilization control system. [PERSON] et al. [19; 20] developed a variable fertilizer applicator and used tractor as power source. It could realize the dual-variable adjustment of the speed of the fertilizer shaft and the opening of the grooved wheel. The two-variable fertilization method greatly improves the range and accuracy of fertilization. It effectively improves the accuracy and flexibility of variable fertilization control. However, it faces the same problem as the univariate fertilizer method, that is, only single fertilizer and compound fertilizer can be applied. European and American technology companies, RDS, CASE, Ag Leader, etc., developed and have been put on the market. After continuous practice, it was proved that precise fertilization can reduce the application of fertilizer and improve the quality of crops. Among them, N, P, K split application was more conducive to controlling the amount of fertilizer on demand and reducing fertilizer waste, and had strong practicability and promotion value [10; 13]. The above results play an important role in further research on the mixing of various fertilizers in different proportions. It can effectively realize accurate variable fertilization. In order to change the traditional unreasonable uniform fertilization or single fertilization method and realize the mixing of various fertilizers in different proportions, this study takes the Zhongji Miele 1304 cultivating fertilizer applicator as the research object. A variable formula fertilization control system composed of pressure sensors, speed sensors, servo motors, on-board control terminals, and fertilization actuators based on the Programmable Logic Controller (PLC controller) was designed. Additionally, based on the soil fertilization prescription diagram, the three kinds of nitrogen, phosphorus, and potassium fertilizers are quickly and automatically formulated as needed, in order to achieve accurate variable fertilization. It can effectively improve fertilizer utilization and reduce production costs.
# An Efficient One-Dimensional Texture Representation Approach for Lung Disease Diagnosis
## 4 Datasets  
This method used two types of datasets to utilize the performance of the proposed framework. The first dataset is the Guangzhou Women and Children's Medical Center dataset [25], which consists of chest X-ray images of children formatted in JPEG with 5863 images. Each of the primary folders, train, test, and validation, has two subfolders: normal and pneumonia categories; the pneumonia images are divided into two labels, bacteria and virus. This dataset has already been split into 90% for training and 10% for validation. Figure 4 shows the samples of each class, normal bacteria and virus, respectively. On the other hand, the COVID-19, pneumonia, and normal chest X-ray PA dataset [27] contains three classes: covid, pneumonia, and normal. Figure 5 illustrates the class of the PA dataset, normal, covid, and pneumonia, respectively. This dataset [27] contains 4575 samples of children and adults, and it was split into 80:10:10 for training, testing, and validation, respectively.  
In addition, in PA, pneumonia and COVID-19 could be differentiated radiologically. Pneumonia looks like a localized, condensed consolidation opacity in one or more lung lobes. COVID-19, in its early stages, demonstrates bilateral, peripheral ground-glass cloudiness distribution without lobar dense consolidation. At late stages, the ground glass cloudiness progresses into consolidations with an \"irrational paving\" shape of interlobular septal thickening. Moreover, pleural effusions are likely present in pneumonia but uncommon in COVID-19 [55]. In the Guangzhou dataset, bacterial and viral pneumonia exhibits distinct radiological characteristics. Radiographically, bacterial pneumonia is typically a focal lobar consolidation seen in one lobe. In contrast, viral pneumonia generally presents with a more diffuse \"interstitial\" pattern across both lungs [25].  
Briefly, in both datasets, chest X-rays are taken with ordinary posteroanterior (PA) and anteroposterior (AP) projections for respiratory assessment to provide clear insights into lung health and the distribution and severity of respiratory involvement. Thus, these datasets mostly include X-ray images for screening and diagnostic purposes in clinics [25; 26; 27]. The X-ray techniques used in this dataset are digital radiography to have a higher resolution and more manageable dataset for computational analysis; standardized radiation settings tailored for patients to reduce the sensitivity to X-rays; and image preprocessing approaches to enhance the clarity of the images for DL applications and testing algorithms for automated respiratory disease classification. Thus, our proposed method will classify the label among three classes per dataset.
## 5 Conclusions  
In this paper, we have proposed a system for the transmission of high-resolution images from a UAV to a ground station in real time. The importance of using GIS as an essential tool in search planning and monitoring was explained. After presenting our motivation and the problems of search and rescue missions, the required specifications were defined. To verify the system's acceptability, a set of tests were designed and carried out.  
The final results of the tests carried out in the chosen non-urban environment showed that the proposed approach using data links at 2.4 GHz is viable. The measured throughput is sufficient for a continuous transmission of high-resolution images from the UAV to the  
Figure 15: A part of the map produced with Agisoft Metashape. Number of stitched 4000 \(\times\) 3000 images: 37; flying altitude: 107; ground resolution: 4.14 cm/pix; coverage area: 0.104 km\({}^{2}\).  
ground station. The additional findings showed that the implementation of a directional antenna is essential for this problem since it significantly improves the throughput results. As a result of this research, up-to-date information about the terrain during early stages of search and rescue missions can be obtained and used for planning and detection.  
Future work will be oriented towards establishing ad hoc networks using the presented modules, a tethered UAV with data links at the ground station as well as additional UAVs for image transmission. The complete system will be tested on several mountainous terrains.  
Conceptualization, V.P.; methodology, V.P., P.S. and M.P.; software, A.M.; validation, M.P. and A.M.; formal analysis, M.P. and A.M.; investigation, S.G. and P.S.; resources, M.P.; data curation, A.M.; writing--original draft preparation, V.P.; writing--review and editing, P.S., S.G. and M.P.; visualization, A.M.; supervision, V.P.; project administration, V.P.; funding acquisition, V.P. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the project \"Prototype of an Intelligent System for Search and Rescue\", grant number KK.01.2.1.01.0075, funded by the European Regional Development Fund.  
Not applicable  
Data Availability Statement: Data sharing not applicable.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Acceleration of Service Life Testing by Using Weibull Distribution on Fiber Optical Connectors
## 5 Discussion  
In this investigation, a Weibull probabilistic alternative to determine the fiber optical connector's reliability is presented. The reliability was estimated based on the service life testing GR-326 and an accelerated vibration test developed. Vibration stress was employed to determine the failure times of the product. In regard to the reliability results obtained by using the steps in Figure 2, they are based on the insertion loss measurements. This proposal predicts a reliability of \(R(t)=0.694\) for service life testing and a reliability of \(R(t)=0.970\) for single vibration ALT. It is important to note that service life testing does consume 70.2% of the connector's lifetime. However, we know the product's reliability data when they are submitted to the whole service life testing program or individual vibration testing.  
The proposed analysis is useful and can be applied in environments where fiber optical connectors are installed in telecommunications. The importance of these results lies in the increased application of fiber optic connectors that has emerged in telecommunications technology, since it uses digital encoding of data streams for telephone, video and data services and internet for homes and businesses, applications in which reliability is key. Finally, the reliability of a fiber optical connector is of high importance in telecommunications applications where long connections represent a large investment and high reliability is required, and therefore, more research must be pursued.