esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Experimental Approach to the Problem  
Stiffness and R\({}_{\text{P}}\) are essential parameters that are often evaluated in strength and conditioning investigations, particularly during vertical, countermovement, and repetitive jumps, in relation to both performance and injuries. However, there is a lack of research specifically focused on stiffness and strength in the context of pylemetric training on different jumping surfaces. To address this gap and provide scientific evidence regarding leg stiffness and R\({}_{\text{P}}\), our study took a new methodological approach. A higher level of leg stiffness is believed to enhance the dynamic mechanism's ability to generate rebound movements during the stretch-shortening cycle [23; 24] and reduce the risk of excessive load on passive knee structures [25]. Plyometric training is known to enhance these characteristics, and by using the mini trampoline as a training tool, we aimed to gain valuable insights beyond what traditional vertical jumps, countermovement jumps, and repetitive jumps can offer. Additionally, our study considered the influence of sex on leg stiffness and R\({}_{\text{P}}\), providing a comprehensive understanding of how these factors interact. In this investigation, we measured leg stiffness (K) in (kilograms*Newton)/(meters) and R\({}_{\text{P}}\) in Watt/kilograms as dependent variables; sex (female and male) and testing time (PRE vs. POST) were used as independent variables.  
To ensure direct comparability with previous studies, we estimated leg stiffness using the methods originally described by [PERSON] [26], in which leg stiffness is defined as the ratio between the peak vertical ground reaction force (F\({}_{\text{max}}\)) and the maximum vertical displacement of the center of mass (\(\Delta\)CoM), which is the difference in the center of mass'svertical position at initial ground contact and at the lowest point. The equation for leg stiffness is given as follows (Equation (1)):  
\[\mathrm{K}=\frac{Fmax}{\Delta\mathrm{CoM}}\ [\mathrm{kN/m}] \tag{1}\]  
This method has been extensively utilized in evaluating leg stiffness during hopping tasks in various studies [27; 28; 29; 30].  
To assess \(\mathrm{R_{P}}\), we employed the following Equation (2) [31], which involves dividing the product of gravity acceleration (g), time of flight (TF), and total time (TT) (the sum of the time of contact (TC) and TF) by the product of 4 and the TC:  
\[\mathrm{R_{P}}=\frac{g^{2}*(\mathrm{TF}*\mathrm{TT})}{4*\mathrm{TC}}\ [ \mathrm{Watt/kg}] \tag{2}\]  
A 2-way mixed effects model was utilized to assess the reliability of the measurements recorded by the force plate. The force platform demonstrated high intra-class correlation coefficients (ICCs) for both stiffness (ICCs = 0.92; 95% confidence interval [CI]: 0.82-0.96) and \(\mathrm{R_{P}}\) (ICCs = 0.90; 95% CI: 0.79-0.96) [32; 33]. These high ICC values indicate strong consistency and agreement in the measured stiffness and \(\mathrm{R_{P}}\) values obtained from the force plate, which confirms the reliability of the force platform's data for our study.
# Modeling Trust in IoT Systems for Drinking-Water Management
## 4 Results and Discussion
### Discussion
#### 4.2.1 Flexibility of the Model  
Our AHP-based model offers remarkable flexibility. By assigning dynamic weights to trust-influencing factors, we can adjust the impact of each characteristic according to the specific needs of the system. This flexibility allows easy adaptation to different conditions of water-treatment plants, reinforcing the relevance of our approach.
# Hybrid LSTM Model to Predict the Level of Air Pollution in Montenegro
## 2 Related Work  
Recent studies have been focusing on sophisticated learning algorithms to enhance air quality evaluation and air pollution prediction. [PERSON] and [PERSON] [11] used the LSTMmodel in conjunction with GA to enhance the performance of air prediction models. The performance of the GA-LSTM model was evaluated and compared with models employing manual criteria. The results showed a significant improvement in LSTM performance with the integration of GA. [PERSON] et al. [12] chose to perform only \(PM_{2.5}\) forecasting by applying deep learning techniques, among which the LSTM encoder-decoder variant showed promising results. In another study, [PERSON] et al. [13] examined the methods of predicting PM levels and showed that LSTM combined with deep autoencoder techniques showed slightly better performance than the typical LSTM model.  
[PERSON] and [PERSON] [14] employed bagging and boosting techniques for PM prediction. Their experiments demonstrated significant improvements in result quality when using bagging and boosting ensembles with weak predictors. The Mean Absolute Error was reduced by more than 30% for \(PM_{10}\) and 20% for \(PM_{2.5}\) compared to individual predictors. [PERSON] et al. [15] developed multiple ML models, including adaptive boosting (AdaBoost), an artificial neural network (ANN), random forest (RF), a stacking ensemble, and SVM, for the prediction of air quality index levels over different time intervals (1 h, 8 h, and 24 h). The stacking ensemble, AdaBoost, and RF models showed the best prediction performance, although their forecasting accuracy varied across geographical regions. [PERSON] et al. [16] used linear regression, SVM, decision tree, and RF models for air quality prediction. The RF model achieved the highest accuracy among the tested algorithms. [PERSON] and [PERSON] [17] applied five different ML models to predict air quality. The authors showed that the strongest correlation between predicted and actual data was achieved by the XGBoost model. [PERSON] [18] conducted a study where a few standard classification models were applied to a dataset that included pollutant concentrations and meteorological data. Due to its robustness against overfitting, the RF classifier demonstrated superior performance compared to other classifiers.  
In their review, [PERSON] and [PERSON] [19] examined a collection of 46 highly relevant journal papers. The authors found that there were more studies focused on pollutants such as \(O_{3}\), \(NO_{2}\), \(PM_{10}\), and \(PM_{2.5}\), while fewer studies covered CAQI prediction. We refer interested readers to a comprehensive review [20] of 155 papers that provides a detailed analysis of air quality prediction using ML techniques.
# Transmission Tower Re-Identification Algorithm Based on Machine Vision
## 2 The Proposed Method  
Transmission tower re-identification, aiming to distinguish between different transmission tower instances, is addressed in this paper based on a template library algorithm. This method encompasses two stages: firstly, establishing a transmission tower identity matching template library, and secondly, performing identity matching of transmission towers in input images. Key technologies in both stages include a transmission tower detection network and a matching network.
## 2 Basic Theory
### Estimation of Apparent Angle  
The submarine target has a certain degree of geometric size in space except for the specific angle of the gangway, and it is especially obvious in the lateral size. While large-scale targets have a certain variation pattern in orientation, the target spatial scale size can be obtained according to its obtained angle, the apparent angle; therefore, the apparent angle is an important parameter of torpedoes which is usually expressed by the difference between the first and last values of the azimuthal strike fitting slope [16; 17]. Figure 5 shows the split echo signal.  
Figure 4: The target echo broadening.  
Figure 5: The split echo signal.  
The apparent angle is extracted by splitting the received echo signal into \(N\) subframes in the time domain, which is shown in the figure above. The exact orientation of the split beam is obtained for each subframe signal \(\theta_{i}\) (\(i\) = 1, 2, \(\cdots\), \(N\)). The least-squares fitting [18; 19] is performed for each of the obtained orientations, and the fitted linear equations are as follows:  
\[\theta=a+b\cdot i \tag{3}\]  
Among them, \(i\) is the number of subframes and \(\theta\) is the corresponding azimuth value of each subframe. The least-squares method needs to minimize the fitting variance \(\Theta\):  
\[\Theta=\sum\limits_{i=1}^{N}{(\theta_{i}-\theta)^{2}=}\sum\limits_{i=1}^{N}{( \theta_{i}-(a+b\cdot i))^{2}} \tag{4}\]  
The following conditions need to be met:  
\[\begin{array}{l}\frac{\partial\Theta}{\partial a}=0\Rightarrow\sum\limits _{i=1}^{N}{(-2\theta_{i}+2a+2 bi)}=0\\ \frac{\partial\Theta}{\partial b}=0\Rightarrow\sum\limits_{i=1}^{N}{(-2 \theta_{i}i+2 ai+2 bi^{2})}=0\end{array} \tag{5}\]  
Set \(\sum\limits_{i=1}^{N}{\theta_{i}}=A\), \(\sum\limits_{i=1}^{N}{\theta_{i}}\cdot i=B\). Then, the intercept and slope of the fitted line can be derived:  
\[\begin{array}{l}a=\frac{6}{N(N+1)}{\left(\frac{2N+1}{3}A-B\right)}\\ b=\frac{12}{N(N^{2}-1)}{\left(B-A\frac{N+1}{2}\right)}\end{array} \tag{6}\]  
The apparent angle \(T_{r}\) is obtained:  
\[\begin{array}{l}T_{r}=\theta(N)-\theta(1)=b(N-1)\\ =12{\left(\frac{B}{N(N+1)}-A\frac{1}{2N}\right)}\end{array} \tag{7}\]  
Figure 6 shows the schematic diagram of the orientation approach method.  
Figure 6: The azimuth direction method.
# \"In Vivo\" Validation of 3D-Printed Innovative Surgical Template for Lumbar Spinal Arthrodesign.
### Aims and Scopes  
This paper is focused on the validation of the design of a surgical device, that is opportunely customized for the patient to optimize the lumbar pedicle screws insertion via the CAD-CAS approach and its manufacturing via rapid prototyping techniques. The optimal insertion trajectories, the length, and the diameter of the screws are designed starting from CAD model of the area of interest (insertion area). This model comes from reverse engineering (RE) techniques.  
The design of the device starts from the knowledge of the screw entry point that will be chosen by the surgeon based on his experience.  
Subsequently, the optimal screw insertion trajectories are determined via a patented algorithm [8; 11; 12]. The matching and disengage system presents several innovations. The first consists of the possibility of being able to apply not only pointers and perforators, but also to directly apply pedicle screws that can be used with any surgical instruments. In fact, the guide cylinders are designed to be able to implant different screws with different instruments thanks to a notch that allows the controlled fracture of a portion of the cylinders themselves, facilitating the last phase of screw implantology and the mask removal. An innovative contact surface between mask and vertebra has also been designed with several holes, with a diameter of about half a millimetre, that are arranged randomly on the surface of the mask to increase the probability that any presence of small tissue produced during the skeletonization will be channelled into these holes, thereby avoiding creating interference in the coupling between the mask and the verteb with possible mismatching.  
This template also allows preserving a good portion of the supraspinous longitudinal ligament (when there is no laminectomy) because the anchor points of the template on the verteb are traceable to four portions of vertebral lamina. Finally, thanks to a semi-automatic trajectory identification algorithm that allows the surgeon to choose the first access point, it is also possible to think about designing the whole template in a semi-automatic way.  
The proposed device is used only as a guide that integrates perfectly with the standard freehand surgical procedure without altering it but allowing a guided screw implantology.  
In the classical freehand procedure, the radiation doses, surgery time, and risk of error are generally acceptable but high. Moreover, wrong positioning could induce to surgery failure resulting in re-intervention and, normally, an increase in surgery duration that means an increase of noxious real-time X-rays. The proposed surgical device drives surgeon throughout the best insertion direction of the screws, keeping down the risk of damaging the nervous system. Moreover, this procedure cuts down the surgery duration (resulting in an increase in surgery comfort), the blood loss, and radiation doses.
# Two-Dimensional Interference Estimator with Parallel Structure for Holographic Data Storage Channel
###### Abstract  
Recently, holographic data storage (HDS) has attracted significant attention as the next-generation storage technology because of its large capacity and high-speed transfer capabilities. However, two-dimensional (2D) inter-symbol interference (ISI) is a critical problem encountered in HDS. Therefore, in this study, we propose a 2D interference estimator to prevent the 2D ISI. In addition, we propose a parallel structure to exploit horizontal interference (HI) and vertical interference (VI) effectively. After detecting the HI and VI, the signal is combined using an average function. The simulation results indicate that the proposed model can significantly improve the performance of HDS.  
holographic data storage (HDS); estimating interference; removing interference; detection 1
## 5 Computer Simulations
### Impact of Data Transmission Paths on Spectral Efficiency  
Figure 5 illustrates the impact of increasing the number of data transmission paths \(L\) on spectral efficiency performance. The number of antennas at the source, each relay node and destination are set as \(N_{t}=64,\ N_{rel}=64\), and \(N_{r}=16\), respectively. Just like the previous simulation results, it is assumed that the number of RF chains is equal to the number of transmitted data streams, i.e., \(N_{t}^{RF}=N_{rel}^{RF}=N_{r}^{RF}=N_{s}=4\). In addition, the mm-Wave channel matrix is generated with ULA [49] at \(L=[5,\ 10,\ 20,\ 30]\), where each transmission path follows uniformly distributed \(AoA\) and \(AoD\) in the interval \([-\pi/2,\pi/2]\). The spectral efficiency is evaluated at the aforementioned values of \(L\) by keeping all other parameters constant to visualize the effect of this change. It is evident from simulation results in Figure 5 that the spectral efficiency decreases by increasing the value of \(L\). However, the performance of the proposed algorithm is close to its fully digital counterpart irrespective of the number of data transmission paths.
# Detection of Logos of Moving Vehicles under Complex Lighting Conditions
## 3 Methodology
### Improved YoloF Algorithm
Traditional object detectors usually use the mean square error to directly regress the center point coordinates, height, and width of the bounding box, while the anchor-based  
Figure 1: The sketch of YOLOF [33]. method estimates the offset. Both of them fail to consider the integrity of the object itself. In the design of the loss function of the original model, the prediction error of the coordinates of the bounding box, the confidence error of the bounding box, and the prediction error of the object classification are taken into consideration. To solve the multi-scale problem with vehicle logos in moving vehicles' logo images, this study adds a modulation coefficient ato the cross-entropy function based on the idea of Focal Loss [36], which changes the classification error function to Equation (7):  
\[L_{p}=\sum_{i=0}^{S^{2}}l_{ij}^{obj}\sum_{\epsilon=0}^{classes}\left(\left[ \hat{P}_{i}^{j}\log\left(P_{i}^{j}\right)+\left(1+\hat{P}_{i}^{j}\right)^{ \alpha}\log\left(1-P_{i}^{j}\right)\right]\right) \tag{7}\]  
where \(S^{2}\) represents the number of grids divided by the input image, \(l_{ij}^{obj}\) decides whether the \(j\)-th anchor box of the \(i\)-th grid is responsible for the judgment of a certain target, \(c\) represents the category judgment, classes are the category set, \(P_{i}^{j}\) represents the classification probability, and \(\hat{P}_{i}^{j}\) represents the estimated classification probability. Moreover, inspired by Gaussian [5], to improve the accuracy of logo detection, Gaussian distribution is used to improve the loss function and increase the reliability judgment of the bounding box for logo recognition. Taking the x-coordinate of the center point of the bounding box as an example, the modified calculation method of the prediction error of the x-coordinate of the bounding box is as follows (Equation (8)):  
\[L_{x}=-\lambda_{coord}\sum_{i=0}^{S^{2}}\sum_{j=0}^{B}\log\left(N\left(\left. \hat{x}_{i}^{j}\right|\mu_{t_{x}}\left(x_{i}^{j}\right),\,\sum_{t_{x}}\left(x_{ i}^{j}\right)\right)+\epsilon\right. \tag{8}\]  
where, \(t_{x}\) is the offset of the center coordinate of the bounding box from the x-coordinate of the upper left corner of the grid, \(\mu_{tx}\) is the coordinate of the bounding box, and \(\Sigma\) is the uncertainty of each coordinate. In terms of vehicle logo detection speed, the traditional Non-Maximum Suppression (NMS) algorithm arranges the detected target boxes in descending order according to their confidence scores and sets an \(IoU\) threshold to remove the bounding boxes larger than this threshold until all the predicted boxes are traversed, and the remaining bounding boxes are taken as the final target detection results. Since it is sequential traversal that requires sorting and filtering for each category, it will lead to a loss in the speed of the algorithm. In this paper, we use FAST NMS [37] processing to filter and retain each bounding box, which can strike a balance between vehicle logo detection accuracy and speed.
## 2 Materials and Methods
### Data Analysis
Therefore, we used the second approach to determine the sensitivity of NEE and LE to precipitation based on the API index. Extremely high daily API anomalies were defined as anomalies above 1 standard deviation (STD) of available time series at each station. To eliminate the influence of theseasonal cycle, the STD was calculated separately for each calendar month. The threshold for extremely low API is the 5% threshold of the time series amplitude. The amplitude was defined as the difference between the mean maximum and mean minimum API for each calendar month averaged over the period 1991-2021. The STD threshold is not suitable for low API because the STD is often greater than the mean API for a specific month, and therefore, the values below \(-1\) STD are negative, which is impossible for precipitation. The extremely high (low) NEE and LE flux anomalies were defined as exceeding 1 STD (\(-1\) STD) of the total time series for each calendar month separately. The quantile thresholds were not applied to fluxes and API because their PDF types varied significantly between ecosystem types, making it difficult to select the type of theoretical distribution to approximate the empirical distribution of the data sets. In addition, the short time series for ecosystem fluxes at several stations also made it difficult to determine the appropriate theoretical PDF. To analyze the relationships between temperature/precipitation and NEE/LE anomalies, we first focused on the temporal variability of weather conditions (temperature and precipitation) and associated NEE and LE flux anomalies. As an example, Figure S3 in the Supplementary Materials shows the temporal variability of daily temperature anomalies, precipitation amount, API, and NEE and LE flux anomalies for the forest station located in the evergreen needleleaf forest of western Canada (CA-Ca3). To quantify the relationships between the weather extremes and flux anomalies, we calculated the percentage of days when the NEE or LE anomaly exceeded the STD values while the temperature/precipitation exceeded the 95% quantile for the upper threshold or the temperature (API) did not reach the 5% quantile (was lower than 5% of the amplitude) for the lower threshold. The percentage was calculated from the total number of days on which one of the characteristics (temperature anomaly, daily precipitation, or API) exceeded the threshold. Positive NEE anomalies are associated with increased CO\({}_{2}\) emissions, whereas negative anomalies are associated with enhanced CO\({}_{2}\) uptake. The positive/negative LE anomalies are associated with increased/decreased evaporation. The cases for which the total number of days above the threshold for temperature/precipitation and fluxes was less than 10 days were excluded from the data analysis.
## 3 Numerical Simulation Analysis
### Parametric Analyses
#### 3.3.1. Pretightening Force  
Different pretightening forces cause different friction forces for the connector, which may in turn affect the slip of the connector. Therefore, the models with different pretightening forces are analyzed in this section. The change of pretightening force is achieved by modifying the \"Apply Force\" (see Table 4). Through numerical analyses, the force-displacement curves and slip-displacement curves are shown in Figures 19 and 20. In Figure 19, the initial slip of A1-A4 occurs at the horizontal displacement of 1.03 mm, 1.38 mm, 1.73 mm, and 2.00 mm, respectively. In addition, the slip magnitudes of A1-A4 are 2.00 mm, 2.00 mm, 0.97 mm, and 0.16 mm, respectively. It is worth noting that the initial slip will be delayed, and the slip magnitude will decrease, with an increase of the pretightening force. In Figure 20, the stiffnesses of A1-A4 decrease by 70.0%, 66.1%, 57.9%, and 55.2% from the start of slip to the end of slip, respectively. It implies that the stiffness degradation occurs during the slip and the stiffness decreases with the increase of the pretightening force.  
In summary, it is necessary to increase the pretightening force, and high strength bolts can be used for this purpose.
## 4 Conclusions  
In this manuscript, an off-grid, PV-powered RO desalination process was investigated. The focus was on reducing the energy storage size needed for proper operation of the unit against a specified demand. The study showed that controlling the pressure of the feed-in water enhanced the efficiency of the separation process and reduced the power consumption. The idea was to increase the pressure of the feed-in water when there is an excess of the PV-generated, power to reduce the conductivity of the permeate water and to decrease the feed-in pressure when the PV-generated power is relatively low to produce permeate water with a relatively high dissolved salt concentration. This decreased the required capacity for energy storage by up to 20%, and allowed the use of permeate water with a lower salt concentration than that required and accepted as an energy storage parameter.  
The study also shows that if the applied pressure is modulated proportionally to the solar insolation, then the desalinated volume of water per solar day can be controlled to a specific salinity level while minimizing and even eliminating the need to energy storage elements if properly sized. This would greatly motivate the utilization of off-grid PV-powered RO desalination units in remote and agricultural areas due to the introduced cuts in capital cost.  
Formal analysis, M.B. and M.A.-A.; Methodology, M.B., Z.D., and H.K.; Writing - original draft, M.B., Z.D., M.A.-A, A.K., and H.K; Conceptualization, Z.D. and H.K; Visualization, M.A.-A; Resources, H.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors would like to thank the German Jordanian University represented by the Deanship of Graduate Studies and Scientific Research for supporting this work through the SEED grant number: SNRE 03/2018.  
The authors declare no conflict of interest.