esa-sceva/satcom-chunk-collection huggingface dataset:
# Action Classification for Partially Occluded Silhouettes by Means of Shape and Action Descriptors
## 3 Proposed Approach for Extraction and Classification of Action Descriptors  
In this paper, an approach for action recognition is evaluated in the presence of static occlusion. A procedure consisting of several processing steps is proposed. It combines centroid locations that capture motion information, simple shape descriptors that represent characteristics of silhouettes and the Fourier transform to extract features from action descriptors. A previous version of the approach was presented in [36], where it was testedusing more than twenty shape features and three matching measures. The convex hulls of silhouettes were used as input data in a scenario involving the recognition of eight types of actions corresponding to home exercises. The aim of the experiments was to indicate the most accurate shape features for action classification without any occlusions. In turn, here the evaluation of the approach is carried out in the presence of occlusion for a specific scenario concerning the recognition of various exercises performed by a single person in front of a static camera. It is assumed that either the upper or lower part of the silhouette is occluded because of the camera being positioned incorrectly or a person being not far enough from the camera. Only foreground masks extracted from the video sequences are used for recognition, due to the fact that they carry information about an object's shape, pose and position in the video frame. A static occlusion is added to every image, which results in removing a part of a silhouette from a foreground binary mask. It is assumed that all images are occluded; however, in several cases, a silhouette could temporarily be out of occlusion. For example, if there is an upper occlusion and a person bends down, a silhouette is fully visible for several frames. The similar situation may occur for jumping in place when a person bends the legs. This can be seen as a dynamic occlusion. However, since static occlusion is added to every frame and whole sequences are analysed, the small amount of dynamic occlusion is of minor importance and simply results from the intrinsic characteristics of some action classes. The following subsections explain in more detail how silhouette sequences are processed and classified. Figure 1 gives an overview of the proposed approach.
# Micro-Raman Spectroscopy of Dental Implants Subjected to Different Surface Treatments
###### Abstract  
The aim of the study was to qualitatively investigate the structure of the surface layer of TiO\({}_{2}\) on dental implants made of Ti-6 Al-4V subjected to different manufacturing treatments. M (machined), B (Al\({}_{2}\)O\({}_{3}\)-blasted), E (HNO\({}_{3}\)\(\backslash\)HF-etched), B + E and A (B + E + anodized) implants and a further group receiving the same treatments as the first group with the addition of a final decontamination with cold plasma were included in the study. Examination was performed using micro-Raman spectroscopy. The surface treatments evaluated did not achieve the formation of crystalline TiO\({}_{2}\). The increase in the complexity of surface treatment produced a proportional increase in the thickness of amorphous TiO\({}_{2}\) oxide. In the B + E group, the plasma treatment enhanced the amorphous oxide thickness of TiO\({}_{2}\). The other surfaces treated by plasma decontamination did not show a difference to the respective untreated ones. The investigated surface treatments did not change the crystalline cage of TiO\({}_{2}\) in Ti-6 Al-4V implants but affected the thickness of the oxide layer. The biological response could be influenced by different oxide thicknesses. Additional information on superficial TiO\({}_{2}\) structural organization can be obtained by micro-Raman evaluation of dental implants. Dental implants with B + E + plasma and A superficial treatments allowed the maximum formation of the amorphous oxide thickness.  
anatase; dental implant; Raman spectroscopy; surface; TiO\({}_{2}\)
## 4 Verification  
### Indirect HI Construction  
In this section, the #5 satellite Li-ion battery (as described in Section 2.1) is used and handled to verify the proposed method of this paper.  
Firstly according to Steps 1-3 in Section 3.1, the upper and lower limit voltages are set 3.8 V and 3.41 V respectively. Figure 5a,b gives the Li-ion battery capacity and indirect HI series curves. From the two figures, it is shown that the constructed indirect HI is very similar to Li-ion battery capacity, and the Spearman correlation coefficient between indirect HI series and Li-ion battery capacity series is equal to 0.9991. The result of Figure 5c also verifies the higher correlation. Finally, the mapping relationship between indirect HI and Li-ion battery capacity is obtained by the polynomial fitting method. The specific mapping relationship can be expressed as a linear function; the parameters are, respectively, 0.0005 and 0.7193. According to the two parameters, the transfer result of indirect HI can be seen in Figure 5d.  
Therefore, the constructed indirect HI can replace Li-ion battery capacity for on-line RUL prognosis.
## 2 Methodology
### Evaluation Metrics  
In this paper, two evaluation metrics were used to verify the effectiveness of the proposed method. The peak signal-to-noise ratio (PSNR) measures the ratio between the maximum possible power of a signal and the power of corrupting noise. The signal-to-noise ratio (SNR) evaluates the level of desired signal to background noise, providing an indication of structural clarity.
# Quantifying the Occurrence of Multi-Hazards Due to Climate Change
## 3 Results and Discussion
### Precipitation Rate  
Figure 5a presents, for the historical period, the probability of precipitation extremes calculated as the probability of precipitation rates exceeding 10 mm/h. The pattern with persisting probabilities over the majority of the domain of less than 0.025% does not yield noticeable spatial variability. Yet, increased probabilities of extreme precipitation with values greater than 0.075% are observable in the very high mountainous areas of the central and eastern mainland and over the summits of Crete and Peloponnese. In addition, some areas known for high-precipitation rates such as the Ionian islands and parts of central and eastern Macedonia and Rhodes reasonably exhibit distinguishable contours of the probability of exceeding 10 mm/h up to 0.075%.  
The differences between the future projections and historical simulations indicate a reduction in future extreme precipitation mostly in the eastern parts of the mainland, central Aegean islands and mountainous areas of Crete (Figure 5b-e). The maximum decrease is obtained in the near future (Figure 5b,c). Additionally, with reference to the historical period and both RCPs, we may observe minute and insignificant changes in extreme precipitation rates, more extensively in the regions of Thessaly and central Macedonia and predominantly in the near future (Figure 5b,c). However, interesting patterns of increased probabilities of extreme precipitation can be seen in highly mountainous areas primarily in the western and northeastern mainland (in the range of 0.051-0.285%) and more vividly for RCP8.5 (Figure 5d,e). The RCP4.5 projections do not show a noticeable change between the two future periods. On the contrary, according to RCP8.5, the far-future period shows on average an increase in the probability of rainfall extremes compared with the near future, both in magnitude and spatial extent. Overall, the patterns of differences showcase increased probabilities of extreme precipitation rates in the studied future periods even at low topographic heights, which may highlight a bothersome climate-change effect for the agricultural economy.  
Figure 4: (**a**) Spatial distribution of probability of TN exceedance below \(-\)5 \({}^{\circ}\)C calculated using EC–Earth–WRF downscaled data for the historical winter period 1980–2004. Differences (future–historical) in the probability of TN exceedance for: (**b**) RCP4.5 in near future (2025–2049), (**c**) RCP8.5 in near future (2025–2049), (**d**) RCP4.5 in far future (2075–2099) and (**e**) RCP8.5 in far future (2075–2099).
## 3 Case Study
### Processed Datasets (CallsDay1h and CallsDay6h)  
To check the calling behaviour of the different users, two different datasets that characterize the behaviour of a user in terms of calling consumption per day are created from the previous CDR. These datasets are obtained only by pre-processing the previously-described CDRs, and no extra data about the calls and callers is used. As it was explained, since the patterns obtained need to be clear and easy to understand, the time divisions used are very typical: one-hour interval (_CallsDay1h_) and six-hour interval: morning, afternoon, evening, and night (_CallsDay6h_). In Section 5 the proposed approach is described and it will be evaluated using the original CDRs and the two generated datasets.  
In the first processed dataset (named _CallsDay1h_), the total duration of the calls (in seconds) made by a specific caller is calculated per hour (from 12:00 to 13:00, from 13:00 to 14:00, etc.). Thus, a set of 24 values (seconds) is obtained per day and user. Figure 1b shows how this set of values created per day and user can be represented as a distribution of 24 values. This is the pre-processing of information about the calling behaviour of the user #0 during the day 1. For example, this user (#0) made the first call from 09:00 to 10:00. Since a new record per user and day is created, the dataset _CallsDay1h_ contains 4000 samples (10 days \(\times\) 400 users).  
The structure of the second dataset (named _CallsDay6h_) is the same as _CallsDay1h_. However, in this case, the total duration of calls made by a caller is calculated during six consecutive hours is made: (1) from 00:00 to 06:00, (2) from 06:00 to 12:00, (3) from 12:00 to 18:00, and (4) from 18:00 to 00:00. Thus, the values can be stored as a vector of four values (seconds). Figure 1c shows how these four values are obtained and represented as a distribution of four values. As in the previous dataset, the number of samples of this dataset is 4000.
# Hub Airport End-Around Taxiway Construction Planning Development: A Review
## 4 The Construction Principles and Key Points of End-Around Taxiway Layout
### Limiting Requirements in End-Around Taxivany Design
#### 4.3.1 BAT (Back-Around Taxiway)  
The aircraft operating on the EAT axis behind the departing aircraft, so there are no obstacle limitation surfaces involved in takeoff or landing. The BAT primarily considers the impact of jet blasts from departing aircraft. However, the taxiway itself (excluding the operating aircraft) should not penetrate the ILS surface of the airport.  
Figure 12 illustrates the impact range of a jet blast during takeoff for a typical aircraft. It is generally considered that a jet blast wind speed of 56 km/h is tolerable for people, vehicles, or other movable devices [76; 77; 78]. The B747-8 has the longest 56 km/h jet blast range [67], starting at approximately 533 m. Only a few aircraft, like the B747-8 and B777-200, require distances greater than 450 m.  
However, it should be taken into consideration that after the aircraft enters the runway, the takeoff starting point is typically situated about 70-80 m away from the runway threshold [73; 80; 81]. The aircraft requires a certain distance to prepare for takeoff and reach full power. Therefore, a length of 350-385 m is considered sufficient to meet the blast limit. For instance, PKX has a BAT of 375 m from the runway threshold, which satisfies this requirement.  
For the BAT mode, if the runway is equipped with an ILS (Instrument Landing System), the distance of 350-385 m is generally considered outside the critical zone but cannot completely prevent entry into certain sensitive areas. But, when an aircraft is operating on the BAT, the runway is in a takeoff state and the LOC (Localizer) and GP (Glide slope) signals are not active.  
The BAT is typically positioned within the protected area of the approach lighting system. It is recommended to refrain from installing lights on the surface of the BAT. However, lights on the shoulder of the BAT are allowed. These shoulder lights should be embedded to prevent protrusions and potential obstruction on the taxiway. It should be noted that the elevation of these lights aligns with the terrain, which may not comply with the required slope ratio of 1:66 for ascent or 1:40 for descent. Consequently, meticulous coordination between terrain design and the elevation of the light fixtures is imperative to ensure compliance with the applicable specifications.  
Figure 12: Typical aircraft jet blast impact range of taking off [17].
# Adaptive Polling Medium Access Control Protocol for Optic Wireless Networks
## 1 Introduction
Nevertheless, the benefits of using the visible light spectrum in communication can only be realized if several challenges such as medium access control for multi-user access, precise channel model, interference mitigation techniques and software-defined networks (SDN) for visible light-based networks are well addressed [13]. In this study, we investigate the problem of multi-user access for optical wireless networks (OWNs). We propose the non-contention protocol namely adaptive polling medium access (APMAC) protocol that minimizes waiting time, and packet collision while maximizing system utilization and fairness. Moreover, the APMAC protocol considers the physical properties of the physical layer of OWNs. The APMAC protocol exploits the dynamic future knowledge (DFK) algorithm for estimating the maximum transmission unit presented in [27] and involves three phases such as association, data transmission, and dissociation phases. The rest of this study is organized as follows. Section 2 presents existing research publications related to this study. Section 3 describes the statement of the problem and assumptions we considered in this study. We narrate the APMAC protocol in Section 4 considering the association, data transmission and dissociation phases. Experimental setup and numerical results are presented in Section 5. Finally, the conclusion of this study is found in Section 6.
## 2 Closed Loop Model
### System Dynamics with PPF Control  
Figure 2 depicts a two-mass model and a schematic illustration of a series hybrid electric vehicle incorporating a PPF controller. \(T_{E}\) denotes engine torque, \(T_{G}\) denotes PMSG torque, \(J_{1}\) denotes PMSG inertia, \(J_{2}\) denotes engine inertia, \(\phi_{1}\) and \(\phi_{2}\) denote the torsional vibration angles at the shaft's end under various excitations, where C represents the damping coefficient, and \(K\) denotes equivalent shaft stiffness. \(x\) is the displacement of the system. The damping factor epitomized by \(\alpha_{1}\), The nonlinearities terms factors are \(\eta,\beta,\gamma,\mu\). The excitation frequency and amplitude are \(\omega\) and \(f\). \(\omega_{1}\) is the natural frequency with the addition of the positive position feedback controller on the system.  
The effective PPF controller design hinges on aligning its natural frequency, denoted as \(\omega_{1}\) with both the external force frequency symbolized by \(\omega\), while the natural frequency of the hybrid electric vehicles (HEVs) is represented by \(\omega_{2}\). This synchronization is essential for achieving optimal performance and stability and enables the controller to accurately respond to the external force and the intrinsic dynamics of the HEVs system. However, when resonance conditions between the main system and the PPF controller are disrupted, the controller may inadvertently inject excessive vibrational energy into the main system instead of damping it.  
Therefore, the equations of the motions of the system with PPF control found in Figure 2 can be constructed as follows:  
\[\ddot{x}+\varepsilon\alpha_{1}\dot{x}+{\omega_{1}}^{2}x+\varepsilon\eta x^{2} +\varepsilon\beta x^{3}+\varepsilon\gamma x^{4}+2\mu x^{5}=\varepsilon f\sin( 2\omega t)+\varepsilon\lambda_{1}y, \tag{2}\]  
Figure 1: Diagram of the series hybrid electric vehicle.  
Figure 2: A two-mass model and a schematic illustration of a series hybrid electric vehicle [6].  
\[\ddot{y}+\varepsilon\alpha_{2}\dot{y}+{\omega_{2}}^{2}y=\varepsilon\lambda_{2}x. \tag{3}\]
## 1 Introduction
The effectiveness of the model was demonstrated by predicting future video frames using the synthetic dataset MovingMNIST [11] and the open-world dataset KTH [12]. In this study, we build an encoder-decoder structure based on SA-ConvLSTM, which combines the advantages of ConvLSTM with an attention mechanism, and apply it to the MovingMNIST and KTH datasets to verify the performance of the proposed model. The major contributions of this study are as follows:  
* An encoder-decoder structure based on SA-ConvLSTM is proposed. This structure can effectively capture the long-range dependence through the self-attention mechanism while maintaining the advantages of the traditional ConvLSTM. * The effectiveness of the SA-ConvLSTM-based encoder-decoder structure is validated on the MovingMNIST and KTH datasets, showing that the proposed model outperforms the traditional ConvLSTM model in the spatiotemporal prediction task. * The importance of a multi-layer encoder-decoder structure is emphasized, with the experiments confirming that multi-layer structures provide better prediction performance than single-layer structures. This suggests that the model can better capture hierarchical spatiotemporal features. The remainder of this paper is organized as follows. Section 2 presents related studies from the literature, and Section 3 provides an overall description of the proposed model. Section 4 describes the results of the experiments conducted to evaluate the performance of the model. The final section concludes the paper.
## 2 Materials and Methods
### Artificial Neural Networks (ANN)  
An artificial neural network is a data processing system built of simple processing units (neurons) with the tendency of storing experience for later use [27]. Each ANN consists of neurons interconnected by directed communication links (synaptic weights of neurons). The weights are calibrated when solving tasks in which the network acquires \"knowledge\" by learning from available data [27]. The most common networks are so-called Feedforward (fully connected) or shorter just ANN, which consist of one input layer, one or more hidden layers, and one output layer which are fully connected (each neuron of the previous layer is connected with each neuron in the next layer).  
The mathematical process in each neuron \(k\) of the network can be described by the following two equations [27]:  
\[u_{k}=\sum_{j=1}^{m}w_{kj}x_{j} \tag{1}\]  
and  
\[y_{k}=\varphi(u_{k}+b_{k}) \tag{2}\]  
where: \(x_{1}\), \(x_{2}\),..., \(x_{m}\) are the inputs; \(w_{1}\), \(w_{2}\),..., \(w_{km}\) are the synaptic weights of the neuron \(k\); \(u_{k}\) is the linear combiner output due to the inputs; \(b_{k}\) is the bias term; \(\varphi\) is the activation function; and \(y_{k}\) is the output of the neuron \(k\). A visualization of the processes (Equations (1) and (2)) in the neuron is given in Figure 4.  
When data is delivered to an untrained network, the output differs from the expected value because the synaptic weights (w) and bias (b) are not yet adjusted to the data. The process of adjusting the network weights and biases is called training. During the training phase, input data is delivered to the network (forward propagation process), the outputs are calculated and compared with the observed (expected) output, the loss is calculated using the target (loss) function. The type of function depends on the type of the problem, for regression tasks a popular choice is the mean squared error (MSE) function, while for classification tasks the cross-entropy loss function is often chosen. The calculated error propagated backwards (backpropagation process) through the network by calculating the gradients of the loss function (gradient descent). Depending on the loss value during gradient descent, the weights and biases get updated to minimize the error. The procedure is repeated either a fixed number of times (fixed number of epochs) or until certain conditions are met, e.g., the network has reached a predefined error value.
# Systematic Review on Individualized Versus Standardized Parental Nutrition in Preterm Infants
## 4 Discussion  
PN plays an important role in the nutritional support of preterm infants. However, PN practice is usually described as often with insufficient intakes, poor growth and adverse events. Both SPN and IPN are currently used in preterm clinical practice. A SPN is a pre-defined formulation made to a set composition that does not vary. Their efficient and safe use in pediatric patients--as in adults in whom they have been used for more than 10 years--is based on the tenet that many patients with a similar condition have  
Figure 4: Forest plot on the effect of standardized parenteral nutrition (SPN) vs. individualized parenteral nutrition (IPN) on duration of PN (days). Green squares represent individual studies, the diamond represents the subgroup analysis.  
Figure 5: Forest plot on the effect of standardized parenteral nutrition (SPN) vs. individualized parenteral nutrition (IPN) on growth. Green squares represent individual studies, diamonds represent subgroup analyses.  
comparable nutritional needs allowing their management with one or several balanced SPN formulations. Prepared in advance by a hospital pharmacy or an external compounding center, SPN bags are rapidly available when indicated.