esa-sceva/satcom-chunk-collection huggingface dataset:
# Biosimilar Medicines: From Development Process to Marketing Authorization by the EMA and the FDA
## 5 Demonstration of Biosimilarity
### Other Relevant Aspects
#### 5.4.3 Interchangeability  
Interchangeability means that the original medicine may be exchanged by a biosimilar (or vice versa), or a biosimilar by another biosimilar, as long as the expected clinical outcome remains the same. This exchange can take place through two different processes, namely, switching and automatic substitution. Switching is performed when the prescriber ordered the change from one medicine to another with the same therapeutic intent. On the other hand, automatic substitution is carried out if the change is made during clinical practice without consulting the prescriber [2].  
However, there are differences in the regulatory approach to interchangeability. According to the EMA, after the medicine has been approved and has demonstrated comparable efficacy, safety and immunogenicity to its reference product, it is considered interchangeable, and no further studies are necessary. Interchangeability rules are decided by individual Member States according to their legal framework [2; 75; 76]. In Portugal, this decision is undertaken by the National Pharmacy and Therapeutics Commission as an advisory body to INFARMED, I.P. [77; 78]. The exchange process must be carried out under the supervision of the prescriber and with the patient's consent. This way, switching is permitted, but with some restrictions, such as that it can only be performed for treatment periods of more than 6 months. Automatic substitution, on the other hand, is not currently allowed [79].  
The FDA requires additional information to determine if a biosimilar is interchangeable. A switching study should be carried out, in which patients alternate between the original medicine and the interchangeable biosimilar multiple times over a defined interval of time, as shown in Figure 6, in order to prove that the switch between the two medicines does not reduce effectiveness or increase safety risks compared to using only the original product. After the interchangeable biosimilar is approved, it can replace the reference product without the intervention of the prescriber and pharmacies can change the patient's medicines. This process is commonly called \"pharmacy-level substitution\" and depends on the pharmacy laws of each state [80; 81].
## 3 Experimental Results
### Effect of Layering in the Y-Direction of the Cavity  
Since the main response direction of the structure is the Y direction, the Y-direction collision gap between the particles and the cavity is particularly concerned.  
The \"2\" type cavity (cavity with spacers in the Y direction) can ensure that the Z-direction collision gap is equal to the \"1\" type cavity (Y-direction non-separator), and the Y-direction collision gap is reduced to half. In this part of the test, the C1 and C2 cavities were first used to test 2 mm stainless steel particles and 32 mesh tungsten carbide powder. The test results are shown in Table 7, and the corresponding amplitude-frequency response curve are Figures 10 and 11.  
Two-millimeter lead and stainless steel particles and tungsten carbide powder were filled into the A1 and A2 cavities, respectively, and the test results were compared to verify the speculation about the optimal cavity. The test results are shown in Table 8, and the corresponding measurement lines are shown in Figures 12-14. The same trend as given in Table 7, it can be seen that the damping effect of the A2 scheme continues to increase on the basis of A1, so that the \(A_{\text{sin}}\) and \(A_{\text{spec}}\) of the schemes A2_Pb_0.9  
Figure 11: Experiment results of Tungsten carbide powder (layering in Y-direction of the cavity); (a) Sinusoidal excitation; (b) Random excitation.  
Figure 10: Experiment results of 2 mm stainless steel particle (layering in Y-direction of the cavity): (a) Sinusoidal excitation; (b) Random excitation.
# Partial Order Rank Features in Colour Space
## 2 Background
### Rank-Based Approaches to Colour Processing  
Previous approaches to rank-based colour features typically extend grey-scale rank-based methods to the colour domain by considering either the colour channels separately (_intra_-channel features) and/or in pairwise combination (_inter_-channel features). [PERSON] and [PERSON] [18] for instance extended classic LBP by applying it both to each R, G and B colour channel separately and pairwise between each of the R-G, R-B and G-B pairs. [PERSON] et al. [19] adopted the same approach for extending grey-scale ranklets [12] to the colour domain. [PERSON] et al. [20] defined Local Colour Vector Binary Patterns (LCVBP) by decomposing the colour triplets into a norm and angular component and by computing LBP on each of them. More recently, [PERSON] et al. [21] introduced Local Angular Patterns (LAP) which consider the angular component only and discard the norm part altogether.  
Another possible strategy consists of establishing some sort of _a priori_ total ordering on the colour data. This approach is not uncommon in colour morphology--see for instance [PERSON] [4], [PERSON] and [PERSON] [6]--and has been advocated for extending LBP to colour images by [PERSON] [22]. Of late, this family of methods has been extensively investigated by [PERSON] et al. [1] and [PERSON] et al. [23]. The problem is that imposing a total ordering on the colour data inevitably entails a certain degree of arbitrariness, with the consequence that the results tend to be dataset-dependent. On the other hand, morphology for tensor-valued images (that arise from certain magnetic resonance techniques) has relied on the Loewner order, that is in fact a partial order (see for instance [PERSON] et al. [24]; more on this in Section 2.5.2). More recently, this approach has been extended to morphology for colour images [PERSON] and [PERSON] [5]. Partial ordering circumvents the problem of ordering multivariate data totally, at the expense of not allowing comparisons between some colour values.
## 3 Microwave Photonics Applications of DFT
### DFT for Microwave Arbitrary Waveform Generation
#### 3.2.3 Nonlinear DFT for Photonic Microwave AWG  
DFT-based microwave arbitrary waveform generation system, as shown in Figure 5, relies on optical spectral shaping and the following DFT-enabled linear frequency-to-time conversion. A properly designed optical spectral filter with its magnitude response identical to the target microwave waveform is always required. For example, an optical spectral shaper with chirped FSR is used to generate chirped microwave waveforms [103,104]. It is known that an optical filter with a uniform FSR is usually easier to produce. We have proposed a new technique to generate chirped microwave waveforms using an optical spectral shaper having a uniform FSR [56]. Different from previous demonstrations, nonlinear frequency-to-time conversion is employed to generate chirped microwave waveforms from uniformly shaped spectrum. Such a nonlinear DFT process is expressed in Equation (4) and can be realized using a dispersive device having high-order dispersion, such as a very long length-of fiber [106] or a nonlinearly chirped fiber Bragg grating (NL-CFBG) [56]. The NL-CFBG was produced from a regular LCFBG based on strain-gradient beam tuning, as shown in Figure 7a. Figure 7b shows the tunable group delay response of NL-CFBG at different beam displacements. Therefore, another advantage of this technique is that the frequency profile of the generated chirped microwave waveforms can be continuously tuned by applying different strains.
# Application of Low-Altitude UAV Remote Sensing Image Object Detection Based on Improved YOLOv5
## 3 Materials and Methods
### YOLOv5 Network Model
#### 3.1.1 Backbone Module  
The first layer of the backbone is focus. The main function of this module is to enrich the training dataset; in particular, random scaling is used to increase the number of small targets in the training process, improving the robustness of the network model, and greatly improving its ability to detect small targets.  
The default input of YOLOv5s is 640 \(\times\) 640 \(\times\) 3, and the focus layer copies it into four, and then cuts the four pictures into four 320 \(\times\) 320 \(\times\) 3 slices using a slicing operation. Then, the four slices are stitched together depth wise, making the output 320 \(\times\) 320 \(\times\) 12, before being passed through a convolutional layer with a number of convolution kernels equal to 32 in order to generate a 320 \(\times\) 320 \(\times\) 32 output. Finally, the batch normalization and activation function are applied, and the results are used as input to the next convolutional layer.  
BottleNeckCSP is in the third layer of the backbone, and is divided into two main parts, BottleNeck and CSPNet [31]. BottleNeck is a classic residual network structure. The first is a 1 \(\times\) 1 convolutional layer (conv+batch_norm+leaky relu), the next is a 3 \(\times\) 3 convolutional layer, and finally, the initial input is added through the residual network structure. The full name of CSPNet is Cross Stage Partial Network, and it solves the problem of repeated gradients in other large convolutional network structures [32; 33; 34].
## 5 Discussion  
Clay sensitivity is not only important to the safety of geotechnical engineering, but it also influences the feasibility of such projects. However, it is difficult to classify clay sensitivity, which is greatly influenced by disturbance. There are more than two sensitivity categories under different classification charts. Moreover, the distribution of clay sensitivity categories is often imbalanced. Therefore, new methods are needed. However, XGBoost is rarely used for multiclassification problems, such as predicting the sensitivity categories of soft clays. Additionally, it is necessary to deal with imbalanced problems.  
In this study, NB, ANN, and XGBoost were used to predict the multiple classes of clay sensitivity. SMOTE was applied to address the imbalanced classes of data. Additionally, a set of performance indices were developed to evaluate accuracy.  
The evaluation indices of XGBoost incorporating SMOTE were better compared with those of other machine learning methods, based on the classification results. Compared with XGBoost_NoSmote, the mean AUC of classification for XGBoost improved by 9.9%, which indicates that SMOTE improves the model performance of imbalanced datasets. The best classification performance was achieved for high sensitivity, followed by low sensitivity and medium sensitivity. This study did not only investigate multiclassification with regard to clay sensitivity, but it also employed SMOTE to handle imbalanced issues. The results prove that the combination of XGBoost and SMOTE is a simple and quick way to classify imbalanced clay datasets. Furthermore, more accurate indices for evaluating the model performance, such as the AUC and F1 score, were applied to assess the models.  
However, this study has a few limitations. For the AUC of medium sensitivity (Table 3), all models performed slightly worse compared with other categories. The possible reason is that the medium sensitivity is between the low sensitivity and high sensitivity categories, which results in a particularly unclear boundary and affects the model performance. Other studies have also proven that the overlap between different classes can influence classification performance [54, 55]. Therefore, new methods should be developed to solve these issues.
# Investigation of Potential of GNSS-R Polarization: Theoretical Simulations
## 2 Model Development
### Spaceborne DDM Model  
Currently, there is no spaceborne GNSS-R payload that is oriented to GNSS-R polarization. However, spaceborne models can be adopted for analysis. In this section, we describe the adoption of the extended version (full polarization) of LAGRS, which is a simulator for the FY-3E GNSS-R payload [25; 31].  
Figure 1: Forward scatter alignment.  
LAGRS is an integral form of bistatic radar that is based on the ocean surface GPS scattering model During our development of LAGRS, we extended it for application to bare soil and vegetation. The main modifications involved converting the integration area, observation geometry, and target microwave scattering models. Using this model, we can describe the GPS and Beidou reflection power for a different time delay \(\hat{\tau}\) and Doppler frequency shift \(\hat{f}\):  
\[Y_{s}\Big{(}\hat{\tau},\hat{f}\Big{)}=\frac{T_{I}^{2}P_{T}\lambda^{2}}{\left(4 \pi\right)^{3}}\iint_{A}\frac{G_{T}\sigma^{0}G_{R}}{R_{R}^{2}R_{T}^{2}}\Lambda^ {2}(\hat{\tau}-\tau)sinc^{2}\Big{(}\hat{f}-f\Big{)}dA \tag{8}\]  
where \(Y_{s}\) is the GPS or Beidou reflection power, \(P_{T}\) is the transmitted power of the GNSS constellation, and \(G_{T}\) and \(G_{R}\) are the antenna gain of the transmitter and receiver, respectively. \(R_{R}\) is the distance from the receiver to the ground scattering point, \(R_{T}\) is the distance from the transmitter to the ground scattering point, \(\lambda\) is the electromagnetic wavelength, \(T_{I}\) is the integration time, \(\sigma^{0}\) is the bistatic scattering cross section, which can be calculated using the models described in Sections 3.1 and 3.2, \(\Lambda(\hat{\tau}-\tau)\) is the GPS correlation function (triangle function), \(\hat{\tau}\) and \(\tau\) are the time delays of the duplicated and incident signals, respectively, \(sinc^{2}\Big{(}\hat{f}-f\Big{)}\) is the doppler filter function, \(\hat{f}\) and \(f\) are the frequency of the duplicated and incident frequencies, respectively, \(A\) is the effective scattering area, which is approximative to the glistening zone, and \(dA\) is the integration area in zone \(A\). Figure 2 presents a flowchart of the LAGRS model. The bistatic scattering model is obtained from the previously mentioned random surface scattering model and the first-order RT model. The velocity and position of the transmitter and receiver can be used to obtain the geometry module. By combining the two modules, we can determine the final delay Doppler map (DDM) of the corresponding geophysical parameters.
# Body Balance Ability of Girls Practicing Cheerleading
###### Abstract
Changes also occur within the nervous system, and its maturation affects motor coordination, thereby improving the ability to control movements and reactions [13]. The intense development of the nervous system allows for controlling movements, posture, and balance. This occurs naturally; however, engaging in physical activity through various forms of exercise further enhances balance by building muscle mass, as well as supporting bone and nervous system development. Appropriate exercises and dance elements during training strengthen specific muscle groups. Studies conducted on athletes aged 11 to 13 years confirm the beneficial effect of increasing muscle mass on postural coordination [14]. Similar mechanisms have also been observed in children with developmental coordination disorder [15]. It is generally accepted that balance, in conjunction with coordination, plays a fundamental role in most sports disciplines. In dance, balance is a crucial element and can be divided into static and dynamic components. Static balance refers to maintaining the body's center of gravity within the base of support when in a fixed position, while dynamic balance pertains to sustaining postural stability during movement [16]. In dance sport, the ability to maintain balance is closely linked to the performance of various technical elements, as dancers must continuously regulate and adjust their movements. Athletes who train in this discipline at a professional level demonstrate high spatial awareness and the ability to orient themselves effectively in space. These skills, along with body flexibility, long-term training, and muscular strength, contribute to enhanced balance control. Postural balance is also influenced by the interaction of the visual, vestibular, and somatosensory systems. Dancers exhibit proficiency in utilizing one or more of these systems to regulate body position and prevent potential falls [17]. Despite the growing popularity of cheerleading as a sport discipline, scientific literature focusing on body balance analysis in youth cheerleaders remains limited. Most available studies concern artistic gymnastics or acrobatics, which, while sharing some elements with cheerleading, differ in training structure and performance goals. Existing cheerleading studies predominantly involve university-level or adult athletes. There is a distinct lack of biomechanical research on younger athletes (aged 8-14), who form a substantial segment of cheerleading participants in school and club contexts. The aim of this study was to analyze whether participation in cheerleading training is associated with improved postural balance in girls aged 8-14. It was hypothesized that older girls with longer training experience would exhibit better postural stability, as reflected by lower displacement and velocity of the center of pressure (C.O.P). A secondary objective was to explore differences in foot load distribution across age groups.
# AI-Based Smart Monitoring Framework for Livestock Farms
## 2 Related Work
The feeding behavior of cows in a feeding environment was identified using the DRN-YOLO technique based on YOLOv4 [3]. Another study was conducted using YOLOv5 to detect the major body parts of cows. A study was conducted using the YOLOv5-EMA model to detect the appearance of cows. Through this study, the performance of detecting small parts such as the head or legs was improved [9; 10]. It was carried out to identify abnormal conditions of cows' legs by detecting movement characteristics such as head shaking and posture duration measurement [11]. In another study, the monitoring of behaviors such as the lying down and standing of dairy cows on a farm using the Res-Dense YOLO technique was performed [12]. However, these methods often generate a large number of overlapping bounding boxes, requiring a postprocessing step known as Non-Maximum Suppression (NMS) to eliminate redundancies. This step can become a bottleneck, reducing the overall detection speed, which is problematic for applications requiring high speed and accuracy. To address this issue, a new approach called DETR (Detection Transformer) was introduced, which eliminates the need for manually crafted anchors and NMS [13; 14; 15; 16; 17]. Instead, DETR adopts an end-to-end Transformer-based architecture that uses bipartite matching and directly predicts a one-to-one set of objects. While DETR offers these architectural advantages, it also faces significant limitations such as slow training convergence, high computational cost, and challenges in query optimization [18]. Although subsequent improvements like Deformable-DETR, DAB-DETR, DN-DETR, and Group-DETR have been proposed to address these issues, these Transformer-based models still tend to have high computational requirements and are not fully optimized for real-time performance. However, RT-DETR (Real-Time Detection Transformer) emerges as a more suitable alternative for real-time object detection. It has many advantages such as query initialization, computational costs, and high performance suitable for real-time detection [18; 19; 20]. In this paper, we propose an AI-based intelligent monitoring system framework and implement a deep learning-based AI model to detect lesions and identify inactive Hanwoo cattle within the barn environment. We apply the RT-DETR model which enables the intelligent recognition of abnormal behaviors and situational awareness related to the individual maintenance behaviors of cattle. To validate the performance of RT-DETR, a comparative analysis was conducted against traditional YOLO-based methods. This research is the first application of RT-DETR to livestock behavior recognition, and it will contribute to enhancing the efficiency and productivity of livestock management by integrating intelligent technologies into existing smart farm systems that are primarily focused on environmental control and monitoring.
## 3 A Resilient Routing Protocol
### Routing Algorithm
#### 3.2.1 Algorithm Explanation
Additionally, the algorithm allows for the selection of lower-cluster-level nodes when higher-level clusters are insufficient or unavailable, ensuring flexibility in routing decisions and preventing potential bottlenecks. By following this structured sequence, the algorithm dynamically adapts to the changing conditions of MANETs, balancing objectives such as energy efficiency, latency, and reliability in routing decisions while maintaining clustering stability and adaptability.
# Asynchronous Chirp Slope Keying for Underwater Acoustic Communication
## 2 Materials and Methods
### Demodulation
#### 2.4.6 De-Multiplexing  
The last block of the demodulation chain re-assembles the \(N_{\text{lo}}\)-dimensional symbol sequences of each frame into a one-dimensional bit sequence. The length of the received bit sequence \(N_{\text{est}}\) is first truncated to multiples of \(8\), as the application is meant to send and receive data bytewise, hence  
\[N_{\text{est}}=8\left\lfloor\frac{N_{\text{lo}}\,N_{\text{sym}}}{8}\right\rfloor. \tag{42}\]  
The data is then de-multiplexed by reshaping the sequences \(d_{\text{rmx}}\) with \(n\) in the range \([1,\,N_{\text{est}}]\) to  
\[d_{\text{est}}[n] =d_{\text{rmx}}[k,l],\quad\text{where} \tag{43}\] \[k =\frac{n}{N_{\text{lo}}},\] \[l =n\text{ mod }N_{\text{lo}}.\]
## 1 Introduction
In conjunction with tracking, a contextualized video view projects the video in the approximate location and/or direction of its contents, considering the drone's location and orientation, resulting in a mixed reality view combining the virtual video feed with the real world. The main contributions of this work are as follows:  
* Gesture control for drones, supporting all drone and camera motions, including complex motions;
* Drone tracking in AR, based on cumulative Inertial Measurement Unit (IMU) readings and an initial relative pose calibration, while exploring visual-based drift correction;
* Visualization of the drone's camera feed in AR in context with the real world, considering the relative pose between drone and user, with the additional option to explore and integrate depth estimation for more accurate placement. * A unified solution encompassing all of the above, consisting of a HoloLens 2 user interface app, an Android UAV interface app, and a Kafka message broker. The rest of the paper is organized as follows: Section 2 discusses earlier work related to the presented solution, in particular regarding drone gesture control, drone tracking, and the visualization of information in AR; Section 3 presents the system's architecture, including hardware, software, communication, and data flow; Section 4 describes the primary technical tasks that were needed to realize the solution; Section 5 discusses the testing of the system, including subjective and objective measurements and feedback; and finally, Section 6 summarizes this work and outlines possible future developments.