esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Classification Map of CL Categories  
In contrast to conventional spectroscopy, the major advantage of HSI is the spatial distribution information involved. Since both detailed spectral and spatial information was included, the developed multivariate model obtained can be applied back to the hyperspectral images to map quality properties and/or the chemical distribution of pixels within each sample. In the literature, several researchers have implemented HSI successfully for meat classification in visualization forms [25; 49; 50]. For example, in predicting classifying pork muscle into three classes--pale, soft, and exudative (PSE), dark, firm, and dry (DFD), and reddish-pink, firm, and non-exudative (RFN) by HSI, [PERSON] et al. [25] created classification maps to present the diverse grades in all pixels within the samples. [PERSON] et al. [49] created the classification maps of different kinds of red meat (pork, beef, and lamb muscles) using a PLS-DA model. [PERSON] et al. [50] built the classification maps using HSI system to classify and observe fresh and frozen-thawed pork. The overall results suggested that HSI could be implemented as a key component of a computer-integrated system to generate classification maps for the discrimination purposes. In this study, classification maps were generated tentatively by transferring the CARS-PLS-DA model to the pixels in hyperspectral images at selected wavelengths. Although it is hard to differentiate CL categories by the naked eye only by their RGB images in Figure 8 (Figure 8a,d), the spatial CL variation within a fillet could be identified via the classification maps. In pixel-level classification maps (Figure 8b,e), different categories representing high-CL and low-CL were visualized in red and green, respectively. The reference CL values were 29.08% and 16.31% for fillet A and B, respectively. As presented, the corresponding general CL category of fillets A and B can be identified by observing the color of the majority of the pixels (exceeding 50%) within each fillet to make a correct recognition. Furthermore, the classification maps at the fillet-level were also generated (Figure 8c,f) to show the finally classified results which could be used to automate the inspection in final quality grading. Overall, classification maps could also be successfully generated for CL classification in chicken fillets. HSI reveals that the advantages (which could benefit poultry meat producers and processors, retailers, and consumers alike) cannot be achieved by either conventional imaging or spectroscopy.  
Figure 7: Performance of the full-wavelength PLS-DA model and the CARS-PLS-DA model. (**a**) Calibration set of the full-wavelength PLS-DA model; (**b**) Prediction set of the full-wavelength PLS-DA model; (**c**) Calibration set of the simplified CARS-PLS-DA model; (**d**) Prediction set of the simplified CARS-PLS-DA model.
# Effective Dereverberation with a Lower Complexity at Presence of the Noise
###### Abstract  
Adaptive beamforming and deconvolution techniques have shown effectiveness for reducing noise and reverberation. The minimum variance distortionless response (MVDR) beamformer is the most widely used for adaptive beamforming, whereas multichannel linear prediction (MCLP) is an excellent approach for the deconvolution. How to solve the problem where the noise and reverberation occur together is a challenging task. In this paper, the MVDR beamformer and MCLP are effectively combined for noise reduction and dereverberation. Especially, the MCLP coefficients are estimated by the Kalman filter and the MVDR filter based on the complex Gaussian mixture model (CGMM) is used to enhance the speech corrupted by the reverberation with the noise and to estimate the power spectral density (PSD) of the target speech required by the Kalman filter, respectively. The final enhanced speech is obtained by the Kalman filter. Furthermore, a complexity reduction method with respect to the Kalman filter is also proposed based on the Kronecker product. Compared to two advanced algorithms, the integrated sidelobe cancellation and linear prediction (ISCLP) method and the weighted prediction error (WPE) method, which are very effective for removing reverberation, the proposed algorithm shows better performance and lower complexity.  
speech enhancement; microphone array; dereverberation; noise reduction; linear prediction +
Footnote †: journal: Applied
## 4 Numerical Results  
For all cases analyzed in this section, the optimization problem (4) was solved using the CVX, which is a system that works under the MATLAB environment for modeling and solving convex optimization problems [40]. The MATLAB-CVX code developed to generate the results shown hereafter is given in the Appendix.  
For all results presented in this section, the initial energy in the sensor nodes' batteries, the energy consumptions and sporadic battery recharge values are not actual ones, but scaled so as to anticipate the network death with respect to a real one, thus preventing the simulations of lasting prohibitively large intervals. Such a scaling has been also adopted for instance in [21], and though it changes the absolute lifetimes, it does not affect the conclusions regarding relative lifetime improvements or comparisons.  
The results regarding the non-optimized network were obtained by setting identical equivalent activity levels \(X^{\prime}_{n,f}(k)=1/N\) to all sensor nodes during the whole interval of analysis, with \(X^{\prime}_{n,f}(k)\) being the element in the \(n\)-th row and \(f\)-th column of the equivalent activity level matrix \(\mathbf{X}^{\prime}(k)\) associated with the \(k\)-th block of \(F\) frames. Notice that these activity levels are nothing more than the average of the activity levels of all sensor nodes, which add up to one, during all optimization events up to the death instant of the network. As a consequence, the sensor nodes' residual energies in the non-optimized network vary as determined by the their consumptions computed according to \(\mathbf{X}^{\prime}(k)\mathbf{B}(k)\), with \(\mathbf{B}(k)\) being the same maximum consumption matrix considered in the optimized network.  
Figure 5: Time line illustrating the operation of Algorithm 1.
# A Risk Assessment Analysis to Enhance the Security of OT WAN with SD-WAN
## 2 Related Works
### SD-WAN-Related Research  
#### 2.2.1 SD-WAN Non-Security Related  
As SDN gained popularity, the need arose to extend the network design to cloud environments and expand its reach across wider WANs. This demand led to the development of SD-WAN, which has since become the focus of numerous studies examining its performance and compatibility with evolving networks. Through a systematic review, the authors in [36] compared the architecture of SD-WAN with that of legacy WANs, providing insights into how SD-WAN overcomes the limitations of traditional networks in response to market demands and emerging network techniques and protocols. Another paper introduced a backup and recovery mechanism utilizing SD-WAN, proposing a method for Distributed Data Backup and Recovery (DDBR). The suggested SD-WAN configuration is implemented across multiple network switches for backing up control plane data [49]. Another backup scheme examines load-balancing optimization of WAN links through OpenNetMon, an open-source dashboard, to enhance bandwidth utilization and facilitate traffic engineering. This article provides an in-depth analysis of SD-WAN features and showcases its effective data backup capabilities [50]. With the increasing demand for cloud networks, the application of SD-WAN in these environments has become widespread. Consequently, one study examined how effectively SD-WAN can enhance network connectivity through its flexibility, scalability, and improved security related to traffic-routing optimization. Another study evaluated the efficiency of SD-WAN in resource provisioning and green energy scheduling within multi-cloud environments [48; 51] (pp. 5645-5656).
## 8 Routing Protocols of Multi-UAV Systems
### Hierarchical-Based Routing Protocols  
In these types of protocols [79; 80], the network is divided hierarchically into clusters. Each cluster is used for a specific function and has a head that facilitates inter-cluster communication through the other heads, see Figure 18. The communication process is supported by proactive routing protocols, while changes in the network or failures are managed by reactive protocol processes. The most popular of these routing protocols are Mobility Prediction Clustering (MPC) [79] and the Clustering Algorithm for Networking (CAN) [80].  
Figure 17: Geographic position routing paradigm.  
CNU is a clustering-based protocol that divides the network into clusters starting with the hierarchical level closest to the GCS. Thus, in an iterative way, new clusters are formed from the previous level's clusters until they cover the entire network. For each cluster, UAVs elect a cluster head based on their power law distributions in the graph network. The cluster head will be responsible for calculating the route to effectively broadcast messages inside the cluster. Thereafter, it shares them with other clusters through the cluster heads.  
MPC is an adapted version of CAN for highly mobile FANETs. The MPC protocol uses mainly the Doppler shift method to compute the velocity and movements of a couple of UAVs. The clustering is done according to the state of each UAV: orphan (stateless), member (cluster member), or head (cluster head). Initially, all UAVs are in the orphan state. Afterwards, each UAV sends a \"HELLO\" message containing, in its header, the information relative to its velocity, direction, and identifiers. Depending on the change in wave frequency (obtained from Doppler shift) between the transmitter UAV that moves compared to the receiver UAV, a state (member or head) is assigned to each UAV until the clusters are completely formed.
# Enhancing Robustness of Per-Packet Load-Balancing for Fat-Tree
## 6 Related Work
### Flow-level load balancing
When a failure occurs, the controller prunes the spanning trees that are affected by the failure or enforces a weighted scheduling algorithm over the spanning trees. Packet reordering is handled by the GRO (Generic Receive Offload) handler modified to reduce computational overhead. DRILL (Distributed Randomized In-network Localized Load-balancing) [12] is a datacenter fabric for Clos networks which employs per-packet decisions at each switch based on local queue occupancies and randomized algorithms to distribute load. DRILL compares queue lengths of two random ports and the previously least-loaded port, and sends the packet to the least loaded of these. To handle asymmetry, DRILL partitions network paths into groups, each of which is symmetric and applies micro load balancing inside each path group. TTC (Thresholded Two-Choice) [13] has been proposed to improve downward load-balancing in fat-trees based on the observation that DRILL [12], which well balances upward traffic, may perform poorly on downward load balancing since local path decisions are agnostic to downward congestion. Similar to DRILL, TTC also uses a two-choice algorithm. However, to improve downward load-balancing, TTC makes local routing decisions from two choices: a default path using the deterministic D-mod-k scheme [15] and a random choice. The random choice is selected only when the load of the default path exceeds that of the random choice by a threshold. However, TTChas yet to handle link failures and packet reordering. More recently, VMS (Virtual Multichannel Scatter) [19] has been proposed. VMS is a packet-level load-balancing scheme which runs in the virtual switch layer.
## 1 Introduction
Moreover, previous studies used either a single-date image (e.g., [27; 29; 30; 58]) or a temporal mosaic that produced a single aggregated reflectance spectrum over a time series (e.g., [25; 28; 35]), but did not use multiple dates to represent changes in the soil surface in the models. The present study aimed to increase the accuracy of SOC content predictions using two complementary mechanisms: (i) Enriching the information considered by combining remote sensing time series and laboratory spectral measurements; (ii) Improving model accuracy using machine-learning algorithms. To this end, we used Sentinel-2 remote sensing data of bare soil and combined them with Sentinel-1 SAR-derived data and indices derived from Vis-NIR laboratory spectra to predict SOC content in an agricultural study area. We also compared the performance of PLS regression, RF, and DNN models in predicting SOC content.
## 3 Results
### Damage Model  
#### 3.2.1 Regression Curves  
The damage model, proposed in this paper, is based on displacement ductility. Figure 4 demonstrates that the defined ductility can be effective in evaluating the masonry brick degradation under the F/T cycles. The probabilistic damage index of masonry brick wallets due to F/T cycles can therefore be expressed as [31,32]:  
\[DI(n)=1-\frac{\mu(n)}{\mu_{0}}, \tag{5}\]  
where \(\mu(n)\) and \(\mu_{0}\) are the ductility of damaged (at \(n\) F/T cycles) and intact (unconditioned, at \(n=0\) F/T cycles) samples, respectively. Since, in some cases, \(\mu_{0}\) can be less than \(\mu(n)\) (e.g., M3a at lower number of F/T cycles for standard approach), \(\mu_{0}\) is defined as \(\mu_{MAX}\) and consequently the damage index is always less or equal than 1. Total damage is therefore represented by a value of 1.  
For M3a wallets, two possible regression curves are shown. The variant with quadratic regression (dotted red line) corresponds to a monotonically increasing function, which can, however, very quickly exceed the maximal value of the damage index. Therefore, it makes sense to propose a variant with cubic regression (solid red line), which approximately asymptotically approaches the maximum value of the damage index.
# Design and Stability Analysis of Six-Degree-of-Freedom Hydro-Pneumatic Spring Wheel-Leg
## 4 Analysis of Horizontal Rotational Stability
### Analysis of Spring Correction Force
#### 4.2.2 Displacement and Tensile Force Variations of Right Tension Spring  
At the static equilibrium position, the horizontal distance between the upper and lower fixed ends of the right tension spring is given as  
\[b_{t0}=\sqrt{s_{t}^{2}-h_{t}^{2}}. \tag{39}\]  
The angle between the line connecting the lower fixed end of the right tension spring to the center of rotation and the line connecting the upper fixed end to the center of rotation at the static equilibrium position is given as  
\[\varphi_{t}=\text{arcos}\left(\frac{l_{c}^{2}+r_{c}^{2}-b_{t0}^{2}}{2l_{c}r_{ c}}\right). \tag{40}\]  
After rotation by an angle \(\theta\), the horizontal projection length of the right tension spring is  
\[b_{t1}=\sqrt{l_{c}^{2}+r_{c}^{2}-2l_{c}r_{c}\cos(\varphi+\theta)}. \tag{41}\]  
The new length of the right tension spring is given as  
\[s_{t2}=\sqrt{b_{t1}^{2}+h_{t}^{2}}. \tag{42}\]  
The rotational lever arm corresponding to the right tension spring is given as  
\[\left\{\begin{aligned} \beta_{t}&=\text{arcos} \left(\frac{l_{c}^{2}+b_{t1}^{2}-r_{c}^{2}}{2l_{c}b_{t1}}\right).\\ L_{t2}&=l_{c}\sin\beta_{t}\end{aligned}\right. \tag{43}\]  
By combining the above equations, the tensile force of the right tension spring after rotation by an angle \(\theta\) is expressed as  
\[F_{t2}=k(s_{t}-s_{t2})=k\sqrt{l_{c}^{2}+r_{c}^{2}-2l_{c}rccos(\varphi+\theta) +h_{t}^{2}}. \tag{44}\]  
Thus, the corrective rotational torque provided by the right tension spring is given as  
\[N_{t2}=-F_{t2}L_{t2}\frac{b_{t1}}{s_{t2}}. \tag{45}\]  
Summarizing the above equations, with the variation of the horizontal rotation angle \(\theta\), the spring correction torque is given as  
\[\text{N}_{t}=2(N_{t1}+N_{t2})=2\bigg{(}F_{t1}L_{t1}\frac{a_{t1}}{s_{t1}}-F_{t2 }L_{t2}\frac{b_{t1}}{s_{t2}}\bigg{)}. \tag{46}\]
# Detection of Waste Containers Using Computer Vision
## 2 Related Work  
The main focus of this study is general object detection and classification, as, with this project, there is a concern with differentiation among types of waste containers [4].  
Currently, there are several approaches to the task of object recognition and classification. There is a requirement to decide amongst the different object recognition techniques: the leading ones in terms of research and results, in their respective fields, are feature-based methods and convolutional neural nets [5].  
The traditional method carries some constraints. The feature detectors and descriptors need to be manually engineered to encode the features. This task is difficult due to the variety of backgrounds, the previously stated transformations, their appearance, and their association with simple and shallow architectures. This approach involves picking features (points) in images that are distinct, repeatable, in great quantities, and accurately localized. Subsequently, these features are described in a way that makes them invariant to several image transformations. Features obtained this way are denoted low-level features, and it is harder to acquire semantic information (information that is useful to recognize objects) from these features [6]. The majority of waste containers have features that differentiate them. By constructing a database of these features, we can obtain a satisfactory detection rate. The main problem lies in the low flexibility of this approach. The power of this approach comes from its application to limited and specific domains, as seen in [7; 8].  
In the last decade, convolutional neural networks (CNNs) have received more attention and interest from the computer vision community. CNNs constitute a class of models from the broader artificial neural networks field, and they have been shown to produce the best results in terms of efficiency and accuracy [9]. Their advantages versus traditional methods can be summarized into three main points [6]: features are represented hierarchically to generate high-level feature representation; the use of deeper architectures increases the expressive capability; and the architecture of CNNs permits the conjunction of related tasks. The architecture itself is also similar to the way an eye works. Similar to an eye, CNNs start by computing low-dimensionality features and end up computing high-dimensionality features that are used for classification. Applying CNNs, specifically to waste containers, provides a major increase in flexibility in the detection and production of results, but only if a proper dataset is obtained and the processing power is reasonable, being the last point the downfall of this approach.  
This section is divided into two main topics, each one regarding the previously mentioned methods, and also discusses open research issues.
# XTTS-Based Data Augmentation for Profanity Keyword Recognition in Low-Resource Speech Scenarios
## 3 Experimental Results and Comparisons of Various Algorithms
### Performance Evaluation on RHS Dataset for the Proposed-I Algorithm  
We conducted classification experiments using a pure RHS dataset [4], evaluating the impact of different training, validation, and test set ratios of 5:3:2, 6:2:2, and 7:1:2 on model performance under fixed test set conditions. This approach establishes a baseline performance for the dataset without any adjustments. The experimental results indicate that as the training set proportion increases, the model's average accuracy rises, peaking at 61.33%, while the average loss decreases correspondingly, reaching a minimum of 2.258. Detailed results are presented in Table 5.
## 5 Kinematic Assessment of Movement Performance
### Inertial Measurement Unit--IMU
#### 5.1.1 IMUs in Biomechanics, Health Monitoring, Injury Protection  
Body motion study in physical activities can lead to understanding motor behavior, action, and reaction with the highest efficiency and injury prevention [140]. In [22; 141], the utilized devices present meager weight, and their minimal size has made it possible to carry out several examples of unobtrusive wearable systems that have found interesting applications in monitoring the health or body motion in many sports activities avoidingany imbalance posture for the player [142]. Recording health aspects on the training field or during matches rather than inside a lab can reduce the risk of health damage and improve awareness of the athlete's actual conditions. Moreover, the lower mass and weight of these wearable sensors are helpful in not breaking the players' attention and focus, allowing a more realistic and reliable measure [140; 143]. For example, accelerometers can measure variables such as proper acceleration, which is useful for assessing a player's agility and predicting the prevailing foot activity in team sports, including volleyball and football [123; 124; 144]. Furthermore, IMU devices are widely used to assess the arm joints' acceleration in combat sports, such as karate, where they have been used to take measurements in the punching phase. In addition, in ball-throwing sports such as volleyball, baseball, and cricket, the IMUs are used to evaluate hip, elbow, and wrist angular acceleration in the smash and pitching phase [144; 145; 146; 147].  
Another application of wearable IMU sensors is the estimation of kinematic parameters strokes in racquet sports. A recent study [148] examined the kinematic parameters and their variability in the top-spin forehand stroke of seven elite table tennis players. The study utilized a wireless IMU placed on the hand of each subject. Researchers could use this technology to analyze and measure the movement characteristics and variations in the players' top-spin forehand technique.  
In the selected articles, wearable sensors were used to measure health aspects in running sports such as football to monitor the health aspect of players via wearable health monitoring systems. These systems are equipped with multiple sensors attached to the skin surface to measure some health parameters, including heart rate and breath rate, blood pressure, and human movement, to obtain the list of health information of the players to increase health condition awareness and injury prediction and prevention. Finally, injury prediction is estimated by players' injury records and the collected data via GNSS systems, such as velocity, covered distance, and acceleration during a football season [22; 141].