esa-sceva/satcom-chunk-collection huggingface dataset:
# Use of Lignin, Waste Tire Rubber, and Waste Glass for Soil Stabilization
## 4 Results and Discussion
### Results of the Unconfined Compressive Strength  
The results of the uniaxial compressive strength test are shown in Figure 6. The uniaxial compressive strength (q\({}_{\text{u}}\)) increases with the addition of glass powder and rubber tires. The increase in strength was also observed with increasing curing time. At the end of the 7- and 28-day curing time, the highest strength values for the samples with glass powder and rubber tire additive were 6.07 kg/cm\({}^{2}\) and 6.81 kg/cm\({}^{2}\) for the sample (S7) with 20% GP + 3% RT additive. At the end of the 28-day curing period, a positive increase of 342% was achieved compared to the control sample. Although the strength value of the sample (S8) with 25% GP + 4% RT additive decreased compared to the sample (S7) with 20% GP + 3% RT additive, a strength increase of 333% was achieved compared to the control sample.  
Figure 6: Results of uniaxial compressive strength at different curing times.  
The samples with added glass powder and lignin also showed an increase in strength compared to the samples without additives. However, this was not as strong as with the RT additives. The highest strength value was achieved in the sample (S1) with 5%GP + 5%LG additive. At the end of the 28-day curing period, a positive increase of 150% was achieved compared to the control sample. Although the lowest strength value among the samples with additives was achieved in the sample with 15%GP + 15%LG additive (S3), a 38% increase over the control sample was still achieved at the end of 28 days.  
According to [PERSON] et al. [53], industrial waste can be used effectively for soil stabilization. In some cases, this can also be used instead of conventional binders to improve soil properties. The use of industrial waste as soil stabilizers can contribute to significant cost savings and environmental protection. It has been shown that industrial waste (fly ash, marble dust, boron dust, granite dust, etc.) used in varying proportions has a positive effect on the physical and mechanical properties of clay soils. It can be seen that the results of tests such as CBR and UCS are consistent [54]. In some studies, although the strength values were high at optimum additive ratios, a decrease in strength values was observed with an increasing additive ratio [55; 56].
# A Proposal of a Troposphere Model in a GNSS Simulator for VANET Applications
## 2 Related Work
### Main Paper Contributions  
In this work, we focused our attention on the absolute positioning system such as GPS, considering the simulation environment integrated with real devices in a vehicle scenarios. In particular, our main contribution regards:  
* The introduction of a troposphere model in the GPS-SDR-SIM, an open-source simulator, in order to add the delay of the transmitted signal taking into account the troposphere.
* Co-simulation proposal where simulation modeling is integrated with real equipment and real data elaborated by GPS receivers. The proposed approach can be applied by researchers or industry to simulate positioning errors on realistic path working in the lab. and reducing performance evaluation costs.  
* The goodness of the modeling proposal is proved by a set of simulation tests carried out at three different latitudes. It has been evaluated in terms of longitude and altitude errors.
## 2 Wind Power, Photovoltaic Power and Load Power Forecasting Model
### Stacking Ensemble Learning Model
#### 2.2.5 Knn  
The main idea of KNN is that each sample can be represented by its \(K\) nearest neighbors. When performing regression tasks, KNN selects the \(K\) data points closest to each sample from the training data set, takes the average of their label values, and uses this average as the forecasting value for corresponding sample. This paper uses the most commonly used Euclidean distance [31] to measure the distance between each sample in what follows:  
\[d=\sqrt{\left(x_{1}-y_{1}\right)^{2}+\left(x_{2}-y_{2}\right)^{2}+\cdots+ \left(x_{n}-y_{n}\right)^{2}} \tag{12}\]  
where (\(x_{1}\),\(x_{2}\),\(\ldots\), \(x_{d}\)) and (\(y_{1}\),\(y_{2}\),\(\ldots\),\(y_{d}\)) denote the coordinates of any two points in \(n\) dimensional space.  
KNN is an algorithm with simple principles and is easy to implement and understand. It also has the advantage of being insensitive to outliers. However, since KNN needs to calculate the distances from one sample point to all other sample points, it has large computational overhead and high space complexity, and often suffers from decreased accuracy when dealing with high-dimensional data.
## 4 Discussion  
The proposed DDFM algorithm provides an avenue for resolving the problem of motional pattern recognition. The successful classified results of motional patterns of Lying-to-TL and Lying-to-TR cases in specific physical spaces have been respectively shown in Figures 4 and 5, and that in time domains has been shown in Figure 3. An algorithmic flow is provided as well. By mapping the time signals into a 2D physical space, the most probable cluster number can be estimated using the trend curves of the global Hamiltonian functional and the most probable cluster boundaries can also be determined by finding the lowest energy levels in the LDF morphologies. Thus, the DDFM is a self-consistent method for pattern recognition.  
In the proposed experimental scheme, the rotating directions of the sensor architecture were used as prior information for the algorithmic estimations, so that the most probable states of motional patterns can be definitely determined by combining the features extracted from PA 2-PA 3 scattered plots, i.e., the proposed inter-state features. By simultaneously considering the direction of state migration in the PA 2-PA 3 plot of Figure 4c and the sensor architecture, the progressively increasing curve exhibits an increase in both of the electric voltages in PA 2 and PA 3. Then, this motional pattern was classified to the Lying-to-TL case. A similar deduction can be applied to the Lying-to-TR case in Figure 5c. Therefore, the motional pattern recognitions can be correctly and easily recognized using the proposed DDFM algorithm. The classified results of the patterns can be remapped into the time-dependent space, and the classified time signals of the cases are respectively shown in Figure 3. It  
Figure 6: A simulation of elderly and infant turning-over monitoring. By simultaneously combining the features extracted from the Lying-to-TL and -TR cases, the states of face-up and face-down can clearly recognized using the proposed DDFM algorithm. The inclination might be caused by the unbalanced natural motional strength of the subject.  
is worth discussing the limitation of the proposed algorithm. As aforementioned, the non-ideal effects, as exhibited in Figures 4d and 5d, will occur in the estimation procedure of the global Hamiltonian functionals. In these cases, the employment of the estimator and the chaotic level of the data distribution might significantly limit the accuracy of cluster number estimation and that of the cluster boundary definition as well.
## 4 Event-Based Programming Model With Geometric Spatial Semantics
### Syntax
: _An event model with geometric semantics, \(Ge\), is defined as a 4-tuple:_  
\[e=\langle T_{dt},T_{last},Exp,\Delta M,obj,P\rangle\]  
_where \(T_{dt}\), \(T_{last}\), and \(P\) are defined in Definition 7; \(Exp\) is the expression for the solid representation of the boundary function \(\pi\); \(\Delta M\) represents the transformation function of the object of operation, obj, relative to \(SE(3)\) in the world coordinates (see Equation (1)); and obj can be either a point or a vector._  
\[\Delta M= \begin{cases}R(obj)+v(obj),&\text{if obj is a point}\\ R(obj),&\text{if obj is a vector}\end{cases}  \tag{1}\]  
For example, consider a case in which two robot arms have the same object of operation, \(obj\). As the two robots consider only their own coordinate systems when programmed separately, it is necessary to consider how the attitude and position of \(obj\) should be transformed during cooperative operation. \(\Delta M\) represents a robot's action primitive, \(ap\), acting on \(obj\) rather than the actual physical location of the object. The receiver can estimate the position of \(obj\) by running ap virtually. Therefore, if the two robots each calibrate the initial position of \(obj\) with respect to the world coordinate system and then transmit only their respective action primitives acting on the target point, then each robot can calculate the position of \(obj\) relative to its own coordinate system. Note that the action primitives of both robots are elements in \(SE(3)\). Models in the form of \(Ge\) are designed for both \(EI\) and \(EO\). The expression \(Exp\) for input events (\(Gei\)) is a statement that describes the obstacle region \(\mathcal{O}\), i.e., \(Exp\equiv\Delta M(\mathcal{O})\). In contrast, the expression \(Exp\) for output events (\(Geo\)) describes the action space \(W(\tau_{t})\), i.e., \(Exp\equiv W(\tau)\). Note that the spatial properties described by the formula \(Exp\) depend on the motion planning algorithm, e.g., C-space motion planning [20].
## 3 Results and Discussion
### Prevalence of Other Enterobacteriaceae Genera  
Twenty-five different strains belonging to nineteen Enterobacteriaceae genera were isolated from camel, cattle, and sheep samples obtained from slaughterhouses and butcher shops, as illustrated in Table 2. Figure 3 shows the most predominant bacterial genera occurring in the samples, where _E. coli_ was highest with 84 (70%) followed by _Salmonella_ spp. with 48 (40%), _Proteus_ spp. with 15 (12%), and _Citrobacter_ spp. with 8 (7%) among all samples. Other genera including _Raoultella_ spp. and _Serratia_ spp. were also identified in four (3.2%) samples. _Klebsiella_ spp., _Stigella_ spp., and _Yersinia_ spp. were also confirmed in meat samples at 1.6%, 0.83%, and 0.83% respectively. The occurrence of pathogens belonging to the Enterobacteriaceae family including _Salmonella_ spp., _Shigella_ spp., and _Yersinia_ spp. in meat and meat products may pose significant risks to human health. These bacteria cause foodborne illnesses, leading to severe complications and even death, particularly in vulnerable populations like young children, the elderly, pregnant women, and immunocompromised individuals [38]. Despite the common practice in Saudi Arabia of cooking meat at high temperatures considered sufficient to eliminate any present pathogens, it does not guarantee a reduction in the risk associated with cross-contamination. This is particularly concerning in the context of other types of food, such as fruits and vegetables, which are often consumed raw. Thus, the potential for cross-contamination presents a significant risk to other foods, underscoring the need for attention and precautionary measures [7].  
Figure 3: The most predominant bacterial genera detected in camel, cattle, and sheep carcasses and meat cuts. All values are based on the total samples (\(n=120\)).
###### Abstract  
This study aimed to examine the effects of black maca supplementation on isokinetic muscle function and inflammatory markers in athletes and to extend these findings to non-athletes. The study involved 24 male participants, including 16 elite athletes (soft tennis and table tennis players) and 8 non-athletes (university students). Participants consumed capsules containing 2.5 g of 100% concentrated black maca extract over a 12-week period. Isokinetic muscle performance and physical fitness (strength, muscular endurance, flexibility, power, agility, cardiovascular endurance) assessments were conducted at baseline and after 12 weeks of supplementation. Two-way within-factor ANOVA showed a significant group \(\times\) time interaction for 120\({}^{\circ}\)/s flexor movements (\(p<0.05\)). Paired \(t\)-tests demonstrated significant improvements in 30\({}^{\circ}\)/s and 120\({}^{\circ}\)/s extensor and flexor movements in both athlete groups (\(p<0.05\), \(p<0.01\)). Similarly, significant enhancements were observed in the non-athletes for the 30\({}^{\circ}\)/s flexor and 120\({}^{\circ}\)/s extensor and flexor movements (\(p<0.01\)). Furthermore, reductions in interleukin-6 (from 137.9 \(\pm\) 8.8 to 132.7 \(\pm\) 4.6, \(p<0.05\)) and tumor necrosis factor-alpha (from 274.1 \(\pm\) 13.4 to 264.2 \(\pm\) 3.2, \(p<0.05\)) were noted in the soft tennis group. The table tennis group also showed significant decreases in interleukin-6 (from 135.9 \(\pm\) 4.7 to 131.3 \(\pm\) 2.5, \(p<0.01\)) and tumor necrosis factor-alpha (from 282.1 \(\pm\) 19.2 to 267.0 \(\pm\) 6.4, \(p<0.05\)). No significant changes were observed in the non-athlete group. Black maca supplementation may enhance isokinetic muscle function in elite athletes by reducing muscle contraction fatigue and improving anti-inflammatory responses.  
black maca; isokinetic; muscle; inflammation; athletes 2024
# Real-Time Hybrid Navigation System-Based Path Planning and Obstacle Avoidance for Mobile Robots
## 1 Introduction
### Related Work
#### 1.2.2 Moving Obstacle-avoidance Approach  
Analysis of the time-space relative trajectory of the robot and obstacle is employed in [9]. The result was obtained by finding the intersection between reachable cone and avoiding line that clearly demonstrated in [9]. However, the shape of the reachable cone depends on the robot's linear speed during the robot's movement on a local path. In order to obtain the optimal solution between many criteria, [PERSON], et al., used like-WSM which takes the distance between the robot and obstacle, time to reach to the minimum distance between the robot and obstacle, reachable maximum linear speed and offset angle between the robot heading and the robot via point direction into account. [PERSON], et al., proposed mobile robot navigation in dynamic environments by using time-varying harmonics APF in [10]. A fuzzy controller used to follow the given path and avoid obstacles was introduced in [11]. Fuzzy is a common approach of many control tasks of robots since it is flexible and has the capability to handle problems with imprecise and incomplete data. [PERSON], et al., attempted to indicate the evading static obstacle ability. With the proposed approach in [12], the robot also can avoid moving obstacles in some cases: overtaking, directly passing, but cannot have a good performance in the arbitrary situation. A few recent works have come up with novel methods such as no-target obstacle-avoidance for mobile robot [13]. [PERSON], [PERSON], et al., introduced the No-Target Bug1 and No-Target Bug2 algorithms for online-path planning. Since these algorithms are based on APF, they hardly adapt to the dynamic environment where the moving obstacles are presented.  
In our case, we proposed an analysis time-space relative trajectory method for determining collision and weighted-sum model for obtaining optimal local path since it is simple, fast and results are acceptable.
## 2 Power System Model
### Adjustable-Speed Pumping Generator (ASG)
#### 2.4.1 ASG Model  
ASGs have a similar structure to those of wound-rotor induction generators, and their stators and rotors are both equipped with three phase windings. To enable adjustable speed operation in a pumping generator, variable frequency (slip frequency) three-phase AC currents are supplied to the rotor windings to generate a magnetic field rotating on the rotor. Since the summation of the rotating speed of the magnetic field and the generator rotor speed is equal to the speed corresponding to the system frequency, it becomes possible for a generator to operate in synchronization with the grid frequency. Consequently, adjustable-speed operation becomes possible by controlling the velocity of a rotating magnetic field, i.e., the frequency of the excitation current supplied to the three-phase rotor windings. Though a cycloconverter was adopted as an excitation current source, a self-commutated inverter such as an IGBT PWM inverter is currently often used. In this study, a self-commutated inverter was considered as an excitation current source.
## 2 Introduction of Relevant Domain Knowledge
### Elm  
Due to the outstanding advantages of the extreme learning machine (ELM), such as fewer training parameters, fast learning speed, and strong generalization ability, its theory and application have been extensively studied in the past decade [55; 56]. In simple terms, the ELM model is divided into two main stages: random feature mapping and linear parameter solving.  
According to the literature [57; 58], the specific principle is summarized as follows.  
The network structure of ELM mainly consists of three parts: the input layer, the hidden layer, and the output layer.  
Assume a given training set \(\mathbf{X}{=}\left\{x_{i},t_{i}|x_{i}\in R^{N},t_{i}\in R^{L},i=1,2,3,\dots,N\right\}\), where \(\mathbf{x}_{i}\) represents the \(i\)th input sample, \(t_{i}\) represents the \(i\)th label corresponding to each sample, and the set refers to all the training data.  
The hidden layer, which is the middle layer, is fully connected to the input layer. The output of the hidden layer is \(\mathbf{H}(\mathbf{x})=[h_{1}(\mathbf{x}),h_{2}(\mathbf{x}),\dots,h_{K}(\mathbf{x})]\), where \(h_{k}(\mathbf{x})\) is the output of the \(k\)th rule layer. \(K\) is the number of nodes in the hidden layer \(h_{k}(\mathbf{x})=g(\mathbf{w}_{\mathbf{k}},b_{k},\mathbf{x})\), where \(g(\mathbf{w}_{\mathbf{k}},b_{k},\mathbf{x})\) is the activation function, \(\mathbf{w}_{\mathbf{k}}\) and \(b_{k}\) are the hidden layer node parameters. The solution of the weight value \(\mathbf{w}\) and the deviation \(\mathbf{b}\) is complete.  
The output of the single hidden layer feedforward neural network ELM is \(f_{K}(\mathbf{x})=\sum\limits_{k=1}^{K}\beta_{k}h_{k}(\mathbf{x})=\mathbf{H}(\mathbf{x})\beta\), where \(\beta=[\beta_{1},\beta_{2},\dots,\beta_{K}]^{\mathrm{T}}\) is the output weight of the hidden layer (\(K\) nodes) and the output layer (\(L\) nodes, \(L\geq 1\)). We can evaluate the training error with \(\mathbf{H}\beta\) and sample label \(\mathbf{T}\) by minimizing their square difference. The objective function is \(\min\lVert\mathbf{H}\beta-\mathbf{T}\rVert^{2},\beta\in R^{K*L}\), that is, the weight (\(\beta\)) connecting the hidden layer and the output layer is solved by minimizing the approximate square difference. The solution that minimizes the objective function is the optimal solution. When the training error is minimized, the output weight \(\beta\) is solved.
###### Abstract
In contrast to typical HSI devices, which collect narrowband information at high resolution (\(\sim\)1 nm), we envision simple devices that collect information at just a small number of wavelengths (\(\sim\)10-15) using inexpensive light-emitting diodes (LEDs) or filters. Not only will the reduction of the number of sampled wavelengths reduce the complexity and cost of these devices, but it will also provide additional benefits such as reduced data collection time, power consumption, and digital storage constraints. Wavelength selection for hyperspectral technologies is a well-studied problem. [PERSON] et al. [20] provide a thorough review of the state-of-the-art in wavelength selection algorithms for biomedical imaging applications and offer their own selection algorithm based on a domain adaptation technique. [PERSON] et al. [21] propose a wavelength selection for general chromophore concentration estimation based on a novel nonlinear least squares algorithm, which performs selection by maximizing the singular values of a scattering-modulated absorption matrix. While these techniques have shown high accuracy in tissue oxygenation estimation in general, none of these have focused in particular on the ability to achieve this accuracy across all skin types. This paper shows proof of concept for a new set of algorithms for selecting narrow wavelengths and estimating SO\({}_{2}\) in a cost-effective wound-monitoring device. A new wavelength selection method based on heuristic simulated annealing optimization is introduced. At the core of this optimization is the Extended Modified Lambert-Beer law (EMLB), an SO\({}_{2}\) estimation method introduced by [PERSON] et al. [22], which has been proven effective in diabetic wound monitoring [23] and robust to differences in melanin concentration. We reintroduce the EMLB in this work and provide additional details on its implementation. The EMLB is applied with different numbers of selected wavelengths and SO\({}_{2}\) estimation accuracy is evaluated with validation datasets consisting of visible-band Monte Carlo simulation spectra representing light to dark skin types, and in vivo spectra collected during an occlusion study from 13 Asian volunteers. To simulate the properties of a collection device designed with inexpensive LEDs and/or filters, these evaluations are repeated with the same data convolved with a 15 nm full-width half maximum (FWHM) Gaussian filter. Furthermore, a stability test is performed to determine the variation in prediction accuracy that might be experienced should the peak wavelengths of the LEDs/filters not match the selected wavelengths, either due to manufacturing tolerances, temperature fluctuations, or other phenomena.
# A Realistic Hand Image Composition Method for Palmprint ROI Embedding Attack
## 3 Methodology
### Palm Blending  
Each palm exhibits a distinct texture characterized by unique and specific patterns. Directly overlaying attacking ROI \(R_{Atk}\) onto carrier hand \(C\) can introduce noticeable inconsistencies in the palm area surrounding \(R_{Atk}\). To address this challenge, we have devised an intuitive solution that uses a pretrained inpainting network for region regeneration. Specifically, we utilize the Paint by Example (PbyE) inpainting network [5], which employs a reference image to generate a mask area. The generated area, while not identical to the reference image, mirrors the semantic and visual characteristics of the reference. Moreover, the mask's surrounding area is also considered a reference for the inpainting process, making this network suitable for generating a seamless and continuous palm area without additional training.  
The process begins by obtaining the embedded hand by warping the harmonized \(R_{Atk}\) into the specified embedding position \(P\) on carrier hand \(C\). Subsequently, reference ROI \(R_{Ref}\) is employed as the reference image to guide the inpainting process. Finally, an inpainting mask is generated based on the embedding position \(P\) of carrier hand \(C\), as illustrated in Figure 6.  
As illustrated in Figure 6, ROI localization is employed to extract the embedding position \(P\) from the carrier image \(C\). Subsequently, the inpainting mask is deduced based on \(P\) through a three-step procedure. Firstly, an ROI mask is delineated using \(P\), where white represents the mask and black signifies the background to be disregarded. Secondly, employing a dilation kernel, the ROI mask from the first step is expanded to generate a dilated ROI mask. The final inpainting mask is derived by subtracting the ROI mask from the first step from the dilated ROI mask obtained in the second step. The impact of this inpainting mask on the masked embedded hand is depicted in Figure 6.  
Nevertheless, the size of the dilation kernel requires careful consideration. A larger kernel size leads to a more extensive inpainting area, potentially introducing significant changes in \(C\) and consequently diminishing the realism of the composited hand image. Conversely, a smaller kernel size results in a more confined inpainting area, making the transition harder than a larger kernel. To address this, we devised an adaptive kernel size strategy, selecting one of the five side lengths of the ROI as the dilation kernel size.  
Once the embedded hand, inpainting mask, and reference image \(R_{Ref}\) have been obtained, the final composited hand image is synthesized using the Paint by Example (PbyE) method. The palm blending pipeline is depicted in Figure 7.  
Figure 6: The pipeline of making inpainting masks.