esa-sceva/satcom-chunk-collection huggingface dataset:
# Time-Domain Sound Field Reproduction with Pressure and Particle Velocity Jointly Controlled
## 2 Review: Frequency-Domain Velocity-Assisted Sound Field Reproduction
That is, we have the matrix form representation of (3) and (4) as  
\[\mathbf{p}(\omega) =\mathbf{T}_{p}(\omega)\mathbf{w}(\omega)S(\omega) \tag{5}\] \[\mathbf{v}(\omega) =\mathbf{T}_{v}(\omega)\mathbf{w}(\omega)S(\omega), \tag{6}\]  
where given \(M\) control points, \(\mathbf{x}_{m}\) and \(m=1,\dots,M\), \(\mathbf{p}(\omega)=\left[p(\mathbf{x}_{1},\omega),\dots,p(\mathbf{x}_{M}, \omega)\right]^{T}\) and \(\mathbf{v}(\omega)=\left[\mathbf{v}^{T}(\mathbf{x}_{1},\omega),\dots,\mathbf{ v}^{T}(\mathbf{x}_{M},\omega)\right]^{T}\) are column vectors of length \(M\) and \(3M\), respectively. The ATF matrix  
\[\mathbf{T}_{p}(\omega) =\left[\mathbf{t}_{p}(\mathbf{x}_{1},\omega),\dots,\mathbf{t}_{p }(\mathbf{x}_{M},\omega)\right]^{T}\] \[\mathbf{T}_{v}(\omega) =\left[\mathbf{T}_{v}(\mathbf{x}_{1},\omega),\dots,\mathbf{T}_{v }(\mathbf{x}_{M},\omega)\right]^{T}\]  
is of size of \(M\times L\) and \(3M\times L\), respectively. Based on Equations (5) and (6), and assuming the unit amplitude of the source audio signal, i.e., \(S(\omega)=1\), the cost function for a jointly controlling the reproduced sound pressure and particle velocity is formulated as follows:  
\[\min_{\mathbf{w}(\omega)}\{\tau(\omega)\|\mathbf{T}_{p}(\omega)\mathbf{w}( \omega)-\mathbf{p}_{d}(\omega)\|_{2}+(1-\tau(\omega))\|\mathbf{T}_{v}(\omega) \mathbf{w}(\omega)-\mathbf{v}_{d}(\omega)\|_{2}\} \tag{7}\]  
where \(\mathbf{p}_{d}(\omega)\) and \(\mathbf{v}_{d}(\omega)\) represent the desired pressure and particle velocity, respectively. The control strategy is to minimize the reproduction error of both components, and \(\tau(\omega)\in[0,1]\) is the parameter to adjust the relative weights for matching of pressure and velocity. Equation (7) is known as the weighted least squares problem and can be solved using a Moore-Penrose pseudoinverse with Tikhonov regularization. Proposed: Time-Domain Sound Field Reproduction with Joint Control of Sound Pressure and Particle Velocity
## 5 Results
### Ablation Study  
To evaluate the effectiveness of the three-stage training pipeline, we conducted an ablation study by removing Stage 1 and Stage 2 during the training of the EmoSC dataset. Additionally, the impact of residual input was examined by training the model using only discrete content tokens. Table 5 presents the results, highlighting the contributions of each training stage and the role of residual input in capturing paralinguistic features.  
Removing residual input resulted in a substantial decline in emotion recognition performance (input acc.: 58.4%), while text recognition remained relatively stable (BLEU: 9.7%, BERTScore: 87.8%). The slight increase in WER (0.6%) and CER (0.4%) indicated that textual information alone was insufficient for capturing emotional nuances. These findings demonstrate that residual input is critical for encoding paralinguistic cues, which are essential for distinguishing emotional variations beyond lexical content. Consequently, this enabled the model to effectively recognize emotions even when the same content was spoken with different emotional states, as in the EmoSC dataset.  
The absence of both Stage 1 and Stage 2 resulted in notable performance degradation across all metrics (input acc.: 90.0%, BLEU: 8.5%, ROUGE-L: 38.9%), confirming the necessity of structured training. Removing Stage 1 slightly reduced emotion recognition (input acc.: 96.0%) while increasing WER (1.9%) and CER (1.1%), indicating its role in enhancing transcription accuracy. In contrast, removing Stage 2 had minimal impact on input acc. (96.4%) but slightly lowered BLEU (10.1%), suggesting that it refines text generation.  
The full EmoSDS pipeline achieved the highest performance (input acc.: 98.0%, BLEU: 10.4%, ROUGE-L: 40.5%), demonstrating that residual input and structured training improve both emotion recognition and response generation. These findings confirm that training on emotionally diverse datasets with residual input enhances the model's ability to capture both content and paralinguistic information.  
Figure 9: Example response outputs of EmoSDS. The input was delivered as cluster ID sequences. The examples were selected from the EmoSC test set.
## 3 Results and Discussion
### Cr(VI) Adsorption Experiments
#### 3.1.4 Effect of the Ionic Strength  
There are usually other metallic and nonmetallic components in addition to the metal to be removed present in the liquid solution. These components could affect its ionic strength, thus the effect of this parameter on the adsorption process is also interesting to investigate. Figure 5 exhibits that, when the ionic strength increased, it dramatically reduced the adsorption percentage, being the same tendency shown with other adsorbents [39]. The obtained results reveal that the electrostatic forces between the activated carbon surface and Cr(VI) ions were attractive [40].
# Expanding Horizons: Taking Advantage of Graphene's Surface Area for Advanced Applications
## 2 Increasing the Surface Area of Graphene  
The phrase \"increasing the surface of graphene\" describes a variety of changes and treatments made to the material to increase its usable surface area, which makes it ideal for use in sensors, energy storage, and catalysts. Recent advancements have introduced methods such as wrinkling, folding, and buckling its sheets to achieve this [11; 19; 20]. Its surface area is mostly determined by its physical size and dimensions and is composed of a single layer of carbon atoms organized in a two-dimensional hexagonal lattice. This distinctive honeycomb structure is fundamental to its properties, with its surface area primarily determined by its geometric characteristics [21; 22; 23; 24]. [PERSON] et al. [25] reported three ways of enlarging its surface area. For practical applications, large-area sample preparation is crucial, and three methods have been explored to achieve this. The first method is micromechanical cleavage, which involves exfoliating its layers from graphite [26]. The second method is the thermal decomposition of a silicon carbide (SiC) substrate, which is produced by heating SiC to high temperatures, allowing the silicon to sublimate and leaving a graphene layer behind [27]. Finally, the carburization and annealing of a metal substrate method involves introducing carbon into a metal surface and using heat treatment to form them [28]. Each approach has distinct advantages and challenges in producing large, continuous graphene sheets. Its surface area is of paramount importance in a myriad of applications due to its ability to provide a vast number of active sites for catalysis, enhance sensitivity and detection limits in sensors, increase capacity and performance in energy storage devices, facilitate efficient filtration and adsorption processes, offer versatile drug delivery platforms, optimize supercapacitor performance, aid in efficient gas storage and separation, enable sensitive biosensing, and promote various chemical modifications and functionalizations, ultimately making it a versatile and indispensable material in a wide range of cutting-edge technologies and scientific advancements. Increasing the size of the surface further can be viewed as a novel challenge. However, the surface area can be increased in a number of ways.
# A Study on a Radio Source Location Estimation System Using High Altitude Platform Stations (HAPS)
## 3 Numerical Analysis and Results
### Location Estimation Simulation  
In order to realize a simulation of radio source location estimation using the HAPS, we used MATLAB to estimate the location of radio sources. Several location estimation methods were discussed in Section 2, but considering the wide search area and required estimation accuracy, the Beamformer method and the MUSIC method were used in our simulation. As a comparison, a case in which location estimation was performed at the altitude of the UAV was also simulated. In our analysis, the location estimation simulation was performed for a non-mobile illegal radio source located outdoors in an urban area, as shown in Figure 7.  
The simulation process is shown in Figure 13. (1) A radio source is randomly generated, (2) a ray tracing simulation is performed to obtain information about arriving radio waves (direction of arrival, number of reflections, propagation distance, etc.), and (3) the received signal is generated from the aforementioned information. The direction of arrival is estimated by using each estimation method applied on the received data generated in (3). Then, location estimation is performed using the triangularization method. By comparing the randomly generated coordinates of the radio source with the estimated coordinates, the location estimation of the radio source using the HAPS and other methods is evaluated. Figure 13 shows an overview of the simulation.  
Figure 13: Simulation overview of our location estimation process.  
The receiving antenna was modeled after the cylinder antenna used in the HAPS as a base station for the simulation. The actual prototype cylinder antenna can be found in [15]. The specifications are shown in Table 2.  
In this paper, the cylinder antenna consists of \(N=36\) elements, as depicted in Figure 14, where each element was assumed to be omni-directional and the maximum gain was set to 0 dBi in the simulation.
# Secure K-Means Clustering Scheme for Confidential Data Based on Paillier Cryptosystem
## 6 Secure Homomorphic K-Means Clustering Protocol  
### Secure Computation Distance (SCD) Protocol  
Algorithm 2 guarantees that the squared Euclidean distance between the data point and the cluster center can be securely computed without decrypting the data, thus protecting the privacy of the data points and the cluster center.  
The input consists of encrypted data point \(E(x_{i})\) and encrypted cluster center \(E(c_{j})\), each represented as vectors of encrypted values over \(d\) dimensions. For each dimension \(k\), the difference between the encrypted data point and the encrypted cluster center is computed homomorphically: \(E(\Delta)=E(x_{ik})\cdot E(c_{jk})^{N-1}\). This uses Paillier's homomorphic properties where subtraction is achieved by multiplying the ciphertext of \(x_{ik}\) with the modular inverse of the ciphertext of \(c_{jk}\). The square of the encrypted difference is computed by homomorphically squaring the difference: \(E(\Delta^{2})=RSM(E(\Delta),E(\Delta))\). The encrypted squared Euclidean distance is obtained by multiplying all the encrypted squared differences: \(E(d_{ij}^{2})=\prod_{k=1}^{d}E(\Delta^{2})\). Homomorphic addition in the Paillier cryptosystem is performed by multiplying the encrypted values.  
```
1:Input: Encrypted data \(E(x_{i})=E(x_{ik}){k=1}^{d}\), Encrypted cluster center \(E(c_{j})=E(c_{jk})k=1^{d}\).
2:Output: Encrypted distance \(E(d_{ij}^{2})\).
3:Performed by CS:
4:for each dimension \(k\) from 1 to \(d\)do
5: Compute the encrypted difference :
6:\(E(\Delta)=E(x_{ik})\cdot E(c_{jk})^{N-1}\).
7:endfor
8:Performed by and CSP:
9:for each dimension \(k\) from 1 to \(d\)do
10: Compute the encrypted squared difference :
11:\(E(\Delta^{2})=RSM(E(\Delta),E(\Delta))\).
12:endfor
13: Compute the encrypted squared Euclidean distance :
14:\(E(d_{ij}^{2})=\prod_{k=1}^{d}E(\Delta^{2})\).
15:return Encrypted distance \(E(d_{ij}^{2})\).
```  
**Algorithm 2** Secure Computation Distance (SCD) Protocol using Paillier Cryptosystem
## 3 Results and Discussion
Figure 7 shows the SERS spectrum of 1 mM bisphenol A (BPA) on the silver nanoplate film formed after exposing the substrate to the nanoplate suspension for 2 h. The normal Raman spectrum for 1 mM BPA on the silicon surface is also provided in Figure 7. As expected, BPA shows seven major Raman peaks at 642 cm\({}^{-1}\), 819 cm\({}^{-1}\), 835 cm\({}^{-1}\), 1114 cm\({}^{-1}\), 1183 cm\({}^{-1}\), 1600 cm\({}^{-1}\), and 1614 cm\({}^{-1}\), clearly indicating the presence of BPA on the substrate [40]. These BPA peaks were strongly enhanced when the molecule was absorbed on the silver nanoplate film, clearly demonstrating that these nanoplate films are a good SERS substrate and suitable for further investigations for incorporation in a SERS-based sensor.
## 1 Introduction  
Recently, the use of plasma for producing nanostructures of different materials are gaining growing interest. One of the most powerful methods for producing nanoparticles (NPs) with plasma is the use of laser pulse, namely, pulsed laser ablation in liquid (PLAL) or laser-assisted synthesis in solution (LASiS) [1, 2]. In this technique, the material ejection is obtained by focusing a laser pulse on a specific target immersed in liquid. The laser-induced plasma (LIP) and cavitation bubble generation are the main processes occurring after the laser-matter interaction. During the plasma and cavitation bubble evolution, the production of NPs, generally with the same composition of the target, occurs. One of the most attractive features of such a tool for NP production is that with a very simple set-up, it is possible to obtain high quality NPs, in terms of purity and size distribution. Moreover, PLAL does not require stabilizers or chemical compounds for their growth and stabilization in solution.  
In the last decades, several kinds of nanostructures have been produced with PLAL including metallic NPs [1], various carbon NPs and nanotubes [3], alloy NPs, core-shell NPs [4], etc.  
Despite the ease of the instrumental set up, the process is very complex to investigate for several reasons: the plasma produced with laser\(-\)matter interaction in water has a maximum dimension comparable to the laser spot size and a lifetime at microsecond scale; the emission of the plasma contains poor information on the ablated species because it is dominated by continuum radiation. A few hundreds of ns after the laser pulse, the cavitation is induced around the plasma and it evolves very fast in the order of hundreds of microseconds changing, in turn, the refraction index. Finally, during the PLAL processes, the observation of the NPs' evolution is very difficult with visible sources, since the NPsthemselves have a smaller size than the visible light wavelength. The previously mentioned issues imply the necessity of using high temporally and spatially resolved techniques to retrieve information on the generation of NPs during the PLAL processes. Optical tools like optical emission spectroscopy (OES), shadowgraph and laser scattering techniques are generally employed for the diagnostics of the processes occurring during PLAL, while most of information is obtained with theoretical calculation [5; 6]. In this paper, a general overview of these techniques including the advantages and disadvantages is discussed.
# Artificial Neural Networks in Coordinated Control of Multiple Hovercrafts with Unmodeled Terms
## 4 Consensus Strategy
### The Cascade-Directed Communication Graph  
The CDCG used in this study is shown in Figure 5, where agent 1 can be viewed as the leader. Its corresponding Laplacian matrix \(\mathcal{L}_{1}\) is  
\[\mathcal{L}_{1}=\begin{bmatrix}0&0&0&0&0\\ -1&1&0&0&0\\ 0&-1&1&0&0\\ 0&0&-1&1&0\\ 0&0&0&-1&1\end{bmatrix}. \tag{58}\]  
The desired paths are defined as  
\[\mathbf{p}_{di}(\gamma_{i})=R_{i}\begin{bmatrix}\cos(\gamma_{i})\\ \sin(\gamma_{i})\end{bmatrix}\ (\mathbf{m}) \tag{59}\]  
where \(R_{i}=6-i,\ (i=1,2,\ldots,5)\) denotes the radius of the circle, and unmodeled terms \(\Delta_{V}\) and \(\Delta_{\omega}\) are denoted by \([0.1u^{2}+0.01 uv,0.01 uv+0.1v^{2}]^{T}\) and \(0.01 uv+0.05u\omega+0.01v\omega\), respectively. The parameters used herein are as follows: \(m=0.6\), \(J=0.1\), \(c_{i}=2\), \(a_{1}=1\), \(a_{2}=2\), \(a_{3}=1\), \(k_{1}=6\), \(k_{2}=3\), \(k_{3}=2\), \(\Gamma_{1}=\text{diag}(0.6,0.6)\), \(\Gamma_{2}=\text{diag}(0.01,0.01)\), \(\Gamma_{3}=\text{diag}(0.022,0.022)\), and \(\mathbf{\eta}=[0.2,0]^{T}\).  
Figure 4: Control block in Simulink/Matlab for the \(i\)-th vehicle.  
Figure 3: Coordinated control block in Simulink/Matlab.  
Figure 6a shows the actual trajectory of the mobile robots, and we can see that they move forward in a line formation. Moreover, Figures 7a,b display the convergence of \(||\mathbf{e}_{1i}||\) and \(||\mathbf{e}_{2i}||\), respectively, showing that all of them converge to the neighborhood of zero. The consensus performances are shown in Figures 8a,b, showing that \(\gamma_{ij}=\gamma_{i}-\gamma_{j}\) converges to zero, where agent \(j\) is the neighbor of agent \(i\). Moreover, we can also see that \(\dot{\gamma}_{i}\) converges to the desired value \(\dot{\gamma}_{d}=0.5\). It is important to point out that, in this work, we chosen agent \(1\) as our leader, and this satisfies \(\dot{\gamma}_{1}=\dot{\gamma}_{d}\). The approximate performance of RBFNNs can be found in Figure 6b, where the blue lines denote the real values of \(\Delta_{v}\) and \(\Delta_{\omega}\), while the red lines represent their estimates \(\hat{\Delta}_{v}\) and \(\hat{\Delta}_{\omega}\). Thus, both estimates converge to their corresponding real values.  
Figure 5: A cascade-directed communication graph (CDCG).  
Figure 6: Norm of the position errors and the performance of unmodeled term approximation (CDCG).  
(**a**) Norm of the position errors. (**b**) Performance of unmodeled term approximation.  
Figure 7: Norm of the position and linear velocity errors (CDCG). (**a**) Norm of the position errors. (**b**) Norm of the linear velocity errors.
# Complete Blood Cell Detection and Counting Based on Deep Neural Networks
## 1 Introduction  
For an adult, there are about five liters of blood in the body, and blood cells account for nearly 45% of blood tissue by volume. Three blood cell types are red blood cells (RBC), white blood cells (WBC, including basophil, lymphocyte, neutrophil, monocyte, and eosinophil) and platelets. Red blood cells are the main medium carrying oxygen, white blood cells are a part of the immunity system resisting diseases, and platelets have coagulation function, which can recover wounds with scabs. Both physiological and pathologic changes affect the composition of blood clinically. Thus, blood tests have become a direct channel to detect one's health status or diagnose diseases. Complete blood cell (CBC) counting is one of the classical blood tests, which identifies and counts basic blood cells to examine, follow and manage the variation in blood [1].  
CBC counting is often performed by flow cytometers or medical professionals to obtain reliable test results. However, manual operation is time-consuming, tedious, and fallible. Scientists began exploiting automated analyzers in the early 20 th century. With the development of computation capabilities, many researchers adopt image processing techniques and statistical or deep learning models to elevate CBC counting on blood smear images. However, the blood smear images for CBC counting are usually low resolution and blurry, making it hard to accurately identify blood cells. Furthermore, blood cells are sometimes heavily overlapping. We will further survey these studies in Section 2.  
Convolutional Neural Networks (CNNs) have been applied successfully in several visual recognition tasks [2]. Due to their outstanding abilities of learning and extracting features, CNNs have become a prevalent approach in dealing with medical image analysis [3]. Well-trained CNNs can capture more useful information and better detect andclassify objects on input images than traditional image processing methods. In addition, CNNs can be easily integrated into information systems and have been widely adopted in big data information processing [4].  
In this paper, we propose a novel CNN-based deep learning architecture to detect and classify blood cells on blood smear images and simultaneously realize accurate CBC counting. Five models are constructed herein with different settings. The experiments are carried out on a public blood smear image dataset. In the end, we compare and discuss the results of the proposed models under different conditions.  
In this paper, we introduce the research background, motivation, and objective in Section 1. We survey related works in the literature in Section 2. We then describe the proposed architecture in Section 3. We show and discuss the experimental results in Section 4. Finally, we draw conclusions in Section 5.
## 6 Conclusions  
A novel methodology for CAP analysis based on data symbolization, vectorization, and classification is presented.  
First, a subset of 21 patients from the annotated CAP Sleep Database [4] was selected for this research. One-second segmentation was implemented on this data subset. Then, the symbolization process was performed using 1d-SAX, and the experimentation showed that it is a suitable form of transforming EEG signals into sequences of symbols.  
Further vectorization was carried out using two Doc2 Vec models (one per selected channel) with the PV-DM algorithm. The resulting Doc2 Vec-trained models were implemented to infer the vectors corresponding to the EEG segments of interest: those labeled as CAP1, CAP2, and CAP3 (according to the CAP A-phase type).  
The classification metrics on the 10 classic ML-implemented models have shown that increasing the dimensions of Doc2 Vec does not necessarily improve the classification results. Additionally, PCA dimensionality reduction improves the classification results in comparison to the same-length vectors from the original embedding concatenation. However, it does not represent the best overall result.  
The best results when using hold-out validation (\(F_{1}Score=0.7651\)) were achieved with the support vector classifier with a radial basis kernel and class-wise balanced regularization and the Doc2 Vec vector dimension = 300. The best results for stratified 10-fold cross-validation (\(F_{1}Score=0.7611\pm 0.0133\)) were achieved by using a Doc2 Vec vector dimension = 50, without PCA.  
From the confusion matrix (Figure 6) analysis, it can be concluded that the CAP1 and CAP3 classes are more differentiable from each other, whereas CAP2 is easily misclassified as CAP1. This is also supported by the number of instances present in each class (Table 3).  
Finally, based on the vector representation's distribution, it can be stated that this problem is not fully solved, and more experimentation and research in this field are needed.