esa-sceva/satcom-chunk-collection huggingface dataset:
# Influence of Roll Diameter on Material Deformation and Properties during Wire Flat Rolling
###### Abstract
From an industrial perspective, it is challenging for engineers to overcome the issue of shape control because the shape of the rolled product varies with both the working conditions and material properties. Therefore, the shape change behavior of a workpiece during wire flat rolling with the roll diameter must be understood. Hence, this study mainly focuses on the influence of roll diameter on the deformation behaviors, such as the strain distribution, shape change, contact pressure, and damage value of the workpiece during wire flat rolling to control the material deformation and properties of the wire. For a systematic comparison study, flat-rolled wires fabricated using the four different rolls were compared using finite element (FE) analysis.
## 3 Main Results
### Adaptive Output Feedback Control Design
Via [20], the boundedness of the error can be ensured by a suitable choice of the TDs' design parameters. Thus, there exists a positive constant \(s_{M}\) such that \(|s_{2d}|\leq s_{M}\). **Remark 4**. : _It should be emphasized that, despite the generated estimation error \(s_{2d}\) being bounded, it still will have an influence on the control performance to some extent. Thus, it is reasonable and necessary to establish the compensating signals and the corresponding new tracking errors (15) for System (3)._  
Now, the compensating signal \(\vartheta_{1}\) is established as:  
\[\dot{\vartheta}_{1}=-\kappa_{1}\vartheta_{1}+\vartheta_{2}+(\alpha_{2d}- \alpha_{1}), \tag{28}\]  
with \(\vartheta(0)=0\) for \(t\in[0,T_{1}]\). This is a time-varying linear filter with bounded input \(s_{2d}=\alpha_{2d}-\alpha_{1}\). Then, the following virtual control input is constructed:  
\[\alpha_{1}=-\kappa_{1}z_{1}-\frac{3}{4}v_{11}^{\frac{4}{3}}\tilde{\varsigma}_{ 1}-\frac{3}{4}v_{12}^{\frac{4}{3}}\tilde{\varsigma}_{1}+\dot{y}_{d}. \tag{29}\]  
Substituting them into (26) results in:  
\[\mathcal{L}V_{k1}\leq-\;(\Xi-\frac{1}{4v_{12}^{4}})\|\dot{x}\|^{4}-\kappa_{1} \xi_{1}^{4}+\frac{1}{4v_{11}^{4}}\xi_{2}^{4}+D_{0}. \tag{30}\]  
_Step 2_. Define the stochastic Lyapunov function as:  
\[V_{k2}=V_{k1}+\frac{1}{4}\xi_{2}^{4}+\frac{\vec{\theta}^{2}}{2\gamma}, \tag{31}\]  
in which \(\theta=\max_{k\in\Xi}\{\|W_{k,1}^{T}\|^{2}\}\) is an unknown constant, \(\vec{\theta}=\theta-\hat{\theta}\). According to (14) and (15), we have:  
\[d\tilde{\varsigma}_{2}=(u_{k}+l_{k,2}\tilde{\varsigma}_{1}-\dot{\alpha}_{2d}- \dot{\theta}_{2})dt. \tag{32}\]  
By combining (31) and (32), we obtain:  
\[\mathcal{L}V_{k2}= \mathcal{L}V_{k1}+\xi_{2}^{3}(u_{k}+l_{k,2}\tilde{\varsigma}_{1 }-\dot{\alpha}_{2d}-\dot{\theta}_{2})-\frac{1}{\gamma}\hat{\theta}\hat{\theta}. \tag{33}\]  
In light of the above description, it yields:  
\[\xi_{2}^{3}u_{k}=\xi_{2}^{3}(v_{ck}+\sigma_{k}^{\prime}(v_{k})-v_{\phi k}+ \varpi_{k}), \tag{34}\]  
then the FLS is utilized to develop the control issue on the dead-zone input:  
\[\xi_{2}^{3}(\sigma_{k}^{\prime}(v_{k})+\varpi_{k})= \xi_{2}^{3}(W_{k,1}^{T}S_{k,1}+\delta_{k,1}+\varpi_{k}),\] \[\leq \frac{1}{2a^{2}}\xi_{2}^{6}\theta S_{k,1}^{T}S_{k,1}+\frac{a^{2}} {2}+\frac{3}{4}\xi_{2}^{4}+\frac{1}{4}\vec{t}^{4}\] \[\leq \frac{1}{2a^{2}}\xi_{2}^{6}\theta+\frac{a^{2}}{2}+\frac{3}{4}\xi_ {2}^{4}+\frac{1}{4}\vec{t}^{4}, \tag{35}\]  
where \(|\delta_{k,1}+\varpi_{k}|\leq\vec{t}\), and the property of \(0<S_{k,1}^{T}S_{k,1}\leq 1\) is used here. Similarly, we have:  
\[\alpha_{1}=-\kappa_{1}z_{1}-\frac{3}{4}v_{11}^{\frac{4}{3}}\tilde{\varsigma}_{ 1}-\frac{3}{4}v_{12}^{\frac{4}{3}}\tilde{\varsigma}_{1}+\dot{y}_{d}. \tag{29}\]0  
where \(\
u_{22}\) is a positive designed scalar.
## 3 Result and Discussion of the Simulation Study
### Setpoint Tracking of the PID Pressure Control  
In general, only constant setpoint control tests are performed to inspect system stability. However, the FTS reactor system is nonlinear and time-varying, thus the pressure setpoint is supposed to be shifted in a wide range to verify the response power of the control system. The initial pressure value of the setpoint tracking tests was 1.55 MPa, reset to 2.32 MPa a  
Figure 5: The schematic diagram of fixed-bed reactor control configurations.  
while after the pressure settled out; corresponding water boiling points of 200 \({}^{\circ}\)C and 220 \({}^{\circ}\)C are eligible for heterogeneous reaction, such as FTS and diethyl oxalate hydrogenation. The two-kind-PID-controlled drum pressure, drum temperature, and reactor temperature were recorded in real-time in KINGVIEW as Figures 6 and 7. Both dynamic and steady-state controls were researched in this study.  
As Figure 6 represents, the static misalignment of conventional PID is \(-\)0.1 MPa\(\rightarrow\)+0.01 MPa as we set 1.55 MPa, much more than that of BPNN self-tuned PID, \(-\)0.03 MPa\(\rightarrow\)+0.01 MPa. Having adjusted the pressure set value to 2.32 MPa later, the former static error of PID grew between \(-\)0.15 MPa and +0.01 MPa, BPNN self-tuning PID remaining unchanged though. Consequently, the fixed value control performance of BPNN self-tuned PID controllers is significantly superior to traditional ones. The step change from 1.55 MPa to 2.32 MPa, embodiment of strong nonlinearity and time-varying nature, enabled BPNN self-tuning PID controllers to display the control power well, which has shown better robust ability.  
Simultaneously, we also observed the real-time temperature alteration of the steam drum as shown in Figure 7. The BPNN self-tuning drum temperature held a steady and final short swing of less than 1 \({}^{\circ}\)C (as opposed to more than 3 \({}^{\circ}\)C in conventional PID control) due to the powerful pressure control; the temperature with altered pressure stayed  
Figure 6: The real-time curves of drum pressure control over two controllers.  
Figure 7: The real-time curves of drum and reactor temperature over two controllers.  
constant and had no residuals, which proved a stronger control quality of BPNN. During the control process of BPNN, continual readjustment of the PID parameters allowed greater adaptability to the system instability. Along with tended stable pressure, the PID factors converged to the actual definite value through end-to-end adaptive tuning.
## 3 Methodology
### Domain Knowledge Extraction by Power Spectral Density  
Sleep signals are continuous time series data with fluctuations in amplitude and behavior of different frequencies that changes over time for every sleep stage. The sheer volume of the highly fluctuating raw EEG data makes it challenging for machine learning algorithms to find a meaningful pattern that can be helpful in identifying different sleep stages. Hence, we calculate the Welch PSD of the raw EEG data to improve our chances of finding out a meaningful pattern that can be helpful in identifying different sleep stages [45; 46; 47; 48]. The Welch PSD calculates the distribution of energy across different frequencies of the EEG signal. In Figure 2, the computed Welch PSD is plotted for the five different sleep stages of a female subject to show the differences among the PSD of different sleep stages. For calculating the PSD, the 30 s epoch is considered as an input. After that, DFT of 50% overlapping segment of nfft (number of points in DFT) is used (sampling frequency = 200 Hz, nfft = 100) to calculate the spectrum with DFT averaging. Therefore, the change in the amplitude is observed by converting the amplitude response from dBW/Hz to dBW/bin. For each dataset, the computed PSD response of each sleep state is different from the others, hence necessitating the use of three channel EEG data for sleep state identification.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sex**} & \multirow{2}{*}{**Dataset**} & **Subject** & **EEG** & **EEG Scalp** & & \multicolumn{3}{c}{**Sleep Stages**} \\  & & **Amount** & **Channel** & **Source** & & & \\ \hline \multirow{3}{*}{Female} & Dataset 1 & 16 & Fp2-A1 & Pre-frontal & Wake & REM & N1 & N2 & N3 \\  & Dataset 2 & 16 & Cz2-A1 & Central & Wake & REM & N1 & N2 & N3 \\  & Dataset 3 & 16 & O2-A1 & Occipital & Wake & REM & N1 & N2 & N3 \\ \hline \multirow{3}{*}{Male} & Dataset 4 & 04 & Fp2-A1 & Pre-frontal & Wake & REM & N1 & N2 & N3 \\  & Dataset 5 & 04 & Cz2-A1 & Central & Wake & REM & N1 & N2 & N3 \\ \cline{1-1}  & Dataset 6 & 04 & O2-A1 & Occipital & Wake & REM & N1 & N2 & N3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Details of considered dataset.  
Figure 2: Power spectral density (PSD) curves of all the sleep stages of a single female subject but from different datasets.
## 1 Introduction
Performance analysis of an SAC system with a diagonal double weight (DDW) code in an FSO communication system is reported in [20]. A continuous-wave (CW) laser source with a DDW code was used, and performance was analyzed under rain and haze conditions. The results indicate that a multi-array source in combination with a DDW code provides acceptable performance even when transmitted power values are low. Furthermore, to bypass the shortcomings of incoherent sources, previous studies suggested using multi-wavelength laser sources (laser arrays) to achieve higher performance without examining the attainable performance. Such an analysis is essential because lasers of the same wavelength support higher data rates. Indeed, a coherent single-wavelength light source that can generate a huge number of wavelengths is a cost-effective approach [21]. Hence, the use of optical sources is very important because it trades off all the advantages of both coherent and incoherent sources [22]. So, using both coherent and incoherent sources together as we introduce a CCIS technique in this paper will achieve the advantages of both LED and CW laser sources, where a high number of users is achieved in comparison to an LED source with a low BER and a higher data rate [23]. In this paper, for the first time according to our knowledge, an FSO communication system based on the use of CCIS with an SAC- OCDMA system using an FRS code is proposed. Additionally,different weather conditions such as different haze levels (low, medium, and heavy) and different fog levels (low, medium, and heavy) are implemented. Moreover, the feasibility of using the CCIS technique in aerial altitude platform system (AAPS) applications that are based on the FRS code is discussed. The performance is investigated in terms of bit error rate (BER), fiber length, FSO propagation range, quality factor (Q-factor), and eye diagram. Therefore, the main contribution of our proposal is as follows:  
1. Provision of an affordable solution for future communication system that is neither costly nor complicated;
2. Application of the CCIS technique for codes having a fixed cross-correlation of unity (CC = 1) such as the DPS code;
3. Application of the CCIS technique in a free space optics (FSO) system by investigating different weather conditions;
4. Application of the CCIS technique in a sky mesh network using the aerial altitude platform system (AAPS). This paper is structured in as follows. Section 2 deals with the principle of CCIS operation based on SAC-OCDMA systems. Section 3 discusses the feasibility of applying CCIS in different SAC-OCDMA codes. CCIS designs for three coherent source configurations are presented along with their performance analysis with FRS codes in Section 4 under an FSO channel. The application of CCIS design for an AAPS in a sky mesh network under an SAC-OCDMA system is given in Section 5. The conclusion is given in Section 6.
# Comparative Study on Physicochemical and Nutritional Qualities of Kiwifruit Varieties
## 1 Introduction  
The kiwifruit is a perennial deciduous vine that belongs to the genus Actinidia. The genus Actinidia has 75 taxa, comprising 54 species and 21 variations, as of the most recent classification [1]. China is endowed with a wealth of genetic resources because it is the origin area of kiwifruit. Currently, the commercial kiwifruit cultivars are mainly from _A. chinensis_ and _A. chinensis_ var. _deliciosa_, and a small amount of _A. eriantha_ and _A. arguta_. According to the fruit flesh color of kiwifruits, they are mainly divided into three categories: green-fleshed, yellow-fleshed and red-fleshed. At present, more than 90% of cultivated kiwifruits are green and yellow-fleshed varieties. Green-fleshed 'Hayward' is the main plant variety around the world, and is loved by consumers because of its strong aroma, excellent quality, good storage and other outstanding advantages [2]. Yellow-fleshed 'Jinyan' is tender, juicy and fragrant, with extremely disease-resistant fruit, which is recognized by domestic and international markets [3]. In recent years, due to their unique flavor and nutritional value, red-fleshed kiwifruits have become increasingly popular with consumers [4, 5]. 'Donghong' is a new variety of red-fleshed kiwifruit with strong disease resistance, heat and drought tolerance, brilliant red inner skin and sweet flavor [6].  
As we know, kiwifruits with high nutritional quality, good flavor and aromatic scent [7] have important commercial value [8]. In recent years, the demand for high fruit quality has increased gradually, and studies have shown that fruit nutritional value is critical to fruit quality, which will provide consumers with a basis for consumption [9]. However, fewerstudies have been conducted on the nutritional variation among different varieties, and some popular varieties bred in recent years have not been studied [10].  
Therefore, in order to explore the profile of nutritional variation in each kiwifruit variety, we selected 14 kiwifruit varieties, covering red-fleshed, green-fleshed and yellow-fleshed kiwifruits, which are widely cultivated or have high market shares in China. In this study, the pectin, moisture, chlorophyll, carotenoid, flavonoid, energy, carbohydrate, protein, lipid, dietary fiber, soluble sugar, organic acid, vitamin and aroma compound contents were detected and compared. The physical and chemical properties, nutritional properties and aroma of various kiwifruit varieties provide a reference for their consumption and utilization, as well as for kiwifruit breeding.
# Compact and Accurate Scene Text Detector
## 3 Proposed Method  
### Architecture  
We design a new model, namely _Compact and Accurate Scene Text detector_ (CAST), for efficient scene text detection. It takes an input image of size \(W\times H\) and generates a set of potential word boxes as the output. The overall process is depicted in Figure 1: the input image on the left is injected in to our backbone model, followed by the balanced decoder. The result of the decoder is finally converted into a set of potential word boxes through post-processing. Although we aim to develop an efficient model for text detection, we do not simply ignore the effectiveness. If a model largely fails to predict word boxes, then it is useless even if it is the most efficient. Therefore, we adopt MobileNetV2 as the backbone because it is known to be both effective and efficient.  
MobileNetV2, our backbone model, generates arbitrary features of a compact dimension from a given image. As shown in the left of Figure 2, MobileNetV2 has one convolutional stage and four inverted residual block (IRB) stages. Through these four IRB stages, MobileNetV2 generates the matrix \(F_{5}\), conveying highly semantic patterns obtained from stage 5. For example, given an input image of size \(W\times H\), MobileNetV2 generates a feature matrix \(F_{5}:=\mathbb{R}^{W_{F_{5}}\times H_{F_{5}}}\), where \(W_{F_{5}}=W/32\) and \(H_{F_{5}}=H/32\). The compact feature matrix \(F_{5}\) maintains the latent patterns obtained from the stack of inverted residual blocks (IRBs). As shown in Figure 3, an IRB consists of three steps: expansion, depth-wise convolution, and projection. First, the IRB expands its input to a larger dimension, and finally projects it into a smaller dimension while the input is directly connected to its output. This approach can represent more complex functions with greater bottleneck expansion \(t\), as described in [18], and we set \(t=6\). Together with \(F_{5}\), three more feature matrices (\(F_{4}\), \(F_{3}\), and \(F_{2}\)) at different scales are passed into the decoder in order to incorporate semantic patterns at different scales. Although Deeplab v3 [27] stated that the backbone using output stride (OS) up to 16 was better for effectiveness, we set OS up to 32 for better efficiency. This decoder generates a set of candidate word boxes based on  
Figure 1: Overall flow of prediction. Images are passed to both MobileNetV2 and the balanced decoder, and post-processing generates the predicted results.  
the four feature matrices, and some inappropriate word boxes are filtered out during post-processing. The post-processing technique adopted in this paper is EAST [12]. This method merges adjacent word boxes according to the predicted scores, and then finds appropriate word boxes using non-maximum support (NMS).
# Bit-Level Construction for Multiplicative-Repetition-Based Non-Binary Polar Codes +
## 2 Preliminaries for Non-Binary Polar Codes
### Binary Polar Codes  
Given a BI-DMC \(W:\mathcal{X}\rightarrow\mathcal{Y}\) with input alphabet \(\mathcal{X}\) and output alphabet \(\mathcal{Y}\), where \(\mathcal{X}\in\{0,1\}\), the channel transition probabilities can be defined as \(W(y|x),x\in\mathcal{X},y\in\mathcal{Y}\). If \(W\) is a symmetric channel, the symmetric capacity and the Bhattacharyya parameter of the B-DMC \(W\) can be defined as  
\[C(W)\triangleq\sum_{y\in\mathcal{Y}}\sum_{x\in\mathcal{X}}\frac{1}{2}W(y\mid x )\log\frac{W(y\mid x)}{\frac{1}{2}W(y\mid 0)+\frac{1}{2}W(y\mid 1)} \tag{1}\]  
and  
\[Z(W)\triangleq\sum_{y\in\mathcal{Y}}\sqrt{W(y|0)W(y|1)}, \tag{2}\]  
respectively.  
Assume that the code length is \(N=2^{n}\), the information sequence length is \(K\). With the channel polarization, the \(K\) most reliable synthesized channels \(W_{N}^{(i)},i\in\mathcal{A}\) are used to transmit information bits, and the others are used to transmit the frozen bits, where \(|\mathcal{A}|=K\). The encoding of a polar code can be described by  
\[x_{1}^{N}=u_{1}^{N}\mathbf{F}_{N}=u_{1}^{N}\mathbf{F}_{2}^{\otimes n}\mathbf{ B}_{N}, \tag{3}\]where the input sequence \(u_{1}^{N}\) contains \(K\) information bits and \(N-K\) frozen bits, the matrix \(\mathbf{F}_{N}\) is the \(N\times N\) generator matrix, \(\mathbf{B}_{N}\) is a permutation matrix [1], \(\otimes\) denotes the Kronecker operation, and the Arikan kernel \(\mathbf{F}_{2}\) is given by  
\[\mathbf{F}_{2}=\left[\begin{array}{cc}1&0\\ 1&1\end{array}\right]. \tag{4}\]  
The channel splitting operation splits the vector channel \(W^{N}\) into \(N\) polarized channels \(W_{N}^{(j)}:\mathcal{X}\rightarrow\mathcal{Y}\times\mathcal{X}^{j-1},1\leq j\leq N\). The transition probability of the \(j\)-th polarized channel \(W_{N}^{(j)}\) is defined as  
\[W_{N}^{(j)}(y_{1}^{N},u_{1}^{j-1}|u_{j})\triangleq\sum_{u_{j+1}^{N}\in \mathcal{X}^{N-j}}\frac{1}{2^{N-1}}W_{N}(y_{1}^{N}|u_{1}^{N}). \tag{5}\]
# Multi-Agent-Supported Tracking Based on Hidden Markov Model with Weighted Entropy
###### Abstract  
Multi-object tracking (MOT) has witnessed significant advancements in recent years, yet it remains challenged by complex uncertainties arising from pedestrian movement patterns. To address this, we present a unified framework that explicitly models pedestrian dynamics through a dual-phase paradigm, combining a Hidden Markov Model (HMM) for motion modeling and weighted entropy for adaptive multi-cue fusion. Furthermore, a multi-agent architecture is employed for track management, enabling parallelized state estimation and seamless integration of the HMM-based Kalman filter with multi-cue fusion. Quantitative evaluations show that our method achieves 82.1 in IDF1, 81.5 in MOTA, 65.9 in HOTA, and 1,255 IDs on the MOT17 benchmark, and achieves 81.2 in IDF1, 78.4 in MOTA, 65.7 in HOTA, and 608 IDs on the MOT20 benchmark, and the application of the multi-agent mechanism significantly improves the scores on FPS as a result of efficient computation. The experimental results demonstrate that the proposed method achieves state-of-the-art performance, particularly in highly crowded scenes.  
multi-object tracking; multi-agent; hidden Markov model; weighted entropy Article
## 3 Results and Discussion
### Batch Absorption Experiments
#### 3.2.2 Adsorption Isotherms of Cu(II) onto Lime Sand Bricks  
Two of the most commonly used isotherm equations, Langmuir and Freundlich [32], were chosen to fit the adsorption isotherms of Cu(II) onto lime sand bricks. The values of the derived isotherm parameters are listed in Table 4 along with the \(r^{2}\) of the fits. Both the Langmuir and Freundlich models fit well with Cu(II) adsorption by lime sand bricks (Figure 5). The Langmuir model shows slightly larger \(r^{2}\) than the Freundlich model. We chose the Langmuir model to obtain the maximum adsorption capacity of the lime sand bricks. Many previous studies have used this model to describe the adsorption of heavy metals on heterogeneous adsorbents such as bricks and bentonite [33], red clay stone [5] and zeolite [34]. The adsorption capacity and affinity of lime sand bricks for Cu(II) were evaluated by analyzing the parameters \(q_{\text{m}}\) and \(K_{\text{L}}\) of the Langmuir equation (Table 4). The maximum adsorption capacity of the lime sand bricks was 7.01 mg/g.  
From the elemental analysis, the lime sand bricks contain more exchangeable cations, which are beneficial to the occurrence of ion exchange. In addition, we measured the cation exchange capacity and found that the cation exchange capacity in the lime sand bricks was 46.67 mmol/g, which also  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{3}{c}{**Langmuir Model**} & \multicolumn{3}{c}{**Freundlich Model**} \\ \cline{2-7}  & \(q_{\text{m}}\) **(mg/g)** & \(K_{\text{L}}\) **(L/mg)** & \(r^{2}\) & \(K_{\text{F}}\) **(mg/g)** & \(L/mg\)**n** & \(r^{2}\) \\ \hline lime sand bricks & \(7\pm 1\) & \(0.04\pm 0.01\) & 0.99 & \(0.39\pm 0.07\) & \(1.39\pm 0.14\) & \(0.97\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: The parameters of Langmuir and Freundlich isotherms for the adsorption of Cu(II) by lime sand bricks.  
Figure 5: The adsorption isotherms of Cu(II) by lime sand bricks at 25 \({}^{\circ}\)C (solid-liquid ratio = 8 g/L, stirring speed = 135 rpm, pH = 5).  
facilitated the occurrence of ion exchange. Therefore, it was considered that the adsorption of Cu(II) by lime sand bricks was mainly the role of ion exchange.  
Table 5 presents a comparison of Cu(II) adsorption capacity for different types of natural adsorbents. Among the listed natural adsorbents, The adsorption capacity of bricks for Cu(II) adsorption is slightly larger than some low-cost adsorbents such as fly ash [35; 36; 37] and zeolite [38; 39], while the capacity of bricks is smaller than the traditional adsorbents such as cedar sawdust [40] and clays [41]. As waste materials, bricks could still be applied as adsorbents or filling materials in some filtration devices such as in a permeable pavement to remove pollutants due to their large adsorption capacities and low cost. In the process of adsorption of copper on bricks, both physical adsorption and ion exchange play a very important role.