esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### ML Algorithms Considered
#### 2.1.2 Decision Trees (DT)  
Decision trees are a popular non-parametric machine learning method utilized for both classification and regression tasks. One of their notable advantages, distinguishing them from neural networks, is their interpretability. Decision trees offer easily understandable decision rules, in contrast to the complex weight connections found in neural networks [33]. A decision tree is constructed using a combination of nodes and branches, each serving a specific purpose:  
* The internal nodes or leaves represent the input predictor variables used for decision making.
* Each branch represents a decision based on a particular condition or the probability of an event occurring.
* The terminal nodes provide the final decision or outcome.  
The construction of a decision tree follows a \"divide and conquer\" approach [34]. Given a set of \(k\) classes, \([C_{1},C_{2},\ldots,C_{k}]\), as well as a training set, \(\mathbf{x}=[x_{1},x_{2},\ldots,x_{m}]\), the methodology is based on the following principles [35]:  
* If the training set \(\mathbf{x}\) contains elements that belong exclusively to a single class \(C_{j}\), the decision tree terminates at a leaf node that indicates class \(C_{j}\).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Variable** & **Units** \\ \hline Surface pressure & hPa \\ Total column water & kg/m\({}^{2}\) \\ Total column water vapour & kg/m\({}^{2}\) \\
10 m U wind component & m/s \\
10 m V wind component & m/s \\
2 m temperature & K \\ Skin temperature & K \\ NDVI & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of the predictive (input) variables in the database for locust presence estimation, Western Africa.  
* If \(\mathbf{x}\) contains features that could correspond to multiple classes, a feature is chosen for testing. This feature has associated labels or possible outcomes \([y_{1},y_{2},\ldots,y_{n}]\). The training set \(\mathbf{x}\) is then divided into smaller subsets \([\mathbf{x}_{1},\mathbf{x}_{2},\ldots,\mathbf{x}_{n}]\), where each subset \(\mathbf{x}_{i}\) contains the features from \(\mathbf{x}\) associated with the chosen test label \(y_{i}\). This recursive process is applied to each subset.  
In the traditional methodology of constructing decision trees, the key aspect lies in how the dataset is split. The selection of a feature test determines how training objects are distributed into subsets, facilitating the construction of subtrees accordingly [34]. Notable advancements in and variations of this algorithm include C4.5, which employs information theory to evaluate decision tree splits [36].
# Unsupervised Image Classification Based on Fully Fuzzy Voronoi Tessellation
## 3 Proposed FVT-FCM Algorithm
### Objective Function  
Based on the hierarchical fuzzy membership model, there are two key points that need to be addressed. 1. How to measure the accuracy of fuzzy Voronoi tessellation. 2. How to consider fuzzy transitivity and model the relationship between fuzzy polygons and clustering. Focusing on these two difficulties, the objective function of the proposed FVT-FCM is modeled as,  
\[J_{\text{FVT-FCM}}(\mathbf{\mu},\mathbf{W},\mathbf{C},\mathbf{V})=\sum\limits_{i=1}^{n}\sum \limits_{j=1}^{m}\mu_{ij}^{\lambda_{1}}d_{ij}^{2}+\eta\sum\limits_{j=1}^{m} \sum\limits_{l=1}^{k}w_{jl}^{\lambda_{2}}D_{jl}^{2} \tag{21}\]  
where the first term is used to measure fuzzy Voronoi tessellation; it achieves the explicitly expression of tessellation. It can be regarded as a spatial clustering of two-dimensional lattice points. This term can enhance the constraint of spatially adjacent pixels. The second term is used to measure clustering based on fuzzy polygons. In this term, each pixel will participate in clustering due to the fuzzy relationship between pixels and polygons. Meanwhile, fuzzy polygons will be treated as a whole. Equation (12) greatly ensures the flexibility of pixels when establishing constraints, which provides favorable conditions for balancing noise resistance and boundary fitting. \(\mathbf{\mu}\), \(\mathbf{W}\), \(\mathbf{C}\), \(\mathbf{V}\) are model parameters to be solved; \(\eta\) is a balance coefficient. For the convenience of determining the value range, \(\eta\) is rewritten as \(q/R\), \(q\) generally takes a value of 0.1, and \(R\) is a floating parameter determined by the complexity of the image. \(\lambda_{1}\) and \(\lambda_{2}\) are fuzzy factors for the Voronoi tessellation term and clustering term, respectively. They are usually determined empirically. If the image is fragmented, \(\lambda_{1}\) or \(\lambda_{2}\) are set to a smaller value. \(d_{ij}\) is the spatial distance between pixels and fuzzy polygons, without spectral features involved. It can be defined by the distance measure based on two-dimensional coordinates, such as the Euclidean distance.  
\[d_{ij}=\sqrt{\left(x_{i}-a_{j}\right)^{2}+\left(y_{i}-b_{j}\right)^{2}} \tag{22}\]  
\(D_{jl}\) is the polygon level distance between fuzzy polygons and clusters. In order to consider the transitivity of fuzzy, the pixel level fuzzy membership is introduced into the polygon level distance,  
\[D_{jl}=\sum\limits_{i=1}^{n}\mu_{ij}^{\lambda_{1}}\phi_{il}^{2} \tag{23}\]  
where \(\phi_{il}\) is the spectral distance between pixel \(i\) and cluster \(l\). It can also be defined by Euclidean distance.  
\[\phi_{l^{\prime}}=\left\|\mathbf{I}_{i}-\mathbf{V}_{l}\right\|_{2}=\sqrt{\sum\limits_{ s=1}^{h}\left(I_{is}-V_{ls}\right)^{2}} \tag{24}\]
# Low Complexity Equalization of Orthogonal Chirp Division Multiplexing in Doubly-Selective Channels
## 3 Low Complexity Equalizers for UP-OCDM under Doubly Selective Channels
### Iterative LSQR Block Equalizer  
In this subsection, we propose an iterative block equalizer for UP-OCDM system based on the circulant structure of the transform matrix and the LSQR algorithm. We will derive that the channel matrix in the transform domain is a sum of the product of several diagonal and circulant matrices under the doubly selective channels modeled by basis expansion models. Based on this structure, the channel matrix between the time-domain receive signal and the transmitted symbol vector can be easily reconstructed, and the transmitted symbol vector could be directly estimated via LSQR algorithm iteratively.
## 3 Simulation and Verification
### Effectiveness Analysis of DS-MPC Method under 5% Slope  
Under this operating condition, the vehicle was driven on flat ground at 0-5 s, uphill at 5% after 5 s, with no slope at 25-35 s, downhill at 5% at 35-55 s, and ended on flat ground at 55-60 s. Figure 12 shows the tracking of the vehicle speed under this operating condition. Overall, the vehicle speed was slightly below the reference value when driving in the calm driving style, while the moderate and aggressive styles were slightly above the reference value, in line with the previous description of driving styles. At the same time, the aggressive and moderate styles were more responsive than the calm style when reaching maximum speed, and they shifted earlier when shifting downhill. On a steeper slope, the aggressive driving style control response was faster, but it also increased the instability of the system.  
Figure 13 reflects the fuel consumption at a 5% slope. Overall, more fuel was consumed accelerating uphill than decelerating downhill, which is in line with reality. In the uphill phase, the aggressive driving style consumed more fuel more quickly as it needed to maintain a higher speed variation, whereas the calm driving style consumed less fuel as the speed on the uphill was closer to the desired speed and the speed variation was smoother.  
Figure 12: Speed tracking curve for drivers with different driving styles.  
Figure 13: Fuel consumption change curve of drivers with different driving styles.  
The brake cylinder pressure change curve for drivers with different driving styles is shown in Figure 14. In the downhill phase, the calm driving style produced higher fuel consumption due to frequent driver braking, resulting in frequent speed changes, while the aggressive and moderate driving styles consumed similar fuel and were comparable to their descriptions.
# A Survey on an Energy-Efficient and Energy-Balanced Routing Protocol for Wireless Sensor Networks
## 3 Terminologies Used in WSNs
### Distance Metric  
The distance metric or hop-distance is an important parameter considered in the design of routing protocols for WSNs. The distance metric defines the distance between SNs or the distance between a SN and the BS. In clustering routing protocols, the distance metric defines the distance between a CH and a nCH, the distance between a CH and the BS, or in some cases, the distance between a nCH and the BS. Consider the clustering WSN of Figure 1; if the coordinates of \(SN_{3}\) and \(SN_{6}\) are defined as \((A,C)\) and \((B,D)\) respectively, the hop-distance between these nodes can be computed in the form of a distance metric as:  
\[d=\sqrt{(SN_{3_{A}}-SN_{6_{B}})^{2}+(SN_{3_{C}}-SN_{6_{D}})^{2}}. \tag{3}\]  
In designing most routing algorithms, it is desirable that \(d\) is as small as possible in order to minimise the transmission energy. The distance metric \(d\) is directly proportional to the transmission energy. Hence, most energy-efficient and energy-balanced routing protocols strive to minimise the value of \(d\) in choosing the route(s) from the source node to the BS. The hop-distance is therefore an important decision variable in developing an energy-efficient and energy-balanced routing protocol.
## 2 General Scheme of the Detection System
### Measurement Systems  
The measurement system, shown in step A of Figure 4, consists of an industrial vision camera frame and a vision measurement unit, which includes a CMOS image sensor with a resolution of 5496 \(\times\) 3672 and an image element size of 2.4 \(\upmu\)m, and a high-temperature ceramic checkerboard calibration plate with an accuracy of 0.001 mm.
# Design of a Device Based on an LED Matrix for Water Sterilization
## 2 Materials and Methods
### Encapsulation Procedure  
Ethylene tetrafluoroethylene (ETFE) films (NOWOFLON ET 6235 Z, Nowofol, Siegsdorf, Germany) were utilized for both the top and back layers (150 \(\upmu\)m thick). Ethylene vinyl acetate (EVA) was selected as the lamination material; specifically, a 125 \(\upmu\)m-thick EVA film (Photocap(r)15420P/UF, Specialized Technology Resource, STRE, Llanera, Spain) was employed. Optimal adhesion between ETFE and EVA was attained through corona treatment of ETFE. Encapsulations were conducted utilizing lamination equipment (P.Energy Company L036A, Padova, Italy) at a processing temperature of 140 \({}^{\circ}\)C, with a vacuum cycle of 4 min and a pressure cycle of 8 min. The encapsulation structure consisted of: ETFE + EVA + LED + EVA + ETFE. Initially, LEDs were encapsulated individually, followed by the design of a prototype incorporating the most suitable LED in terms of cost and efficiency. To ensure uniform UV light emission throughout the prototype, LED strips were welded in series, maintaining consistent distances between each LED in all directions.
# A Novel Self-Adaptive VM Consolidation Strategy Using Dynamic Multi-Thresholds in IaaS Clouds
## 2 Preliminaries  
### Virtual Machine Consolidation  
The procedure of VM consolidation can be comprised of three core components: (1) Source PM selection, (2) Target VM selection and (3) VM placement. In (1), a set of PMs are selected for VMs to migrate out. This component takes all the PMs and VMs as input and selects one or more PMs as the source PM. In (2), one or more VM(s) are selected from a source PM for migration. This component takes the source PM as input which has been selected by step (1) and is going to select one or more VMs from this PM to a different PM. In (3), a PM is selected to hold the VM selected from (2) or the new VM requests.  
As portrayed in Figure 1, VMs are scattered in multiple PMs before VM consolidation is applied, with PM 2 only holding one VM while PM 1 holds 5 VMs at high overload. On one side, PMs at overload status can easily cause high SLA violation. On the other side, underloaded PM 2 with only one VM in it still needs to remain alive, which increases unnecessary energy costs. Therefore, if no VM consolidation strategy is interposed to the inefficient usage of computing resources, much extra energy will be wasted, as well as lower QoS.  
Based on different threshold strategies of VM consolidation, the status of each PM can be divided as overload, moderate or underload. If a PM is defined as underload, then all VMs within it would be migrated out and it will then be set to the sleep mode or shut down to reduce energy costs (PM 2). While VMs in the moderate PM will not be migrated, this PM would become one of the target PMs for some VMs to migrate in (PM 3). Besides, in order to prevent a potential high SLA violation, an overload PM would migrate some selected VMs to others (PM 1). Therefore, after adding VM consolidation strategy, there are only two servers alive in the system an both of them are at moderate status. In addition, the underload server (PM 2) is shut down, which further decreases the energy consumption.
# A Survey on Mobility Support in Wireless Body Area Networks
## 3 Mobility Model and Communication Protocol
### Individual Mobility
#### 3.1.1 Mobility Model  
Until now, most of the research for the mobility model focuses on single node mobility describing a person's movement. The well-known and cited models include Random Walk, Random Waypoint and Random Direction that belong to the memory-less model [14]. On the other hand, Random Gauss-Markov Mobility [15] was proposed to get the value of speed and direction according to Markovian property. However, these models cannot be directly applicable for WBAN because they are intended to generate movement trace for each node, respectively. Thus, it is not feasible to produce relative position information of multiple sensor nodes on the body. To defeat this deficiency, [PERSON] et al. [16] introduced detailed posture models for standing, sitting, laying, walking and running. As an example, sitting is modeled by posture on the chair. In this case, trunk of the human body shows little movement while larger dynamics are observed for human arms and legs. By this observation, they estimated the position of each sensor on knees and elbows. In the same way, mobility patterns for walking, running, and laying are collected by the distance and direction. In addition to each behavior modeling, selection between posture patterns is modeled by Markov chain, which presents the probability of posture change from one state to another. Also to study of the new mobility model, they analyzed the impact of mobility for performance from the aspect of energy consumption, delay and path loss.  
Figure 2: Categorization of mobility support for human groups in wireless body area networks.
# Fluid Net Models: From Behavioral Properties to Structural Objects
## 7 Concluding Remarks  
Equilibrium throughput is a performance index of manufacturing systems that measures in \"steady state\" the quantity of goods fabricated per unit time. When the manufacturing system is modeled as a TCPN, then the equilibrium throughput represents the transition flow. This flow depends on the net structure, the firing rate of transitions, and the TCPN initial marking. Given a net structure, from an engineering point of view, both the firing rate and the marking can be varied to modify the equilibrium throughput. However, despite the simple formula of the equilibrium throughput \(f_{e}=\mathbf{\Lambda}\mathbf{\Pi}_{C}\mathbf{m}_{e}\), this work illustrated that a careful analysis must be carried out when those parameters vary. In fact, they may lead to very complex behaviors of the TCPN equilibrium throughput.  
In particular, this work showed that the structural objects named problematic configurations are responsible that varying the initial marking or firing rate of transitions the equilibrium throughput of the net exhibits paradoxical behaviors, such as non-monotonicities and discontinuities. To explain those behaviors, we start by delineating how the equilibrium marking varies when the firing rate of transitions changes. In this case, the state equation \(\dot{\mathbf{m}}=\mathbf{C}\mathbf{\Lambda}\mathbf{\Pi}_{C}\mathbf{m}\), \(\mathbf{m}\in\mathcal{R}_{C}\), \(\mathcal{C}\in SC(N)\), \(\mathbf{m}(0)=\mathbf{m}_{0}\) is used. This work points out that when there exist closed paths gaining/losing tokens and the firing rate of the transitions varies, the equilibrium marking may exhibit jumps, generating undesired behaviors, such as discontinuities and non-monotonicities, in the equilibrium throughput with respect to the variation of firing rates.  
The structural objects named _problematic_ configurations are considered to formally explain how the structure of the net determines the undesired behaviors in the equilibrium throughput. Those configurations highlight how the net structure participates in the generation of discontinuities and non-monotonicities in the equilibrium throughput when they vary the initial marking, \(m_{0}\), or firing rates of transitions, \(\lambda\).  
Herein, we show that when the equilibrium marking exhibits a discontinuity w.r.t. firing rate variations, then it is non-monotonic w.r.t. firing rate variations, i.e., increasing the speed of the resources reduced the equilibrium throughput, which is an undesired effect. Moreover, if the equilibrium is not deadlock free w.r.t. firing rate variations, then it is non-monotonic w.r.t. firing rate variations. Similar properties are found when the initial marking varies instead of the firing rate.  
It is worth to note that the number of configurations may grow exponentially in the number of join transitions. Nevertheless, a procedure to reduce the net, preserving the undesired equilibrium throughput behaviors, is described. It allows to face the problem of finding out a problematic configuration in a reasonable amount of computational time.
## 3 Materials and Methods
### Equipment  
The water jet-guided quasi-continuous laser cleaning equipment used in the experiment is shown in Figure 2. The equipment is mainly composed of a quasi-continuous fiber laser (YLR-150/1500-QCW-MM-AC, IPG), beam shaping device, water-optic coupling device, high-pressure pure water device, three-axis precision mobile platform, and computer control system. The maximum average power of the fiber laser is 150 W, the pulse frequency is adjustable in the range of 10-50 kHz, and the minimum pulse width is 20 \(\upmu\)s. The diameter of the water beam fiber is about 0.5 mm, and the water jet remains stable in the pressure range of 0.8\(\sim\)1.6 MPa. The design and manufacture of water jet-guided laser cleaning equipment conforms to international standards, relevant safety requirements, and environmental standards of China. The geometric accuracy, working accuracy, and tolerance of the water jet-guided quasi-continuous laser cleaning equipment conform to relevant standards. A national standard is adopted for the measurement units of all parts and various instruments.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Element** & **C** & **Mn** & **P** & **S** & **Si** & **Cr** & **Ni** \\ \hline Mass fraction/\% & \(\leq\)0.08 & \(\leq\)2.0 & \(\leq\)0.045 & \(\leq\)0.03 & \(\leq\)1.00 & 18.0–20.0 & 8.0–11.0 \\ \hline \end{tabular}
\end{table}
Table 1: Chemical composition and content of 304 stainless steel.  
Figure 1: Sample appearance and coating thickness.  
In the process of water jet-guided quasi-continuous laser cleaning, the pulsed parallel beam emitted by the laser is converted into a Gaussian-focused beam through a beam shaping system. The focused spot is coupled with the steady-state water jet in the water-laser coupling device to form a water beam fiber with a uniform laser energy distribution. Then, the water beam fiber irradiates the part to be cleaned of the sample so that the attached coating, paint, rust, and other pollutants absorb the laser energy and undergo a series of complex physical and chemical reactions (including vibration, melting, combustion, and gasification). Eventually, the contaminants are removed from the sample surface. The sample is placed on a three-axis precision mobile platform, and a water jet-guided laser cleaning experiment can be realized by controlling the movement of the mobile platform in the X and Y directions.  
After the water jet-guided laser cleaning experiment is completed, the surface morphology of the cleaned area is observed by a 3D digital microscope (DSX1000, OLYMPUS), and the surface roughness is measured by an optical profiler (Micro-XAM100, KLA Tencor). Field emission scanning electron microscopy (Apreo, FEI) is used to analyze the microscopic morphology of the cleaned area, and an X-ray energy dispersive spectrometer (EDS) attached to the SEM was used to detect the element content of the cleaned area.
# _Bacillus subtilis_ Simultaneously Detoxified Aflatoxin B1 and Zeralenone
## 2 Materials and Methods
### Effects of Various Factors on the Degradation of AFB1 and ZEN by B. subtilis ZJ-2019-1  
The _B. subtilis_ ZJ-2019-1 strain was cultured until it reached the logarithmic growth phase and then centrifuged at room temperature for 5 min at a speed of 6000 rpm. Subsequently, the bacterial cells were washed with sterile PBS and finally resuspended in PBS to achieve an optical density (OD\({}_{600}\)) of 0.3-0.5 [33; 34].  
To investigate the impact of temperature, AFB1 or ZEN at a concentration of 10 mg/L was added to _B. subtilis_ ZJ-2019-1 culture (2% inoculation, pH 7.0), which was incubated for 72 h at temperatures ranging from 22 to 42 \({}^{\circ}\)C [33]. Samples were collected and the degradation rate of mycotoxins was determined using HPLC as described above. To examine the effect of different pH values on the degradation rate, AFB1 or ZEN at a concentration of 10 mg/L was added to the bacterial culture adjusted to various pH values (4.0, 5.0, 6.0, 7.0, 8.0, or 9.0), followed by incubation for 72 h at 37 \({}^{\circ}\)C. Regarding incubation time, AFB1 or ZEN (10 mg/L) was added into _B. subtilis_ ZJ-2019-1 and then incubated at 37 \({}^{\circ}\)C for varying time: 0, 6, 12, 24, 36, or 48 h [33]. Additionally, to evaluate the effect of mycotoxin concentration on degradation rate, different concentrations of AFB1 or ZEN (2, 5, 10, 20, or 40 mg/L) were added into _B. subtilis_ ZJ-2019-1 and incubated for 48 h at 37 \({}^{\circ}\)C. All experiments were conducted in triplicate. Following incubation, samples were collected and analyzed using HPLC. The residual rate and degradation rate of mycotoxins were calculated by the following formulas [35]:  
Residual rate (%) = peak area of the experimental group/peak area of the control group \(\times\) 100%  
Degradation rate (%) = (peak area of the control group \(-\) peak area of the experimental group)/peak area of the control group \(\times\) 100%