esa-sceva/satcom-chunk-collection huggingface dataset:
# Different Object Functions for SWIPT Optimization by SADDE and APSO
## 5 Conclusions  
In this paper, different algorithms and different criteria are used to optimize the harvesting power and information quality for the SWIPT system at millimeter wave in the real environment. SADDE and APSO are used to optimize the SWIPT system and compare different types of objective functions. For the SWIPT system, we consider BER and HP for information quality and harvesting power. The first criterion (Equation (9)) can achieve the BER constraint and in the meantime optimize the total harvesting power. APSO has a better convergent speed than SADDE but has the same harvesting power. Although the first criterion can achieve the goal for SWIPT, the criterion must test the weighting in advance to obtain good results. However, the second proposed criterion does not need to set the weighting case by case and this leads to more efficient searching. From the numerical results, we can see that the second proposed criterion is capable of finding the optimized harvesting power under the BER constraint. Thus, we can use the novel proposed criterion (the second criterion) to optimize the SWIPT problem without having to test the weighting case by case in advance. For all these, we finally conclude that our proposed criterion is novel and effective for the SWIPT system.  
Conceptualization, W.C. and C.-C.C.; methodology, W.C. and P.-H.C.; software, C.-C.C. and Y.-T.C.; validation, E.H.L., Y.-L.L. and J.-R.W.; formal analysis, C.-C.C. and P.-H.C.; investigation, Y.-T.C. and E.H.L.; resources, W.C. and J.-R.W.; data curation, Y.-L.L. and W.C.; writing--original draft preparation, Y.-T.C. and P.-H.C.; writing--review and editing, C.-C.C.; visualization, E.H.L.; supervision, W.C.; project administration, C.-C.C.; funding acquisition, W.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Guangxi Higher Vocational Education Reform Project (GXGZJG2020A001) and Undergraduate Innovation and Entrepreneurship Project (2021).  
Not applicable.  
The authors declare no conflict of interest.
## 3 SDN Performance Challenges  
Figure 5 presents the SDN performance challenges. This was derived from several combinations of search key terms for SDN, OpenFlow, flowtable, update, and security threats from various scientific academic research libraries, which include ACM Digital Library, IEEE Xplore, ScienceDirect, MDPI, Springer. Difference flexible search key terms were used in these websites to retrieve papers related to SDN performance challenges. The year of publication was filtered from 2015 to 2020, papers were chosen within the specified period as summarized in Table 1. After scanning, the selected papers totaling to 3905 are shown. It was further shortlisted and categorized into controller overhead, security vulnerabilities, switch flow table update operation, and flow entries management. Finally, a total of 88 papers were considered among all the research libraries websites.  
This work categorizes overhead into two; SDN controller and switches flowtable overhead. For the former in large-scale networks, the controller can easily be overloaded due to the large control message request from different switches, and thus cause high communication overhead and end to end packet processing delays [28]. While for the latter, potentially many rules are required to be installed in the switch flow table to efficiently operate; however, switch storage is limited, and as the number of flow rules increases, it overwhelms the switch storage memory [31]. Therefore, the following sections  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Academic Libraries**} & \multirow{2}{*}{**General Performance**} & \multicolumn{3}{c}{**Performance Challenges Related**} \\ \cline{3-5}  & & **Controller Security, Threat and Overhead** & **Switch Flow Table** & **Others (Load Balancing, etc.)** \\ \hline ACM & 1911 & 512 & 213 & 1186 \\ IEEE Xplore & 714 & 318 & 212 & 184 \\ ScienceDirect & 786 & 232 & 24 & 530 \\ MDPI & 317 & 69 & 68 & 180 \\ Springer & 177 & 23 & 26 & 128 \\ Total & 3905 & 1154 & 543 & 2208 \\ \hline \multicolumn{5}{c}{Considered papers} & \multicolumn{3}{c}{88} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Number of article publications from research libraries.  
Figure 5: Taxonomy of software defined networking (SDN) performance Challenges.  
explain four (4) major challenges: (1) communication overhead, (2) resource limitation, (3) rule update operation, (4) SDN security vulnerabilities.
## 2 Data and Methodology  
### Data  
The NNssm ([[https://data.tpdc.ac.cn](https://data.tpdc.ac.cn)]([https://data.tpdc.ac.cn](https://data.tpdc.ac.cn)), accessed on 1 May 2023) soil moisture raster dataset released by the National Tibetan Plateau Science Data Center was used in this study. The data have a spatial resolution of 36 km \(\times\) 36 km and a time span of 2002-2022 [27]. NNssm was able to accurately reproduce the SMAP (Soil Moisture Active Passive) surface soil moisture content with a global mean error of 0.029 m\({}^{3}\)/m\({}^{3}\)[28]. Following [PERSON] et al. [29], the corresponding root zone moisture was estimated by NNssm using an autoregressive exponential framework combined with soil properties in the study area, which also compared well with in situ observations of surface soil moisture content and outperformed JAXA's (Japan Aerospace Exploration Agency's) and LPRM's (Land Param  
Figure 1: A map of the Songnen Plain.  
eter Retrieval Model's) AMSR-E/2 standard soil moisture data products [30]. Potential evapotranspiration data were obtained from the Global Land Evaportranspiration Model Amsterdam (GLEAM v3) ([[https://www.gleam.eu/](https://www.gleam.eu/)]([https://www.gleam.eu/](https://www.gleam.eu/)), accessed on 1 May 2023). The Priestley and Taylor equation in GLEAM v3 calculates potential evaporation based on observations of surface net radiation and near-surface air temperature. GLEAM was used to maximize the recovery of evapotranspiration-related information from existing data stacks derived from space-based climate and environmental observations [29]. Vapor pressure deficit (VPD) data were obtained from the Community Long-Term Infrared Microwave Coupled Atmospheric Product System version 2 Level 3 product. The GPP dataset was derived from the [PERSON] et al. [30] release of the 0.05\({}^{\circ}\) spatially resolved and 8-day temporally resolved 1982-2018 global GPP dataset. All the above data were resampled using a bilinear interpolation method and uniformly processed to a resolution of 36 km \(\times\) 36 km.
# A Machine Anomalous Sound Detection Method Using the IMS Spectrogram and ES-MobileNetV3 Network
## 3 Proposed Method
### Feature Extraction
#### 3.1.2. SincNet Spectrogram Extraction  
The low and high cut-off frequencies are the only parameters of the SincNet filter learned from data. Consequently, the SincNet spectrogram can enhance frequency axis representation in machine sound signals. Aa a result, the SincNet spectrogram complements the log-Mel spectrogram with acoustic signals along the frequency axis.  
In the field of signal processing, the SincNet filters are rectangular (ideal) band-pass filters. The frequency response of a band-pass filter can be described as the difference between two rectangular low-pass filters. The calculation can be written as follows:  
\[G(f,f_{1},f_{2})=rect(\frac{f}{2f_{2}})-rect(\frac{f}{2f_{1}}) \tag{3}\]  
where \(f_{1}\),\(f_{2}\) are the low and high cut-off frequencies, and \(rect(\cdot)\) is the rectangular function in the magnitude frequency domain. By implementing inverse Fourier transform on the filter function \(G\), we can obtain the impulse response of the filter, which is expressed by the sinc function:  
\[g(n,f_{1},f_{2})=2f_{2}\text{sinc}(2\pi f_{2}n)-2f_{1}\text{sinc}(2\pi f_{1}n),n=1,2,...,L. \tag{4}\]  
where \(L\) is the filter length; the sinc function is defined as \(\text{sin}\,\text{c}(\text{x})=\frac{\text{sin}\,\text{x}}{\text{x}}\). Since function \(g\) is infinitely long in the time domain, a direct calculation would result in the leakage of spectral energy. To mitigate this problem, the Hamming window is used to truncate function \(g\) and to smooth out the abrupt discontinuities at the end of \(g\). The calculation can be written as follows:  
\[g^{w}(n,f_{1},f_{2})=g(n,f_{1},f_{2})\cdot w(n) \tag{5}\]  
where \(w(n)\) denotes the Hamming window, and \(g^{w}\) is a time-domain window function of the SincNet filter. The feature extraction operation of the SincNet filters can be expressed as follows:  
\[x_{k}[n]=x[n]*g_{k}^{w}(n),k=1,2,...,K \tag{6}\]  
where \(x(n)\) is the input original sound signal, \(K\) denotes the number of SincNet filters, and \(g_{k}^{w}(n)\) is the \(k\)-th SincNet filter. Then, 1D batch normalization and the ReLU nonlinear activation function are applied to obtain the filter output. In order to obtain the SincNet spectrogram with the same dimension size as the log-Mel spectrogram, we apply adaptive average pooling for each filter output, \(x_{k}[n]\). The calculation process can be written as follows:  
\[m_{k}[n]=AdaptiveAvgPool(x_{k}[n]) \tag{7}\]  
In this study, the length of the SincNet filter is L = 251 (each filter consists of 251 coefficients).
## 3 Design Consideration on Radiation-Grafted AEM
### Radiation Technique and Mechanism
#### 3.2.2 Simultaneous Irradiation Grafting  
In the simultaneous irradiation grafting method, both polymer backbone and monomers are irradiated simultaneously to produce free radicals. In principle, simultaneous irradiation is the simplest and most common grafting method to modify the polymer and is also appropriate for substrates sensitive to radiation. Undesirable effects of radiation degradation on the polymer matrix are greatly lessened by means of the protective effects from the monomers in the simultaneous irradiation grafting system. Furthermore, the greater number of radicals formed by irradiation on the polymer matrix in comparison to the one attained by the irradiation of monomers indicates an efficient grafting process. However, such a technique also leads to a higher chance of homo-polymerization and rapid deactivation of polymer backbone radicals due to recombination [82]. Simultaneous  
Figure 4: Schematic representation of the direct irradiation (**A**) and pre-irradiation methods (**B**) with the presence of oxygen, also known as the peroxidation method, and (**C**) without the presence of oxygen. Reprinted with permission from [79]. Copyright Elsevier, 2018.  
irradiation produces active sites in the polymer backbone, in the monomer and in the solvent, causing several unwanted side reactions that limit the degree of grafting and produce undesired homopolymers from the monomers. As such, this method requires inhibitors such as Fe\({}^{2+}\) or Cu\({}^{2+}\) or radical scavengers to suppress the formation of homopolymers. Hence, the choice of radiation technique depends on the characteristics of the polymer backbone and monomer and the scale of production.
## 2 Model
### Boundary Conditions  
In addition to the governing equation, the soil and pile displacements should also satisfy the following boundary conditions:  
Figure 1: The analytical model of the kinematic response of end-bearing piles under the excitation of vertical P-waves considering the construction effect.  
1. The continuity conditions between the adjacent soil zones: \[\left\{\begin{array}{l}u_{z,j}(r,z)\big{|}_{r=r_{j}}=\left.u_{z,j+1}(r,z)\right| _{r=r_{j}}\\ \tau_{x,j}(r,z)\big{|}_{r=r_{j}}=\left.\tau_{x,j+1}(r,z)\right|_{r=r_{j}}\end{array},\ j=1,2,\ldots,N+1\right.\] (3) 2. The displacements are \(u_{0}\) at the bottom of the soil layers and pile: \[u_{z,j}(r,z)\big{|}_{z=H}=u_{0},\ j=1,2,\ldots,N+1\] (4) \[\left.w(z)\right|_{z=H}=u_{0}\] (5) 3. The normal stresses are zero at the ground surface: \[\sigma_{z,j}(r,z)\big{|}_{z=0}=0,j=1,2,\ldots,N+1\] (6) \[\left.\frac{\partial w_{p}(z)}{\partial z}\right|_{z=0}=0\] (7) 4. The displacement converges to free-field at infinite: \[u_{z,N+1}(r,z)\big{|}_{r\rightarrow\infty}=u_{N+1}^{f}(r,z)\] (8) 5. The continuity conditions between the soil and pile: \[2\pi r_{0}\tau_{zr,1}(r_{0},z)=f(z)\] (9) \[u_{z,1}(r_{0},z)=w(z)\] (10) At this point, the mathematical model of the problem has been built as shown above.
## 2 Literature Review and Related Work
### Energy-Efficient Strategies in Scheduling Problem  
Currently, solving the energy-efficient scheduling problem in complex manufacturing systems mainly focuses on energy-efficient strategies, aiming to reduce idle energy consumption without deteriorating other objectives [17]. Among these strategies, the block-moving energy-efficient strategy, which makes operations within stages more compact by moving process operations, can effectively reduce idle energy consumption. This is currently an effective method for handling multi-operation and complex multi-stage scheduling problems. [PERSON] et al. [18] addressed the energy-efficient flexible job shop scheduling problem and proposed a two-stage Memetic algorithm (TMA) to solve the problem. In the TMA, a block-moving energy-efficient strategy operator was designed to further optimize total energy consumption and the number of machine restarts without altering the makespan. [PERSON] et al. [19] addressed the energy-aware distributed hybrid flow shop scheduling problem and designed an energy-efficient strategy based on the block-moving strategy. This approach further reduces the energy consumption of the non-dominated solutions without altering the makespan, thereby obtaining superior non-dominated solutions. [PERSON] et al. [20] tackled the multi-objective flexible job shop scheduling problem with controllable processing times by incorporating an energy-efficient strategy based on the block-moving strategy into the individual decoding process, thereby achieving lower individual energy consumption.  
From the aforementioned literature, it can be seen that there are two main approaches to using the block-moving energy-efficient strategy: One is to apply the block-moving strategy only to the solutions in the non-dominated set to further reduce energy consumption; however, this operation may cause the loss of some potential solutions, resulting in a local rather than a global optimum. The other approach is to embed the block-moving strategy into the individual decoding process to avoid losing the global optimal solution; however, such a design can lead to the waste of computational resources and severely slow down the search efficiency of the algorithm. Therefore, it is an urgent need to design an efficient block-moving strategy that improves solution quality and search efficiency without losing the global optimum.
## 8 Future Challenges for the Enhancement of ED Limitations
### Research Challenges Related to the Different SS Methods
This was confirmed in the tutorial work [136] dedicated to the analysis of blind SS (BSS) approaches (requesting no prior knowledge of the PU signal), where a comparison between ED and the EBD covariance approaches was performed. Generally, when compared with other BSS or non-BSS methods (Table 5), the ED technique has the least amount of computational complexity since it does not encompass complex processing of signals. The low computational complexity of the ED method enables the simple implementation of ED and explains why ED compared with other local (non-cooperative) methods for SS currently has the highest representation in real life implementation. The presented comparison of distinct local methods for SS shows that ED has a number of weaknesses (Table 5), and there is no optimal non-cooperative SS technique for every application. Choosing the most appropriate SS technique for local PU signal detection is a major challenge because SS techniques differ in their performance (Table 5). The implementation of an appropriate detection technique is strongly dependent on the application and it must be PU system-oriented in order to maximise the probability of detecting spectral opportunity. These cognitions bring up the question of whether it would be possible to improve sensing accuracy through the parallel implementation of different local SS techniques at the same SU node (Table 4). Such cooperative SS can be of particular interest for the purpose of wideband SS, where, in the first stage, a low complexity detection technique (such as ED) can be used to search for possible idle sub-bands. Additionally, in the second stage, more advanced SS techniques (such as MFD, CFD, END) with higher detection sensitivity can be used whenever the desired performance or accurate idle band detection must be achieved. Some works have proposed a two-stage SS approach with a simple detection method used in the first stage and a more accurate and computationally complex method in the second stage [135; 136; 137; 138]. A side effect of such an approach is found in the increased sensing time and SU hardware costs. This explains why cooperative SS is far from full exploitation. Hence, significant research activities must take place to precisely address the role and advantages of ED as a method that can be used in conjunction with other local SS methods at the same SU.
# Metallurgical Effects of Niobium and Molybdenum on Heat-Affected Zone Toughness in Low-Carbon Steel
## 2 Materials and Methods
### Definition of the Test Materials
The base material microstructure and the refining effect of Nb are demonstrated by light optical micrographs in Figure 3. The Nb-free base alloy V2-a reveals a polygonal ferritic microstructure (Figure 3a) containing carbon-rich phases such as degenerated pearlite (dark contrast) and a small amount of MA-phase (white islands). The microstructure of the Nb-alloyed variant (V2-d) is dominated by fine-grained acicular ferrite. The addition of molybdenum to Nb-free steel (V4-a) prevents the formation of polygonal ferrite and promotes a coarse-grained acicular ferritic microstructure (Figure 3c). Adding niobium to this alloy (V4-d) results in remarkable refinement of this acicular ferritic microstructure (Figure 3d). Figure 2: Properties of the as-rolled materials: (**a**) Relationship of yield strength (R\({}_{\text{p0.2}}\)) and tensile strength; (**b**) ductile-to-brittle transition temperature (50%) determined by Charpy testing (PF: polygonal ferrite matrix, all other acicular ferrite matrices).
# A Flexible Session-Based Recommender System for e-Commerce
## 2 Methods and Literature Review
### Tested SBRS Methods and Practical Scenarios
#### 2.5.3 SBRS Methodology Using Embeddings  
When implementing recommendation methods relying solely on the ongoing session, there are several decisions that should be considered on how to execute such operation. For example, how many previous actions of the ongoing session should be accounted for in the prediction? Or, how the previous actions will be summarized of the ongoing session to represent the behavior so far? The following list outlines all these steps and the parameters that are typically involved.  
1. Obtain the embeddings of the most recent n items a user has visited in their current session (or purchased in previous n baskets in the next-basket task). This parameter affects how well these recommenders function.
2. Use these embeddings to calculate the average vector, u.
3. Calculate the L2 norm or the cosine similarity between the vector u and each of the other dataset items' embeddings
4. The user's recommendations are the top k most similar items.  
We conducted several experiments in our project to assess how these criteria might affect the results. In order to obtain the vector u, we averaged the embeddings of either the last n items the user viewed during a current session or all of the products they purchased in the previous n baskets. We conducted a number of tests and found that the \"temporal locality principle\" typically holds. Results are improved when only the most recent items are taken into account; in other words, recent behavior greatly outperforms activity from the session's very beginning in terms of predicting the next action. In general, all SBRS approaches perform better when a smaller memory of the user activities is taken, as we shall demonstrate later.
# Omni Wheel Arrangement Evaluation Method Using Velocity Moments
## 3 Omni Wheel Arrangement Evaluation Method Using Velocity Moments
### Equation of Balance for Velocity Moments  
From the schematic diagram of the Omni wheel robot shown in Figure 3, the moment generated by each wheel's ground speeds \(V\) is expressed as \(Vl^{\prime}\): where \(l^{\prime}\) represents the distance to the robot turning center relative to the velocity vector. Moreover, \(L^{\prime}\) is expressed by using the position \((x,y)\) and angle \(\theta\) of the Omni wheel as follows:  
\[\begin{split} l^{\prime}&=x\sin\theta-y\cos\theta\\ &=l\sin(\theta-\beta)\end{split} \tag{1}\]  
where \(l=\sqrt{x^{2}+y^{2}}\) and \(\beta=\arctan(\frac{y}{x})\) are the distance and direction of the Omni wheel to the robot turning center.  
Based on the equation relating wheel angular velocity to ground speed: \(V=r\omega\), and previous study [18], the ground velocity of the wheels when advancing the robot in the x and y directions is determined by the inverse kinematics of the n-wheeled Omni wheel robot as follow:  
\[\begin{pmatrix}V_{1}\\ V_{2}\\ V_{3}\\ \vdots\\ V_{n}\end{pmatrix}=-\begin{pmatrix}\cos(\theta_{1})&\sin(\theta_{1})&l_{1} \sin(\theta_{1}-\beta_{1})\\ \cos(\theta_{2})&\sin(\theta_{2})&l_{2}\sin(\theta_{2}-\beta_{2})\\ \cos(\theta_{3})&\sin(\theta_{3})&l_{3}\sin(\theta_{3}-\beta_{3})\\ \vdots&\vdots&\vdots\\ \cos(\theta_{n})&\sin(\theta_{n})&l_{n}\sin(\theta_{n}-\beta_{n})\end{pmatrix} \begin{pmatrix}V_{x}\\ V_{y}\\ \Omega\end{pmatrix} \tag{2}\]  
where \(V_{x}\) and \(V_{y}\) are the velocities of the robot's center of mass in the x and y directions, and \(\Omega\) is the angular velocity of the robot's center of mass.  
The balance equation for the velocity moments to move straight ahead with the velocity \(V=(V_{x},V_{y})\) is expressed by Equation (3).  
\[\begin{split}\sum V_{i}l^{\prime}_{i}&=\sum^{n}_{-}(V_{x} \cos(\theta_{i})+V_{y}\sin(\theta_{i}))l_{i}\sin(\theta_{i}-\beta_{i})=0\\ &=V_{x}\sum^{n}_{-}\cos(\theta_{i})l_{i}\sin(\theta_{i}-\beta_{i})+V_{y} \sum^{n}_{-}\sin(\theta_{i})l_{i}\sin(\theta_{i}-\beta_{i})\end{split} \tag{3}\]  
where the first and second terms in the second line of Equation (3) represent the sum of the velocity moments in the x- and y-directions straight ahead, respectively.  
The following section will discuss how the above equations are applied to evaluate wheel arrangement and estimate the straight-ahead direction.