esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Evaluation Metrics of NQG  
BLEU, an automatic evaluation method frequently used in machine translation tasks, determines the level of similarity of the translated candidate sentence with respect to the reference sentence. The presence of overlapping words and differences in the sentence length is compensated, and the geometric average is derived by changing the number of sequences (n-grams) of words of the candidate sentence that match with that of the reference sentence. BLEU-N can be expressed as follows [27]:  
\[BLEU=BP*\exp(\sum_{n=1}^{N}w_{n}\text{log}P_{n}) \tag{1}\]  
Here, \(P_{n}\) refers to precision n-grams corrected for consecutive occurrences of the same word, and \(w_{n}\) refers to the weight for the precision of each gram. In this paper, overlapping sequences from grams 1 to 4 were calculated, and the weight was fixed at 1/4 each. In addition, BP is calculated as follows to provide a disadvantage when candidate sentences are either longer or shorter than the length of the reference sentence, which is the standard when translating.  
\[BP=\begin{cases}1&\text{for}\quad c>r\\ \exp\Bigl{(}1-\frac{r}{c}\Bigr{)}&\text{for}\quad c\leq r\end{cases} \tag{2}\]  
In addition, the ROUGE-L score used in this paper is a modified model of ROUGE. The method allows measurements of similarity using the Longest Common Subsequence (LCS) between candidates and references [28] and, as the F-score is obtained for the longest sequence in the candidate question, a greater value indicates higher accuracy in the word order of the translated sentence. Unlike other ROUGE score methods, implementing the LCS eliminates the requirement for consecutive matches. Moreover, as the longest in-sequence common n-gram can be automatically identified, the length of the n-gram need not be defined in advance. While BLEU focuses on precision, which refers to the frequency of n-grams in machine-generated translated questions occurring in the reference sentences, the ROUGE score functions in the measurement (recall) of the number of grams of the reference sentence that appears in the candidate sentence. Therefore, the two scores are complementary.  
For assessment of similarity between two sentences, the reference sentence is defined as X, the length of X as m, the candidate sentence as Y, and the length of Y as n. Then, recall (\(R_{lcs}\)) and precision (\(R_{lcs}\)) are calculated in accordance with the formula below, and \(F_{lcs}\) is applied to the final formula of ROUGE-L. Parameter \(\beta\) plays functions in adjusting the relative significance of \(P_{lcs}\) and \(R_{lcs}\). The ROUGE-L is calculated as follows:  
\[R_{lcs}=\frac{LCS(X,Y)}{m} \tag{3}\]  
\[P_{lcs}=\frac{LCS(X,Y)}{n} \tag{4}\]  
\[F_{lcs}=\frac{(1+\beta^{2})R_{lcs}P_{lcs}}{R_{lcs}+\beta^{2}P_{lcs}} \tag{5}\]
###### Abstract
The change in demand orders within the supply chain is often amplified as they move upstream, a phenomenon known as the bullwhip effect [27]. When managers design production plans based on inaccurate demand information, it can lead to excessive product inventory and increased inventory costs. The bullwhip effect can be measured in various ways, such as by comparing the variance between demand and orders, using different demand models, and considering cost factors [28]. The choice of measurement method should align with the research purpose and context. In this paper, we measure the bullwhip effect using the ratio of operating cost \(y\) to uncertain demand \(\gamma\). \[\frac{\|y\|_{2}}{\|\sigma\|_{2}}\leq\gamma \tag{1}\]  
**Remark 1**. : _Where \(\|\cdot\|_{2}\) denotes the \(l_{2}\) norm. The equation suggests that when \(\gamma\) is smaller, indicating a lower operating cost to address uncertain demand, the attenuation performance of the bullwhip effect improves. By focusing on reducing the impact of uncertain demand, we can transform the problem of attenuating the bullwhip effect into a control problem, with the attenuation of uncertain demand serving as the control objective._  
The main contributions of this paper are outlined as follows. First, the paper transforms the challenge of suppressing the bullwhip effect in supply chain management into a control problem. The primary objective of this control approach is to prevent uneven warehouse distribution, thereby reducing inventory costs. Given that \(H_{\infty}\) control technology has demonstrated strong performance in mitigating the bullwhip effect causedby uncertain demand [29], this paper applies \(H_{\infty}\) control technology to the supply chain system to achieve this suppression. Second, the paper introduces trust value information into the supply chain system using the T-S fuzzy model. A productivity and distributed consensus protocol switching controller, which relies on fuzzy rules, is designed. Incorporating trust values allows the supply chain system to better reflect practical and complex supply scenarios, thereby enhancing system stability. Finally, the paper provides sufficient conditions for ensuring \(H_{\infty}\) consistency. In situations where the system experiences a failure at a particular node due to a major emergency, the average dwell time method is utilized to maintain system stability.
###### Abstract
The typical generalized frequency dispersion curves and attenuation curves of the three waves were obtained. [PERSON] et al. [32] established dynamic models of three-phase porous media to research the propagation characteristics of unsaturated soils. The results showed that the fluid phase had a great influence on the frequency dispersion and attenuation characteristics of unsaturated soils, whether under loadings or not. [PERSON] et al. [33] showed that the frequency dispersion and attenuation characteristics of two compressional waves are not only dependent on the range of the loading frequency, but are also are remarkably affected by the permeability of saturated soils. The works mentioned above cannot reflect the effect of permeability on wave propagation characteristics. Moreover, most research on wave propagation properties only focus on one certain saturated soil. The analyses of comparisons between different saturated soils are rare. For the \(\mathbf{u}\)-\(p\) equation, analyses exist that summarize the suitable scope of application with two dimensionless parameters only in one-dimensional soil, and the physical meanings of the two dimensionless parameters are ambiguous. In this paper, for zero, finite, and infinite permeability, the wave equations and velocities of P1, P2, and S waves in saturated soils are acquired, respectively. Based on the solutions of the wave equations, the actual velocities and attenuation coefficients are redefined. Moreover, wave propagation characteristics are compared in saturated soils with different permeabilities. In the end, the suitable scope of the \(\mathbf{u}\)-\(p\) formulation is discussed according to the contrasts of the wave velocities obtained from the \(\mathbf{u}\)-\(p\) and \(\mathbf{u}\)-\(\mathbf{w}\)-\(p\) formulations.
# A Thermal Discomfort Index for Demand Response Control in Residential Water Heaters
## 6 Application of the Proposed Model: TDI Calculation
### Scenario #2: Evaluation on a Group of Households with Water Heaters  
Another challenge related to the definition of TDI is to prioritize which group of water heaters should be interrupted to reduce electricity demand while minimizing discomfort (e.g., for demand response management in a peak situation). In this experiment, a group of twenty households \(H\) with electric water heaters was studied using the developed simulator. Ten households (\(H_{1},\ldots,H_{10}\)) have a water use profile concentrated in the morning hours and the other ten households (\(H_{11},\ldots,H_{20}\)) in the evening hours. Figure 9 presents the average water use profiles for each type of household.  
A simulation of a complete day for the twenty defined households was performed. In turn, the TDI associated with a 20 min interruption between 7:10 and 7:30 (\(TDI_{m}\)) and the TDI associated with a 20 min interruption between 20:10 and 20:30 (\(TDI_{a}\)) were computed for the twenty households using the method presented in Section 4.4.  
Table 3 reports the computed TDI values for each household. Table 4 presents the two rankings defined: \(Ranking_{m}\) sorts households according to the values of \(TDI_{m}\) and \(Ranking_{a}\) sorts households according to the values of \(TDI_{a}\), both from lowest to higher values. \(H_{1},\ldots,H_{10}\) households are highlighted in green and \(H_{11},\ldots,H_{20}\) in blue.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Appliance** & \(\rho=1\) & \(\rho=2\) \\ \hline \(EWH_{1}\) & 3362.3 \({}^{\circ}\)C s & 4108.2 \({}^{\circ}\)C s \\ \(EWH_{2}\) & 8109.6 \({}^{\circ}\)C s & 11,041.7 \({}^{\circ}\)C s \\ \hline \hline \end{tabular}
\end{table}
Table 2: TDI applied for the interruption for \(EWH_{1}\) and \(EWH_{2}\).  
Figure 8: Probability distribution for the water utilization of two electric water heaters. (a) \(EWH_{1}\); (b) \(EWH_{2}\).  
For the presented case study, and considering that the probability of affecting comfort when interrupting water heaters in the morning is higher for households \(H_{1},\dots,H_{10}\), these ten households are expected to be in the last ten positions of \(Ranking_{m}\). Analogously, \(H_{11},\dots,H_{20}\) are expected to be in the last ten positions of \(Ranking_{a}\). Results in Table 4 confirm that the TDI properly sorts the water heaters according to the real discomfort produced for users, as expected. These results allow for very precise information to decide about specific demand-management interventions.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c c c c c} \hline \hline \multicolumn{11}{c}{**Order**} \\ \hline
**1** & **2** & **3** & **4** & **5**
## 4 Proposed Cooperative Operation Method
### Capacity Selection
When an inductor and capacitor of arbitrary capacities are used, the zero-phase voltage induced at the tertiary side of the GPT in response to a ground fault can be calculated as follows. Figure 9 depicts a current \(I_{N}\) flowing through the neutral line on the primary side of the GPT in response to a bolted SLG fault occurring on the first line of the AC side of a system, in which an inductor and capacitor are additionally grounded in series. The current \(I_{CLR}\) flowing to the tertiary side of the GPT and the induced zero-phase voltage \(V_{CLR}\) can be calculated according to Equations (2)-(4). This indicates that when the inductor-capacitor is additionally grounded, the zero-phase current flowing through the GPT is reduced and the induced zero-phase voltage is reduced. \[I_{N}=\frac{E}{R_{g}+r_{n}+j\omega(L_{GPT}+L_{Spt})+\frac{1}{j\omega C_{GPT}}} \tag{2}\]  
\[I_{CLR}=\frac{n}{3}\times I_{N}=\frac{n}{3}\times\frac{E}{R_{g}+r_{n}+j\omega \big{(}L_{GPT}+L_{Spt}\big{)}+\frac{1}{j\omega C_{GPT}}} \tag{3}\]  
Figure 9: Equivalent circuit of current flowing to primary side of GPT in response to 1-wire ground fault. \[V_{CLR}=R_{CLR}\times I_{CLR}=R_{CLR}\times\frac{n}{3}\times I_{N} \tag{4}\]  
\(I_{N}=\) Current flowing in GPT primary side neutral wire in response to SLG fault  
\(E=\) Grid phase voltage  
\(R_{g}=\) Fault resistance  
\(n=\) Turn ratio of primary side and tertiary side of GPT  
\(r_{n}=\) Resistance when converting CLR to GPT primary side  
\(L_{CPT}=\) Reactor grounded to primary side of GPT  
\(L_{gpt}=\) GPT inductance of primary side  
\(C_{CPT}=\) Capacitor grounded to GPT primary side  
\(R_{CLR}=\) CLR resistance  
\(I_{CLR}=\) Current flowing to tertiary side of GPT  
\(V_{CLR}=\) GPT tertiary zero-phase voltage.
# Recent Advances and Applications of Semiconductor Photocatalytic Technology
## 3 Common Classification of Photocatalysts
### Oxide Photocatalyst
#### 3.1.3 Other Oxide Photocatalysts  
Transition metal elements of the IV cycle, such as titanium, chromium, manganese, iron, cobalt, nickel, copper, and zinc [85; 86; 87; 88], as well as their corresponding oxides, have excellent physical and chemical properties. These excellent physicochemical properties include a high redox potential, multivalence, chemical resistance, and a high temperature resistance. The storage of transition metal elements is relatively abundant and has a low price, which means that transition metal oxides have good application prospects in photocatalysis.  
Next, we will introduce the three kinds of transition metal oxides generally used, ZnO, WO\({}_{3}\), and Fe\({}_{2}\)O\({}_{3}\).  
ZnO is one of the few oxide semiconductor materials that can achieve a quantum size effect, and ZnO has good UV absorption and piezoelectric properties. The forbidden band width of ZnO is 3.2 eV, which corresponds to ultraviolet with a wavelength of 387 nm. Studies on ZnO have found that there is a certain relationship between the surface composition and structure of ZnO nanoparticles and their photocatalytic properties. Oxygen vacancies on the ZnO surface tend to capture photogenerated electrons, and there is a strong interaction between oxygen vacancies and adsorbed oxygen, which is beneficial for the oxidation reactions.  
WO\({}_{3}\) is one of the metal oxides having photocatalytic activity. WO\({}_{3}\) shows several advantages, including a large specific surface area, excellent absorbing capability, and its potential to be used as invisible material. The band gap of WO\({}_{3}\) is 2.8 eV, and WO\({}_{3}\) is stable. WO\({}_{3}\) can be used both as a main catalyst and as a cocatalyst.  
Iron has long been investigated as an important meta-element in the redox reaction. Among iron oxides, Fe\({}_{2}\)O\({}_{3}\) has been widely studied for its high photocatalysis activity. Fe\({}_{2}\)O\({}_{3}\) is an n-type semiconductor, having a forbidden band width of 2.2 eV, a strong light absorption capability in the visible light region, and the ability to absorb a part of sunlight. Fe\({}_{2}\)O\({}_{3}\) can be used as a reducing agent for the photocatalytic reduction of silver ions [89; 90] and can also be used to decompose reactive dyes [91; 92; 93].
## 6 Non-Linear Methods
### Modeling Complex Physical, Chemical, and Biological Processes  
The food industry frequently deals with processes governed by complex physical, chemical, and biological interactions. Non-linear methods excel in modeling such systems, enabling the optimization of intricate processes that linear methods cannot adequately address. For instance, fermentation, a common process in food and beverage production, involves non-linear dynamics influenced by factors such as temperature, pH, and nutrient availability. Using NLP, producers can optimize fermentation conditions to maximize product yield and quality while minimizing energy consumption [57].  
Quantitative studies highlight the efficacy of non-linear methods in this domain. For example, an optimization model for yogurt production demonstrated a 20% increase in probiotic bacteria viability by fine-tuning fermentation parameters using non-linear methods [58]. This approach not only improved product quality but also enhanced consumer health benefits, aligning with SDG 3 (Good Health and Well-being).
# A Phase Angle-Modulated Bat Algorithm with Application to Antenna Topology Optimization
## 4 Experimental Results and Discussions
### Compact Dual-Band Planar Monopole Antenna Design
#### 4.2.1 Antenna Topology Optimization Problem Formulation  
In antenna topology optimization, the design area is divided into pixel blocks of the same size, and air and conductor are filled in each pixel block according to the binary mapping relationship. Therefore, the problem of antenna topology designs can be expressed as a binary optimization problem:  
\[find\ x^{*}=\arg\min_{x}f(x) \tag{16}\] \[s.t.x\in\{0,1\}^{n}\]  
where \(x=[x_{1}x_{2},\,\dots\,\,x_{n}]\) is an \(n\)-dimensional binary design variable, representing a binary code mapped from the specific antenna geometric structure; \(\mathbf{x}^{*}\) is the obtained optimal antenna design result;\(f(\mathbf{x})\) is the objective function of the antenna design, such as reflection coefficients, gain, or efficiency. Figure 6 shows the mapping process of the antenna structure. If the pixel is filled with the conductor, it is mapped as 1; otherwise, it is mapped as 0.
## 3 The Proposed Resource Allocation Scheme for D2D Communication  
The optimization problem formulated in (12) is an MINLP problem. In general, MINLP problems are known to be non-deterministic polynomial-time hard (NP-hard) and no efficient polynomial-time solutions exist, as the complexity may increase exponentially with the problem size. Moreover, it is not difficult to see that the problem (12) is a non-convex function since both \(\pi_{k,m}\) and \(p_{k,m}^{\mathrm{D}}\) are not jointly concave in \((\pi_{k,m},p_{k,m}^{\mathrm{D}})\).  
Despite this difficulty, this section focuses on solving the resource sharing optimization problem. To proceed, we use \(\mathcal{M}_{k}=\left\{m\in\mathcal{M}|\pi_{k,m}=1\right\}\) to denote the set of the subcarriers which are assigned to the \(k\)th D2D pair. All \(\mathcal{M}_{1},\mathcal{M}_{2},\ldots,\mathcal{M}_{k}\) are subsets of \(\left\{1,2,\ldots,M\right\}\). Then, problem (12) can be described as  
\[\begin{array}{ll}\max\limits_{\left\{p_{k,m}^{\mathrm{D}},\pi_{k,m}\right\} }&\sum\limits_{k=1}^{K}\sum\limits_{m=1}^{M}\pi_{k,m}[R_{k,m}^{D}(p_{k,m}^{D} )+R_{k,m}^{C}(p_{k,m}^{D})-R_{k,m}^{C}]\\ s.t.&\mathrm{C1}\sim\mathrm{C6}\text{ in }\eqref{eq:constraint},\\ &\mathrm{C7}:\sum\limits_{m\in\mathcal{M}_{k}}\pi_{k,m}=|\mathcal{M}_{k}|, \forall k\in\mathcal{K}.\end{array} \tag{14}\]  
Constraint C7 represents the number of subcarriers that the D2D pair \(k\) prepares for reusing.  
Problem (14) is also a MINLP problem, which still needs a large amount of calculation to find the optimal solution. We can solve it efficiently by using a two-stage approach: subcarrier allocation and power assignment, which is verified by many existing similar problems [21; 22]. Specifically, the original optimization problem (12) is decomposed into two individual sub-problems. The first problem is a system positive gain guaranteed subcarriers assignment, where we determine the optimal reuse subcarrier set \(\mathcal{M}_{k}\) for D2D pairs. The second problem is power allocation, where we allocate the transmitted powers for D2D pairs to maximize the sum spectrum efficiency of D2D pairs and CUs in the heterogeneous network.
# A Fast Approach to Optimize Trad Pattern Shape for Tire Noise Reduction
## Results and Conclusions
### The Validation of the Tire Noise Prediction Method
#### 5.3.3 Computational Results  
Based on the optimization method proposed above, the experimental tire pattern was optimized.  
Figure 14 shows the original and optimized results where the noise has been significantly reduced in the target frequency range. It can be seen from the results that the tread pattern shape optimization is effective at reducing noise above 500 Hz. The low-frequency component of the pattern impact is mainly transmitted to the interior of the vehicle through the suspension system, which causes the vibration of the vehicle structures. In this paper, only pattern noise was considered, and low-frequency components were reduced by modifying the objective function.  
Figure 14: Results of pattern shape optimization at different times and at a velocity of 60 km/h.  
Figure 13: The objective function of noise optimization.  
The reduced values at each target frequency are 6.4 dB(A), 9.22 dB(A), 9.08 dB(A), 7.79 dB(A), and 6.55 dB(A), and the maximum reduction rate is 12.1% at the frequency of 1250 Hz. These specific values may differ from those of the experiment. This is mainly because the acoustic radiation characteristics and the coupling effect between the pattern blocks and tire body are not considered in our prediction model. However, these values can provide us with a reference to improve our design sketch in the pattern design stage.  
In terms of optimization time, it takes only 5.5 s to obtain the first optimization solution and only 76 s to produce the final result. Different from regression methods, which include the support vector machine (SVM), relevance vector machine (RVM), ANN [57], and convolutional neural network (CNN) [58] (the RVM, ANN, and CNN take 66,297, 32,428, and 205,694 s for data training [59]), the optimization method proposed in this paper does not need to collect and train data. In addition, there are fewer parameters for pattern parameterization [31]. Last but not least, engineers' design ideas can be integrated into the design of basis vector shapes.  
The optimized pattern shapes are shown in Figure 15. The standard deviations of the exciting force of the original and optimized patterns are 2.62, 2.03, 2.00, and 1.99. The optimized pattern has lower variance and a better Sound Quality Preference Index [53]. Compared with other methods presented in the literature [31], the proposed method, which is based on the linear combination of the basis vector shapes, has fewer optimization parameters when needing to change the pattern shape.
## 1 Introduction  
The unification of public safety-long-term evolution (PS-LTE), LTE-based high-speed railway (LTE-R), and LTE-based maritime (LTE-M) networks is today's dire need in order to maintain communications during a large-scale disaster [1; 2]. In the Republic of Korea, the same 700 MHz band is allocated to PS-LTE, LTE-R, and LTE-M networks [2; 3; 4]. So, a well-defined, centralized operational policy is needed in order to efficiently control resource-management and co-channel interference (CCI) issues [5]. Korea established the world's first public safety network, which leads the world in its vast investment in the evolution of public safety networks [1]. In current scenarios, public safety (PS) users face hurdles in getting a quick response to any PS situation, because they use different networks, and there is too much delay in sharing disaster information between the concerned authorities. To overcome this issue, a unified disaster management system is required where public safety networks work together. Accomplishing this will result in quick sharing of factual information from a disaster, in order to tackle it with correct safety measures. Moreover, the deployment of the mobile personal cell (mPC) can play a vital role in resolving the user-association problem in complex scenarios like the coexistence of two public safety networks. However, this creates new challenges from an mPC's unplanned deployment and its mobile nature in overlapped networks.  
The deployment of an mPC can boost system performance and improve user quality of service (QoS) by deploying the mPC near the desired area, such as high-traffic zones in commercial areas, and in PS situations, etc. Thus, more users can be associated with the mPC in order to achieve an offloading gain in the heterogeneous network (HetNet) environment. Figure 1 shows deployments in PS situations under overlapping PS-LTE and LTE-R networks. However, the unplanned deployment of mPCs generates serious user-association issues due to their mobile nature, which leads to severe co-channel interference for coexisting PS-LTE and LTE-R networks. Conventionally, static user-association schemes are unable to associate users dynamically according to PS situations and network load conditions [6; 7]. We investigate the features of our proposed scenario where co-channel interference is the major problem. In this paper, we deploy the mPC wisely in PS situations to reduce the interference and by employing the eICIC scheme is an efficient way to further reduce CCI in overlapped networks.