esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Study Area  
This study concentrated on Chiang Mai, Chiang Rai, and some parts of the Lamphun province in the north of Thailand (see Figure 1), which is surrounded by active faults [1]. The frequency of earthquake events is relatively higher than in other parts of the country. However, the major focus in terms of disaster management is on Chiang Rai, where a major seismic event occurred in 2014 (Phayao fault). In addition, in the same area and located relatively close to the center of the 2014 event, is the Mea Chan fault. This fault has the highest slip rate of all faults in Thailand. The local population is highly interested in what potential damage may occur in a future earthquake.  
From a social perspective, this study area has rapidly increased in terms of its economic status. According to the National Economic and Social Development Board of Thailand (NESDB), the gross provincial products (GPPs) of Chiang Mai and Chiang Rai are $55,218.56 and $37,189.54, respectively (2.58% and 1.74% of the national gross domestic product, respectively) [32]. Chiang Mai's economic activity and development are equivalent to those of Bangkok (the capital of Thailand). In 2017,the Digital Economy Promotion Agency (DEPA) announced investments worth 36.5 million baht for Chiang Mai's \"smart city\" project [33]. The project includes smart transportation, smart agriculture, smart healthcare, and smart tourism. In addition, although the current economic value is not as high as that of Chiang Mai, many development activities are planned for Chiang Rai. In the 2015 Prime Minister's official announcement, Chiang Rai was selected to be in phase II of the development of special economic zones (SEZs), with Asian Development Bank support [33]. Chiang Rai was selected as the connection point for cooperation between six countries: Cambodia, the People's Republic of China, Lao People's Democratic Republic, Myanmar, Thailand, and Vietnam (Greater Mekong subregion). The primary purpose of developing SEZs is to activate local trading competition, reduce the uneven development distribution, and promote regional economic stability to prepare for the Association of Southeast Asian Nations summit.  
The potential investments will expand the infrastructure and increase population density, which will result in higher exposure and vulnerability in terms of risk management, making it essential to obtain data for seismic risk analysis. This study aimed to support the sustainable and resilient development of the area with seismic microzoning, which will be a useful tool in urban planning and disaster management, i.e., preparation, mitigation, response, and recovery.  
Figure 1: Study area: the districts of Chiang Mai, Chiang Rai, and Lamphun with the V\({}_{\text{S30}}\) observation stations.
# An Enhanced Time Series Analysis to Improve the Performance of 5G Communication Systems +
## 3 Results and Discussion
### Bandwidth Management  
With the rising demand for 5G services, the efficient use of spectrum resources is especially important for 5G communication systems.  
Figure 3 exposes the assessment of bandwidth administration. In a computation cycle, the proposed TSA reached 95.34% bandwidth management. The existing FCS achieved 59.94%, MDM reached 33.77%, NMAA reached 86.67%, and SBRA obtained 78.19% bandwidth management.
# Laser Cooling beyond Rate Equations: Approaches from Quantum Thermodynamics
## 2 Materials and Methods  
### Laser Cooling Model  
We consider a simple laser-cooling scheme, depicted in Figure 1, involving an impurity with two states forming a ground-state manifold, and a single state in an excited-state manifold. A driving laser of frequency \(\omega_{l}\) excites the transition from the upper state of the ground-state manifold to an excited state \(|e\rangle\) and energy \(E_{0}\) above the top of the ground-state manifold. We assume this state decays by radiative emission to the ground state. Crucially, the two states within the ground-state manifold, \(|g_{l}\rangle\), \(|g_{u}\rangle\), which are split by an energy \(E_{\text{man}}\), are coupled by the emission and absorption of lattice phonons.  
We will analyze this problem within the standard framework of open quantum systems theory, separating it into parts corresponding to a'system', one or more baths, and the interactions between the system and the baths. In our case, the baths are the phonons, and the continuum of photon modes that give the radiative decay. We describe those aspects of the model in Section 2.4 below. The system, meanwhile, comprises the states of the impurity and their interaction with the laser, which can be treated as a classical driving field [38]. The system Hamiltonian is then (\(\hbar=1\))  
\[H_{\text{S}}=E_{0}|e\rangle\langle e|-E_{\text{man}}|g_{l}\rangle\langle g_{l} |+\Omega\cos(\omega_{l}t)(|g_{u}\rangle\langle e|+|e\rangle\langle g_{u}|),\]  
where \(\Omega\) is the Rabi frequency given by the product of the electric field amplitude of the driving laser and the dipole moment of the transition, \(\mathbf{d.E_{0}}\). The time-dependence of the driving field can be removed, in the rotating wave approximation, by using a unitary transformation  
\[U=\exp(i\omega_{l}t|e\rangle\langle e|). \tag{1}\]In this frame the driving field is time-independent, and the Hamiltonian for the system is  
\[H_{S}=\left(\begin{array}{ccc}-\delta&\Omega/2&0\\ \Omega/2&0&0\\ 0&0&-E_{\rm man}\end{array}\right). \tag{2}\]  
Here \(\delta=\omega_{l}-E_{0}\) denotes the driving laser frequency relative to the transition. The diagonal terms in Equation (2) are the energies of the electronic states, in the rotating frame. The off-diagonal terms are the coupling between those states produced by the electric-dipole interaction with the driving field [38]. The coupling between the impurity states and the lattice phonons is considered in the following, where we discuss master equations for open quantum systems and specify the system-bath interaction Hamiltonian.
## 4 Experiments
### Performance Evaluation  
This article primarily evaluates the performance of the proposed method by comparing it with several baseline methods, including the following:  
1. LibFM [6]. A matrix-factorization-based recommendation method that extracts features from both user-accessed and candidate news and concatenates them as input vectors to the model.
2. DeepFM [7]. Similar to LibFM, it utilizes factorization machines for recommendations.
3. DKN [8]. This approach preprocesses the news using knowledge graphs and learns news representations from news titles using a three-channel convolutional neural network (CNN).
4. NPA [2], which applies personalized attention mechanisms to model users' interests in different contexts and news.
5. NAML [24]. A neural news recommendation method with attentive multi-view learning.
6. LSTUR [25]. A neural news recommendation method that utilizes GRUs to learn user representations.
7. NRMS [11]. A neural news recommendation method that uses single-layer multi-head self-attention to learn user and news representations in the pre-training phase.
8. FIM [20], which leverages dilated convolutions for multi-scale text feature extraction and utilizes 3D convolutions with MaxPooling for fine-grained matching between browsed and candidate news at each semantic level.
9. KIM [26]. This approach primarily employs a graph co-attention network.
10. DDM. The method presented in this paper.  
To ensure a fair comparison, all methods only use news titles. The application results of these methods on MIND-small are summarized in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Train** & **Dev** & **Test** \\ \hline User & 162,898 & 47,187 & 88,898 \\ News & 76,904 & 53,897 & 57,856 \\ Impressions & 199,998 & 50,002 & 100,000 \\ Positive samples & 300,357 & 75,183 & 153,963 \\ Negative samples & 7,060,083 & 1,779,492 & 3,740,561 \\ \hline \end{tabular}
\end{table}
Table 1: Statistics of the dataset.  
From Table 2, it can be observed that traditional feature-based deep factorization models such as LibFM and DeepFM exhibit a relatively lower performance, while end-to-end models such as DKN, NPA, NAML, NRMS, and FIM exhibit relatively better results. This indicates the superiority of end-to-end approaches. Furthermore, the use of knowledge graphs and a three-channel CNN in the DKN for news-title feature extraction results in a lower performance compared to the attention-based NPA, NAML, NRMS, and FIM models. Establishing knowledge graphs in the KIM model is still not an ideal method.  
The method proposed in this paper consistently outperforms other baseline methods in all evaluation metrics. This significant improvement demonstrates that the enhanced two-layer residual connections and two multi-head attention mechanisms improve the news-feature extraction. The enhancement in the pre-training phase contributes to an overall improvement in the news recommendation model's performance.
# A Novel Sucrose Isomerase Producing Isomaltulose from _Raoultella terrigena_
## 3 Results and Discussion
### Molecular Modification of Recombinant Pal-2  
#### 3.5.1 Homology Modeling, Molecular Docking, and Design of Mutant Sites  
Given the 88% amino acid sequence homology between Pal-2 and Pal-1 derived from _Klebsiella_ sp. LX3, the crystal structure of _Klebsiella_ sp. LX3 (PDB: 1M53, resolution: 2.2 A) was used as a template for structural modeling with sufficient reliability. The constructed model of Pal-2 was evaluated by the PROCHECK online website. Consequently, 88.9% of the amino acid residues were in the entirely permissible region, indicating that the quality is sufficient (Figure 5b). The tertiary structure of Pal-2 is similar to that of known Slase and  
Figure 4: Optimization of isomaltulose production. (**a**) Effect of enzyme dosage on the isomaltulose yield. (**b**) Effect of sucrose concentration on the isomaltulose yield. (**c**) Effect of time on isomaltulose yield.  
is composed of three domains (N-terminal domain, C-terminal domain, and sub-domain). The sucrose molecule was docked into the substrate-binding pocket of Pal-2 by using the AutoDock software. The predicted binding sites are H145, R239, R333, H368, and R456 forming a pocket to hold a sucrose molecule (Figure 5a). This model was used for the further investigation for the design of mutants.  
In order to obtain enzymes harboring higher performance, including enzyme activity and conversion rat, molecular modification was executed in this study. Hotspot Wizard is usually used to generate mutants with higher enzyme stability. However, some reports revealed that the enzymatic activity and conversion can also be improved when the stability was changed. That means the goal is to improve enzymatic stability, which was designed by using Hotspot Wizard, but the unexpected results of enzymatic activity and conversion rate were achieved. Therefore, in this study reverse process was employed; that is, the research goal in this study is to improve conversion rate and enzymatic activity, but mutants were designed using Hotspot Wizard that targets stability.  
The model of Pal-2 was analyzed by Hotspot Wizard and hot spots were identified based on position and substitution scores from multiple sequence alignment. Eight amino acid sites were selected to be mutated based on the mutational landscape in Hotspot Wizard. Site-directed mutagenesis of these eight sites was complemented to produce mutants (N498P, Q275R, V355A, A492E, Y246L, H481P, R310P, and H287R). The plasmids of these eight mutants were assayed by sequencing and the correct plasmids were used for the further experiments.
## 5 Biomarkers of Response and/or Resistance to Immune Checkpoint Inhibitors
### Markers of Response and/or Resistance in Tumor Genome
#### 5.1.1 Tumor Mutational Burden
Microsatellite instability (MSI) is another condition associated with a high load of tumor-associated neoantigen peptides, which is in turn associated with high levels of intratumoral lymphocytes infiltration. For example, MSI-high (MSI-H) colon cancers, in which a high rate of non-synonymous single-nucleotide polymorphisms that lead to the expression of many neoantigens is present, show a higher intratumoral T cell content than MSI-low (MSI-L) colon cancers. The presence of infiltrating lymphocytes often predicts ICIs responsiveness because, although most of these cells exhibit markers of exhaustion, they can be reactivated by immune checkpoint antagonists [74]. In the HCC context, the utility of MSI evaluation as a genomic biomarker of ICIs response is limited by the low prevalence of MSI-H tumors (<1%) [85]. A recent study demonstrated that, among 755 patients with HCC undergoing ICIs, only 6 tumors (0.8%) were classifiable as TMB-high (TMB-H) and only one out of 542 tumors assessed for MSI (0.2%) was TMB-H and MSI-H [80]. In addition to the low prevalence of TMB-H and MSI-H HCCs, another limit in the applicability of these genomic biomarkers as reliable predictors of ICIs responsiveness is the lack of a standardized neoantigen prediction in human HCC [74]. Copy-number alterations (CNAs) influence tumor immune infiltration, and a high RNAs burden correlates with reduced immune cells infiltration, response to therapy, and survival in several types of cancers treated with ICIs [86]. In HCC, large-scale CNAs lead to the structural losses of genes involved in antigen presentation, suggesting that CNAs are partially responsible for immune desertification [34; 62; 63].
# Computational Approaches for Grocery Home Delivery Services
## 3 Problem Description and Formal Model
### Computational Steps during the Ordering Phase
#### 3.1.2 Determination Step  
When a new customer wants to place an order, the system has to determine the available delivery time windows that can be offered to this customer. This process has to be performed in milliseconds as customers are usually impatient when they have to wait for technical reasons. Note that for calculating the availabilities of time windows, the routing service has to calculate the travel times between all pairs of customers based on their provided addresses. The underlying mathematical problem of this step is the slot optimization problem (SOP) [23].  
Optionally, for reasons of profit-maximizing, some available time windows can be hidden from the customer or be offered at different rates. In this work, we do not consider any (dynamic) slotting and pricing because the policy of our partnering grocery chain is to offer available time slots on a \"first-come-first-served\" basis, and each customer is accepted if possible. Nevertheless, we refer to Section 2.3, which provides a brief literature review about this topic.
## 3 Results and Discussion
### Comparison between SCCO\({}_{2}\) Regeneration and SCW Regeneration
It also showed the typical peak at 270 nm and the shoulder at 275 nm, but its signal did not tend to zero as fast as the phenol spectrum at higher wavelengths. At wavelengths lower than 270 nm, it neither described the marked decrease of phenol spectrum and even a new peak appeared at about 250 nm. This dissimilarity meant that other species were present in the sample besides phenol. The ability of SCW to degrade and gasify phenol has been reported [39]. At high temperatures, 700 \({}^{\circ}\)C, and reaction times long enough, the supercritical fluid turns a fraction of this pollutant into gases. Some of the formed by-products are polycyclic aromatic hydrocarbons like naphthalene and phenanthrene, products whose UV-Vis spectra are similar to that for the sample collected during the last minutes of the SCW regeneration treatment. Consequently and despite using low temperatures, the data in Figure 10 suggested that a fraction of the retained phenol was turned into these products. This implied a minor third regeneration pathway in the mechanism of SCW regeneration, a degradation pathway, through which the remaining phenol was removed. Some authors who have studied the regeneration of carbon adsorbents exhausted with phenol using inert gases at high temperatures, 700-900 \({}^{\circ}\)C, state that due to the strength of the bond between the chemisorbed phenol and the adsorbent, the decomposition of that fraction is energetically less evident than its desorption [6; 31]. That is to say, chemisorbed phenol must be degraded to be removed when that method is used. The data reported herein also point that, although great amounts of phenol are removed without degradation thanks to the heating and extractive power of SCW, a fraction of  
Figure 10: Spectra for the original phenol solution and the three samples collected during a SCW regeneration assay. phenol, probably related to the chemisorbed phenol, must also be degraded to be removed. This makes a question about SCCO\({}_{2}\) regeneration mechanism arise. The obtained results probed that phenol was removed more efficiently at high temperatures. This trend could be favored by the degradation of a fraction of the chemisorbed phenol at high temperatures, which low-temperature SCCO\({}_{2}\) was not able to degrade. However, the effluent gaseous streams from SCCO\({}_{2}\) regeneration could not be analyzed and the possible participation of this third regeneration pathway could not be confirmed or denied for this method. Future work could provide a device to recover the extracted pollutants, which would allow studying the mechanism that governs SCCO\({}_{2}\) regeneration more in depth; in that way, higher temperatures may also be explored to try to highlight this pathway.
# Soft Fuzzy Reinforcement Neural Network Proportional-Derivative Controller
## 2 Preliminaries
### Fuzzy Neural Network PD (FNNPD) Controller  
A fuzzy logic system (FLS) [32] is a control system that utilizes expert knowledge and fuzzy logic. The fuzzy control system consists of fuzzification interface, inference engine (including database and rule base), and defuzzification interface, as shown in Figure 2. With fuzzy rules designed based on expert experience, an FLS can effectively identify and control systems [33]. However, the quality of control heavily relies on the expert-designed fuzzy rules.  
Figure 2: Structure and functionality of a fuzzy logic system (FLS), illustrating the process of fuzzification, rule evaluation, and defuzzification.  
A fuzzy neural network (FNN) [34] combines the learning capabilities of neural networks with the advantages of an FLS. Its structure is depicted in Figure 3, where the FNN outputs the coefficients for a PD controller, using the system error e and the derivative error de as inputs. With seven membership functions per input variable, two input variables generate a total of 49 (\(7\times 7\)) fuzzy rules.  
As shown in Figure 3, FNNs have basically five layers: the input layer, membership layer, rule layer, normalization layer, and output layer [35]. The membership layer transfers the input signal to fuzzy input sets according to fuzzy memberships. In the rule layer, each neuron represents a fuzzy rule, which transfers the fuzzy input sets to fuzzy output sets. The normalization layer conducts normalization operations. Finally, the output layer transfers the fuzzy output set into crisp output.  
The traditional PD controller [36] is a classical controller widely used in industry due to its simple structure and effective control performance. The formula for the PD controller is as follows:  
\[u=K_{p}\times e+K_{d}\times\dot{e} \tag{8}\]  
where \(K_{p}\) and \(K_{d}\) are PD parameters, \(e\) represents the system error, while \(\dot{e}\) represents the rate of error change.  
However, its effectiveness diminishes in nonlinear systems, and adjusting PD parameters can be challenging. Combining fuzzy neural networks with the PD controller forms the fuzzy neural network PD controller (FNNPD) [20], which exhibits significant advantages. By utilizing a fuzzy neural network, the PD parameters can be automatically adjusted based on the system's current state. It not only has learning capabilities and utilizes expert knowledge but also retains the robustness of the PD controller.  
Figure 3: Neural network architecture of the fuzzy neural network PD (FNNPD) controller.
# Effect of Transducer Burn-In on Subjective and Objective Parameters of Loudspeakers
## 5 Subjective Measurements
### Experimental Procedure  
To conduct a detailed analysis, the study was divided into three experiments in which the following device comparisons were made:  
* New (A) vs. used (B);
* New (A) vs. burned-in (B);
* Used (A) vs. burned-in (B).  
All tests were carried out in an acoustic chamber (absorbing walls and ceiling, reflective rectangular floor, volume of 815 m\({}^{3}\), ceiling height 6.5 m) at Wroclaw University of Science and Technology, compliant with the recommendations [13; 14]. The loudspeaker sets were positioned at a distance of four meters. The listening point was determined based on documents [15; 16] (listening area 2-4 m from measured devices, area radius \(\leq\) 0.7 m). The sound level at the reference point, measured with a Class I sound-level meter, was 85 dB. Figure 9 shows the listening spot and speaker system setup.  
The tests were based on comparative evaluation, where audio samples were played in four sequences: A-A, B-B, A-B, and B-A. The A-A and B-B sequences served as control sequences. The interval between individual samples within a measurement sequence was 5 s, and the interval between successive sequences was 15 s, during which listeners performed their evaluations [17]. A 20 min break was provided for listeners before each subsequent experiment.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Rating** & **Transparency** & **Dynamics** & **Bightness** & **Overall Assessment** \\ \hline +3 & Much more transparent & Much better & Much better & Much better \\ +2 & More transparent & Better & Better & Better \\ +1 & Slightly more transparent & Slightly better & Slightly better & Slightly better \\ 0 & The same & The same & The same & The same \\ \(-1\) & Slightly less transparent & Slightly worse & Slightly worse & Slightly worse \\ \(-2\) & Less transparent & Worse & Worse & Worse \\ \(-3\) & Much less transparent & Much worse & Much worse & Much worse \\ \hline \end{tabular}
\end{table}
Table 1: CCR rating scale.  
Figures 9-11 depict the listening area and the speaker systems under examination. Although all systems were of the same model, they were produced at different times. To maintain equal distances between the tested speakers, they were arranged from left to right in the following sequence: new, used, burned-in, new, used, burned-in.