esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Feature-Selection Method and Application  
To achieve the best performance of the algorithm, the choice of features can be regarded as an extremely important method. Including in feature selection are feature extraction and feature construction. Among them, [PERSON] and [PERSON] [9] classified the feature subsets into four categories: (a)  
Figure 2: Feature selection method and application.  
completely irrelevant and noisy features, (b) weakly relevant and redundant features, (c) weakly relevant and nonredundant features and (d) strongly relevant features. An optimal subset mostly contains all the features in the category (c) and (d) as shown in Figure 3. In addition, feature selection involves two main objectives, which are to maximize the classification accuracy and minimize the number of features [19]. Strongly relevant features are indispensable for the enhancement of discriminative power and prediction accuracy. Sometimes, weakly relevant features can be useful for improving prediction accuracy if a feature is nonredundant and compatible with evaluation measures [20]. Moreover, the curse of dimensionality of data poses a severe challenge to many existing feature-selection methods with respect to efficiency and effectiveness [21; 22]. The results of this research also compared various feature selection methods to highlight the efficiency and effectiveness of FCBF [23; 24]. In summary, the main purpose of FCBF-PSO proposed in this study is to quickly screen out important features and improve accuracy.
# Combination of LiCs and EDLCs with Batteries:
## 1 Introduction
Although it is expected that with increased adoption, demand and volume of production, the cost of production will also reduce, because of the lithium doping of LiC cells, the price will always be higher than SC cells. In reference [29], the authors combined a bank of 108 Li-ion capacitor series cells obtained from JM Energy with a lithium (NMC) battery for applications in EVs using the same topology proposed by [16]. In this proposal, the authors have sacrificed the high power density that could have been obtained if an SC were used. It is important to note that the limitations of the topology proposed by [16] as discussed above still apply here as well. The authors in [29] claimed to have developed a bench mark for evaluating control algorithms for EVs in their research; however, this claim needs to be verified. There are some interesting research works that have been carried out with the combination of the battery and SCs or the battery and HC with renewable energy sources such as photovoltaic (PV) systems, fuel cells, and wind turbines. Some of these studies can be found in [30; 31; 32; 33; 34; 35; 36]. The dearth of research involving the use of LiCs cannot be overemphasized, particularly in energy storage hybridisation. There is a need to look into cost-effective inclusion or adoption topologies, which would give room to fully exploit the advantages presented by LiCs. The research presented in this paper seeks to address these shortcomings of both battery-SC and battery-HC energy storage systems by proposing a new hybrid energy storage system that combines both SCs and HCs with a battery through a multiple input DC-DC converter. A descriptive block diagram of the proposed hybrid energy storage system through a multiple input converter is presented in Figure 1.
## Appendix A
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Hour** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** \\ \hline Residential, & & & & & & & & \\ commercial, and & 1640.43 & 1757.603 & 1991.95 & 2226.297 & 2343.471 & 2577.818 & 2694.992 & 2812.165 \\ public & & & & & & & \\ \hline Industrial & 772.0152 & 827.1591 & 937.447 & 1047.735 & 1102.879 & 1213.167 & 1268.311 & 1323.455 \\ \hline Transportation & 1043.164 & 1117.676 & 1266.699 & 1415.723 & 1490.234 & 1639.258 & 1713.769 & 1788.281 \\ \hline Agriculture & 137.0101 & 146.7966 & 166.3694 & 185.9423 & 195.7288 & 215.3016 & 225.0881 & 234.8745 \\ \hline Other & 10.25525 & 10.9877 & 12.4528 & 13.91784 & 14.65036 & 16.11539 & 16.84791 & 17.58043 \\ \hline Non-energy & 349.9091 & 374.9026 & 424.8896 & 474.8766 & 499.8702 & 549.8572 & 574.8507 & 599.8442 \\ \hline
**Hour** & **9** & **10** & **11** & **12** & **13** & **14** & **15** & **16** \\ \hline Residential, & & & & & & & \\ commercial, and & 3046.512 & 3398.033 & 3632.38 & 3878.444 & 3515.206 & 3280.859 & 2812.165 & 2460.645 \\ \hline Industrial & 1433.742 & 1599.174 & 1709.462 & 1825.264 & 1654.318 & 1544.03 & 1323.455 & 1158.023 \\ \hline Transportation & 1937.305 & 2160.84 & 2309.863 & 2466.338 & 2235.351 & 2086.328 & 1788.281 & 1564.746 \\ \hline Agriculture & 254.4474 & 283.8067 & 303.3796 & 323.9311 & 293.5931 & 274.0203 & 234.8745 & 205.5152 \\ \hline Other & 19.04546 & 21.24302 & 22.70805 & 24.24634 & 21.97553 & 20.5105 & 17.58043 & 15.38287 \\ \hline Non-energy & 649.8312 & 724.8117 & 774.7987 & 827.2851 & 749.8052 & 699.8182 & 599.8442 & 524.8637 \\ \hline
**Hour** & **17** & **18** & **19** & **20** & **21** & **22** & **23** & **24** \\ \hline Residential, & & & & & & & \\ commercial, and & 2343.471 & 2812.165 & 3280.859 & 3046.512 & 2577.818 & 2109.124 & 1991.95 & 1874.777 \\ public & & & & & & & \\ \hline Industrial & 1102.879 & 1323.455 & 1544.03 & 1433.742 & 1213.167 & 992.5909 & 937.447 & 882.3031 \\ \hline Transportation & 1490.234 & 1788.281 & 2086.328 & 1937.305 & 1639.258 & 1341.211 & 1266.699 & 1192.187 \\ \hline Agriculture & 195.7288 & 234.8745 & 274.0203 & 254.4474 & 215.3016 & 176.1559 & 166.3694 & 156.583 \\ \hline Other & 14.65036 & 17.58043 & 20.5105 & 19.04546 & 16.11539 & 13.18532 & 12.4528 & 11.72028 \\ \hline Non-energy & 499.8702 & 599.8442 & 699.8182 & 649.8312 & 549.8572 & 449.8831 & 424.8896 & 399.8961 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Final energy consumption (_BOE_).
## 4 Performance Analysis and Design of the TPMA
It has a number of attractive characteristics, including a sinusoidal back-electromotive-force waveform, a high force capability, and a low cogging force [35; 36]. Because it is relatively difficult to manufacture magnets with an ideal Halbach magnetization, a simpler form, referred to as a quasi-Halbach magnetization, is preferred [35]. When the mover is in the initial center position, the net cogging force is zero because the permanent magnet is aligned with the stator teeth. However, when the mover has moved to the axial position in which the magnet flux is not in alignment with stator teeth, the net cogging force is no longer zero [37]. Figure 12 shows the variation of the maximum cogging force with respect to the quasi-Halbach ratio and slot opening. According to these two axial design variables, the magnetic flux density at the teeth changes, and the maximum cogging force value changes accordingly. As shown in Figure 12a, when the ratio shifted from 0.5 to 0.6 at 8 mm slot opening, the maximum cogging force decreased from 1575 to 223 N. And when the slot opening decreased from 16 to 6 mm at 0.6 ratio, the maximum cogging force reduced from 952 to 162 N. Therefore, there are optimal values of axial variables that minimize the cogging force. Figure 12a-c shows that although the coil height increases from 10 to 20 mm, there is almost no change in the optimum value of axial variables. However, the optimum value changes depending on the height of the magnet which directly affects the magnetic flux density, as shown in Figure 12d-f.  
Gradient-based optimization was used to maximize the thrust density and minimize the cogging force under the geometric and thermal constraints and systematically modify the design variables in the model. The established problem is of a multi-objective optimization and the weighted-sum approach technique is applied to solve it [38]. The objective function was the sum of the reciprocal average thrust density and the maximum cogging force to find a unique objective function. Through optimization, the thrust density (force per unit volume) of the initial model increased by 35% from 2.252 \(\times\) 10\({}^{5}\) to 3.120 \(\times\) 10\({}^{5}\) N/m\({}^{3}\) under the thermal constraints, and the maximum cogging force decreased by 89% from 2216 to 220 N, which is a reasonable value that is less than 3% of the target thrust force. Table 5 lists the optimized design variables.
# Decoding Urban Intelligence: Clustering and Feature Importance in Smart Cities
## Experiments
### Gradient Boosting
#### Model parameterization  
With regard to model parameterization, different considerations have been applied:  
* The random forest model deploys 100 trees, which is a standard choice for balancing computational cost and model accuracy. Increasing the number of trees generally improves performance but also increases computation time.
* The SVM model employs a linear kernel. This choice is appropriate as it makes it easier to interpret feature weights, as the model essentially performs a linear regression.
* The gradient boosting model uses default parameters, although the model's performance can often be improved by tuning parameters such as the number of boosting stages, the learning rate, and the maximum depth of the individual trees.
## 2 Materials and Methods
### Soil Properties  
Two soil classifications were selected for the study: SP-SM [32] and SW-SM [33]. Table 1 presents the properties of both soils used in the numerical model.  
The friction angle and cohesion values assigned to each soil were defined based on their USCS classification, fines content, and the typical behavior of granular soils. Various sources indicate that for well-graded and poorly graded sands, the friction angle generally ranges between 30\({}^{\circ}\) and 40\({}^{\circ}\), and cohesion may vary between 0 and 20 kN/m\({}^{2}\) depending on the degree of compaction and fines content [48]. In this case, representative values within these ranges were assigned: 38\({}^{\circ}\) and 5 kN/m\({}^{2}\) for the SP-SM soil, and 36\({}^{\circ}\) and 10 kN/m\({}^{2}\) for the SW-SM soil.  
Figure 1: Research activities workflow.  
The selection of these two soil types aims to analyze how different granulometric and hydraulic properties influence slope stability under intense rainfall. Poorly graded sand (SP-SM), with 93.5% sand and 6.5% fines, represents a highly permeable soil with low cohesion (5 kN/m\({}^{2}\)), making it susceptible to rapid infiltration. In contrast, well-graded sand (SW-SM), with 87.8% sand and 11.9% fines, has higher cohesion (10 kN/m\({}^{2}\)) and lower permeability, allowing for greater water retention. This analysis was conducted in Plaxis 2D.
# Toward the Generation of an Isolated TW-Attosecond X-ray Pulse in XFEL
## 2 Methods without Current Modulation  
### Good Emittance Region Method  
A low-emittance electron beam amplifies the radiation pulse faster than a high-emittance beam. If there are two regions within an electron beam, one with high emittance and the other with low emittance, the radiation will grow in the low emittance region faster than in the high emittance region. This means that we can generate a FEL pulse with a duration shorter than the electron pulse. This process was utilized to devise the idea of generating a TW-as XFEL pulse without a laser for modulating the electron beam energy [38]. By using a slotted foil in a bunch compressor, the emittance of the electron beam can be deteriorated locally. If there are many slots in the foil with an irregular spacing, the emittance of the electron after passing through the foil is modulated as shown in Figure 1a. Only the electrons in the good emittance region (GER) will contribute to the radiation process. In the first undulator unit, we can obtain several radiation pulses spaced exactly the same as in the GER. By using an electron delay unit, we can delay the electron beam to match the last radiation pulse--called the \"target radiation pulse\" (TRP)--to the second-last GER. Notably, the exact meaning of TRP is different from proposal to proposal, which will be described in the following sections. In this delay process, the other radiation pulses cannot access a GER because the spacing is not regular. Repeating this process, we can amplify the TRP to the end. The other radiation pulses do not have chances for further amplification. From this method, we can obtain \(\sim\)1 TW power for both 0.1 nm and 0.5 nm X-ray cases, as shown in Figure 1b for Swiss-FEL parameters, in which the beam energy was 5.8 GeV. The pulse length was about \(222\pm 28\) as for the 0.5 nm X-ray and \(142\pm 37\) for the 0.1 nm X-ray.  
The target X-ray pulse is not inherently synchronized with any other external laser, which may be a pump or probe laser in a typical pump-probe experiment, because no laser is used in the emittance modulation process. Hence, this scheme is not suitable for typical pump-and-probe experiments. However, this scheme is suitable for experiments which need only short and high-power X-ray pulses.
# A Fuzzy Inference System for Unsupervised Deblurring of Motion Blur in Electron Beam Calibration
## 4 Conclusions and Discussion  
The algorithm showed superior performance when a rough prior knowledge of \(L_{h}\) was provided for the fuzzy inference system. The \(\Delta E_{i}=(|E_{1}-E_{2}|)\) was greater than 0.5 thereby clearly identifying and segregating the correct deconvolution process. The algorithm was also tested without including the PSF knowledge, in which case \(\Delta E_{i}\) was in the interval of 0.1 to 0.5, which was enough to confidently separate the correct deconvolution process.  
Figure 6 depicted a special case where \(H\) had a null frequency at \(\omega_{h}\) = 120 kHz with a normalized magnitude of 0.09 dB, whereas, \(F\) null was at \(\omega_{f}\) = 170 kHz with a magnitude of 0.02 dB and had four times higher attenuation. Although \(\omega_{f}\) had a magnitude that was in its favor, the PSF deviation of 0.51 was not, yet the PSF deviation outweighed its low magnitude and the correct restoration was successfully distinguished with 14% separation in the deconvolution grades (\(|E_{1}-E_{2}|\) = 0.14). This high attenuation of \(\omega_{f}\) was most likely due to it being closer to the second harmonic of \(\omega_{h}\) and, therefore, it experienced further attenuation. Nevertheless, owing to the FIS implementation, the correct restoration process was identified. All the possible rules were considered for the implementation of this FIS and its tuning was performed heuristically by an expert. However, clustering algorithms could be used for FIS with multiple inputs and membership functions to determine the optimum number of rules. Furthermore, adaptive FISs can be used to automate the tuning and learning process of the FIS in a more complicated and complex scenario.  
The author would like to thank [PERSON], [PERSON] for their help and cooperation, [PERSON] for reviewing the article and [PERSON] for his laboratory assistance. Many thanks to NSIRC, TWI Ltd. and Brunel University for providing the measurement facilities and research funds.  
The author declares no conflicts of interest.  
Figure 6: Null frequencies in the spectrum of the degraded pulse. Result of restoration with detected null frequencies, expected PSF length of 0.4 mm.
# Blasting Vibration Control and Signal Analysis of Adjacent Existing Deterioration Tunnels
## 2 General Situation of Deterioration Tunnel  
The Sanya to Ledong section of the Hainan West Ring Railway is about 103 km. Among them, the Bo Jiling Tunnel is located in Sanya City, with a total length of 385 m (mileage range DK346 + 890-DK347 + 275). It is a single-track tunnel with a maximum buried depth of 48 m, and the designed running speed is 160 km/h.  
The interval between the new tunnel and the existing tunnel is 21.1-28.6 m. The relative position relationship between the new tunnel and the existing tunnel is shown in Figure 1, and the position relationship between the structures around the new Bo Jiling tunnel is shown in Table 1.  
There are many structures around the new tunnel, and the existing tunnel is deteriorated, as shown in Figure 2. The surrounding rock of the tunnel belongs to the \"V\" level, and the rock mass is incomplete, so it is necessary to control the blasting vibration strictly. At present, the commonly used blasting vibration control methods in engineering are controlling the maximum and single-hole charge, electronic detonator differential blasting, sectional blasting, laying vibration reduction ditch, and so on. Cutting hole blasting is inevitable under the existing vibration control methods. The free surface is not sufficient for the cutting blasting, and the interlocking of rock is large. The vibration generated by the cutting hole blasting is the largest [19]. Aiming to reduce the disturbance of cutting hole blasting, mechanical cutting is proposed. Mechanical cutting creates an open space. Subsequently, the number of free surfaces of blasting increases. It is clearly pointed out in the literature [20] that the increase of free surface can effectively reduce the blasting vibration. At the same time, the minimum resistance line length of the hole becomes smaller. The interlocking of the rock becomes smaller. Mechanical cutting instead of cutting blasting will reduce disturbance to a great extent. Thus, the safety of the existing degraded tunnel is guaranteed. At the same time, it is safer and will not cause flying stones.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Mileage** & **Relative Altitude (m)** & **Relative Relation** & **Structure** \\ \hline DK346 + 880\(-\)DK347 + 285 & 32.987\(-\)36.430 & parallel & existing tunnel \\ DK346 + 880\(-\)DK347 + 285 & 32.987\(-\)36.430 & parallel & kerosene pipe \\ DK347 + 140 & 35.197 & above & power tower \\ \hline \hline \end{tabular}
\end{table}
Table 1: Structures around the tunnel.  
Figure 1: Relative positions of the old and new tunnels.  
Figure 2: Tunnel deterioration phenomenon.
# In-Depth Analysis of GAF-Net: Comparative Fusion Approaches in Video-Based Person Re-Identification
## 3 Proposed Method
### Appearance Feature Extraction Module
#### 3.2.1 OSNet Framework  
The Omni-Scale Network (OSNet), introduced by [PERSON] et al. in [16], revolutionized person Re-ID. It excels at processing features at various spatial scales to adeptly handle Re-ID challenges like pose variations and occlusions.  
Depthwise separable convolutions are central to OSNet, which efficiently reduces the number of parameters and computational cost. These convolutions are divided into depthwise and pointwise operations, known as Lite \(3\times 3\) convolutions in OSNet. This approach enhances the network's ability to learn features at multiple scales, which is essential for discerning individual identities.  
Another core element of OSNet is its residual bottlenecks, each of which incorporates a Lite \(3\times 3\) layer. These bottlenecks are characterized by an exponent (s) that denotes the scale of feature learning. By stacking multiple such layers (\(s\geq 1\)), the network expands its receptive field, thereby capturing a more extensive range of spatial scales. This design enables a comprehensive analysis of features at varying scales. Furthermore, one of the most innovative aspects of OSNet is the unified aggregation gate (\(AG\)), which dynamically mixes features from multiple scales based on the input data. This gate assigns weights to feature maps at various scales to adapt the feature learning to each specific input image.  
This dynamic fusion makes OSNet very efficient for person Re-ID tasks.  
Then, each frame of the sequence feeds OSNet to obtain feature vectors that are then averaged to derive a unified feature representation for the entire sequence so that the final feature vector combines features from all frames, which is crucial for accurate video-based person Re-ID. This final feature vector (\(\mathbf{z}_{\text{OSNet}}^{i}\)) for an individual identified by ID \(i\) is expressed as follows:  
\[\mathbf{z}_{\text{OSNet}}^{i}=\phi^{\text{OSNet}}(\mathbf{A}^{i}), \tag{4}\]where \(\mathbf{A}^{i}\) is the input tensor containing appearance data from different camera views, and \(\phi^{\text{OSNet}}\) is a function representing the transformation of these raw data into a refined feature representation.
## 6 Case Studies of QSAR Modeling of Anti-Breast Cancer Agents
Further assays on **C-19** revealed its potential and mechanisms as a multitarget anticancer agent [267]. Another study focused on a 2D-QSAR-guided strategy for designing arylsulfonyl-hydrazzone derivatives targeting human ER-positive breast adenocarcinoma and triple-negative breast (TNBC) adenocarcinoma [274]. The authors developed 2D-QSAR models with strong predictive performance for a series of 26 arylsulfonylhydrazones. A QSAR-based design was conducted, leading to the creation of nine novel derivatives, which were subsequently synthesized and tested. Their predicted anticancer activity, along with the anticipated impact of the indole ring on cytotoxic properties, was confirmed. Notably, seven and eight compounds demonstrated higher activity against MCF-7 and MDA-MB-231 cell lines, respectively, than originally anticipated. This QSAR-driven approach successfully identified several promising leads for anticancer drug development. A study by [PERSON] and colleagues serves as an excellent example of rational drug design using a small in-house dataset and 2D-QSAR modeling [273; 283]. Their four-step QSAR-assisted rational design of novel 2-[(4-amino-6-R2-1,3,5-triazin-2-yl)methylthio]-N-(1-R1-imidazol-2-ylidene)-4-chloro-5-methylbenzenesulfonamide derivatives resulted in a 10-fold enhancement in activity against HCT-116 and MCF-7 cell lines. The activity improved from 45 \(\upmu\)M and 46 \(\upmu\)M, respectively, for compound **31** (included in the first step of QSAR modeling) to 3.6 \(\upmu\)M and 4.5 \(\upmu\)M, respectively, for compound **150** (included in the fourth step of QSAR modeling). The authors further investigated the potential mechanisms of cytotoxicity for the compounds with high selectivity indices by conducting various assays. This comprehensive study indicated that the compound scaffolds hold promising potential for further lead optimization and development.
# E-MQTT: End-to-End Synchronous and Asynchronous Communication Mechanisms in MQTT Protocol
## 3 E-Mqtt  
Our design approach for E-MQTT prioritizes the preservation of the original MQTT packet structure and protocols to the maximum extent feasible. Guided by this design philosophy, the proposed E-MQTT seamlessly incorporates end-to-end communication functionality into the existing MQTT, integrating with the existing QoS 0, 1, and 2. To distinguish E-MQTT from the conventional MQTT, we introduce a new QoS level 3. In asynchronous E-MQTT, the publisher is not suspended after sending the PUBLISH packet and immediately proceeds to the subsequent task. In this mode, the publisher can asynchronously receive a response message from the broker, which forwards the initial response message originating from subscribers. In contrast, within the synchronous E-MQTT configuration, on transmitting the PUBLISH packet, the publisher enters a suspended state. This suspension persists until the publisher receives a consolidated response message orchestrated by the broker. This aggregate message encapsulates the actual responses generated by individual subscribers.