esa-sceva/satcom-chunk-collection huggingface dataset:
# Novel ZIF-67@Bentonite (ZIF-67@BNT) Nanocomposite for Aqueous Methyl Orange Removal
## 3 Results and Discussion
### Analysis of Methyl Orange Adsorption Using ZIF-67@BNT
#### 3.3.2. Examine the Effect of Process Parameters Using ANOVA  
Additionally, the process variables could have a significant effect on the adsorption capacity [60] and Figure 9 illustrates the effect of the present process variables on MO adsorption capacity. To that end, it is noted that an increase in the MO concentration from 10 to 30 mg/L at an adsorbent dose of 5 mg increases the MO adsorption capacity. Such an enhancement in the MO uptake by ZIF-67@BNT is suggested to result from an enhanced driving force for the mass transfer of MO from the bulk aqueous phase to the bulk adsorbent surface. Figure 9 also shows that extended contact time delivers better MO uptake, as more process time permits more adsorbate molecules to diffuse to the adsorbent surface sites until reaching the adsorption equilibrium. Similarly, Figures 10 and 11 also indicate that the MO adsorption capacity (Q\({}_{\text{max}}\)) is affected by both the adsorbent ZIF-67@BNT dose and the methyl orange (MO) initial concentration; at higher MO concentrations (30 ppm) an increase in the ZIF-67@BNT adsorbent dose equilibrates more MO molecules on the adsorbent surface and significantly increases the Q\({}_{\text{max}}\)[61]. Hence, these findings can be used to establish parameters for the enhancement of MO uptake by ZIF-67@BNT. Furthermore, this study also provides a comparison with the other adsorption studies on the removal of MO from aqueous solution as shown in Table 4.  
Figure 10: The 3D interaction plots for MO adsorption capacity: (**a**) MO concentration vs. ZIF-67@BNT dose, (**b**) Time vs. ZIF-67@BNT dose, (**c**) ZIF-67@BNT dose vs. MO concentration.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**adsorbent** & \begin{tabular}{c} **Initial Con-** \\ **centration** \\ \end{tabular} & \begin{tabular}{c} **Adsorbent** \\ **Dose** \\ \end{tabular} & \begin{tabular}{c} **Initial** \\ **pH** \\ \end{tabular} & \begin{tabular}{c} **Temperature** \\ **Temperature** \\ \end{tabular} & \
\begin{tabular}{c} **Time** \\ **Time** \\ \end{tabular} &
\begin{tabular}{c} **Adsorption** \\ **Capacity** \\ \end{tabular} & **Reference** \\ \hline Ni@ZIF-67 & 40 & 20 & 6 & Room & 180 & 33.17 & [21] \\ Ag@ZIF-67 & 200 & 20 & 6 & 30 & 180 & 994.6 & [24] \\ ZIF-67@LDH & & 20 & 6 & 30 & 500 & 663.4 & [30] \\ CG/AC & 300 & 50 & 3 & 40 & 90 & 658 & [62] \\ AC/NiFe2O4 & 100 & 300 & 3 & 30 & 180 & 182.82 & [63] \\ CS-MT Composite & 40 & 320 & 6.3 & 30 & 60 & 154.4 & [17] \\ CTAB@GO & 300 & 50 & 4 & Room & 1440 & 1301.5 & [64] \\ ZnO/GO & 40 & 20 & 4 & 60 & 50 & 296.73 & [65] \\ Nanocomposite & 200 & 50 & 5 & 25 & 120 & 425.8 & [66] \\ Cd-ZIF-8 Composite & 200 & 50 & 5 & 25 & 120 & 425.8 & [66] \\ This Study & 30 & 5 & - & Room & 300 & 187 & - \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison with the other known adsorbent materials for the removal of MO.
# Cooperative Content Caching Framework Using Cuckoo Search Optimization in Vehicular Edge Networks
## 6 Cooperative Content Caching Framework
### Caching Techniques
#### 6.1.1 Cooperative Content Caching (COOP)  
In COOP, the MBS server calculates content popularity based on user preferences, then downloads popular data through a back-haul link and delivers PF content to RSUs on demand. Neighboring RSUs share data instead of downloading it from a BS server or controller. RSUs cache and serve location-based data such as GPS and weather forecasts. Cross-tier collaboration between MBS and RSUs eliminates the need to recalculate and download content because RSU caches it after fetching it from MBS, as demonstrated in Figure 3. COOP prevents data duplication and saves time and money. For every file content placement, Equation (8) is used since RSU will not download all packets of a single file due to limited caching capacity; caching resources would be wasted because requesting nodes are mobile.  
\[S^{f}=(S_{B}{}^{f}.\ S_{R}{}^{f}) \tag{8}\]  
To avoid redundancy and maximize cache capacity, we use the following caching limitations as expressed in Equations (9) and (10), respectively.  
\[S_{B}{}^{f}+\sum\
olimits_{R=1}^{n}s_{R}{}^{f}\ \leq\ S^{f} \tag{9}\]  
\[S_{R}{}^{f}\leq S_{RSU}\ and\ S_{B}{}^{f}\leq S_{MBS} \tag{10}\]  
Content placement using COOP is formulated as shown in Equation (11), where \(n\) denotes the number of possible placing content for file \(f\).  
\[T_{R}{}^{f}(n)=S_{B}{}^{f}(n)\cdot S_{B}{}^{f}(n) \tag{11}\]
# Modified Grey Wolf Optimizer and Application in Parameter Optimization of PI Controller
###### Abstract  
The Grey Wolf Optimizer (GWO) is a well-known metaheuristic algorithm that currently has an extremely wide range of applications. However, with the increasing demand for accuracy, its shortcomings of low exploratory and population diversity are increasingly exposed. A modified Grey Wolf Optimizer (M-GWO) is proposed to tackle these weaknesses of the GWO. The M-GWO introduces mutation operators and different location-update strategies, achieving a balance between exploration and development. The experiment validated the performance of the M-GWO using the CEC2017 benchmark function and compared the results with five other advanced metaheuristic algorithms: the Improved Grey Wolf Optimizer (IGWO), GWO, Whale Optimization Algorithm (WOA), Dung Beetle Optimizer (DBO), and Harris Hawks Optimization (HHO). The performance results indicate that the M-GWO has a better performance than competitor algorithms on all 29 functions in dimensions 30 and 50, except for function 26 in dimension 30 and function 28 in dimension 50. Compared with competitor algorithms, the proposed M-GWO is the most effective algorithm, with an overall effectiveness of 96.5%. In addition, in order to show the value of the M-GWO in the practical engineering field, the M-GWO is used to optimize the PI controller parameters of the current loop of the permanent magnet synchronous motor (PMSM) system. By designing a PI controller parameter optimization scheme based on the M-GWO, the fluctuation of the q-axis current and d-axis current of the motor is reduced. The designed scheme reduces the q-axis fluctuation to around \(-2\)-\(1\) A and the d-axis current fluctuation to around \(-2\)-\(2\) A. By comparing the current-tracking errors of the \(q\)-axis and \(d\)-axis under different algorithms, the validity of the optimized parameters of the M-GWO is proved.  
metaheuristic algorithms; swarm intelligence algorithms; grey wolf optimizer; PI controller 2025154530  
1 The College of Artificial Intelligence, China University of Petroleum (Beijing), Beijing 102249, China; [EMAIL_ADDRESS]  
202525 by the authors.  
[PERSON]; [PERSON] Modified Grey Wolf Optimizer and Application in Parameter Optimization of PI Controller. _Appl. Sci._**2025**, _15_, 4530. [[https://doi.org/10.3390/appl5084530](https://doi.org/10.3390/appl5084530)]([https://doi.org/10.3390/appl5084530](https://doi.org/10.3390/appl5084530))
## 2 Aerial Robot Design and Development
### Aerial Platform  
The aerial platform is a custom-made quadrotor (see Figure 2) that weighs 6.5 kg including batteries and avionics. It can carry up to a 3.5 kg payload which is enough for the diverter installation tool (1.7 kg) and one clip-type diverter (0.3 kg). There is a spare payload (1.5 kg) to add more sensors or carry heavier diverters if needed. The distance between its motors is 975 mm, and the height of the quadrotor is 500 mm. The hardware specification is as follows:  
* Propellers: four ultra-carbon fiber 21-inch radius propellers are used to generate enough thrust and load capacity in a plus \"+\" shape configuration.
* Motors: brushless direct-current (BLDC) motors, 6010, with \(\text{Kv}=130\) (rpm/V), have been used, where Kv is the motor velocity constant. They provide 5.1 kg maximum pulling force per rotor, with a maximum of 770 W power. It has a cooling air circulation system, convenient for long-term operation. The weight of each motor is 270 g. The suggested battery is 12 S 45 V. Thus, the no-load speed by 12 V batteries is 1560 rpm.
* Drivers: a 1240 S electronic speed controller is used as the driver of the BLDC motors. It is waterproof and has silica thermal pads and heat sinks for maximum heat transfer and dissipation. It works with a maximum of 52.2 V and up to 25 A continuous current and 40 A peak.
* _Main_ processor: an Intel NUC-i7, with 16 GB RAM, plays the role of the main processor of the UAV. It has USB 3.0, Ethernet, HDMI, Mini PCI Express ports, and low consumption that allows it to work with a 65 W power supply.
* - Autopilot: the UAV uses PIXHAWK Cube 2.1 autopilot. It has a 32-bit STM32F427 Cortex-M4F core with FPU, 168 MHz/252 MIPS, 256 KB RAM, 2 MB Flash (fully accessible), 32-bit STM32F103 failsafe co-processor, and 14 PWM/Servo outputs (8 with failsafe and manual override, 6 auxiliaries, high-power compatible).
## 4 Discussion
### Outlook for High-Spatial-Resolution Satellite GPP Mapping
Therefore, during the rainy season, the few cloud-free images made the Clr unable to track the monthly changes in vegetation physiology. The LUE-based GPP model corresponds well with short-term GPP variance, but Clr is more related to seasonal changes in the vegetation pigment pool and canopy structure [100]. Thus, the MOD17A2H-based GPP product and GPPEVi are better representatives of GPPEC than GPPCIr. The biodiversity in a forest is higher than that in GRA sites [101]. Although the canopy showed a similar total canopy chlorophyll content, the vegetation ecosystem exhibited a different response to the environment that produced different amounts of GPP (Figure 11b in the CUM series). Thus, the estimation of GPP with one series of parameters by GPP\({}_{\text{VI}}\) across multiple sites must consider the differences in plant traits in regions with high biodiversity. The CIr, as a characterization of canopy chlorophyll content, can be added as one part of the photosynthesis process to improve the accuracy of large-scale GPP assessment [37].
# Big Data Integration in Remote Sensing across a Distributed Metadata-Based Spatial Infrastructure
## 4 Spatial Organization and Management of Remote Sensing Data
### Distributed Data Retrieval  
After the construction of the distributed full-text index in Lucene, the index will be partitioned across several machines. Hence, data retrieval will be executed on several machines, and realized by the distributed search server SolrCloud. In the SolrCloud distributed clusters, all full-text indexes can make up a collection comprising one logical index. The collection is usually split into one or more shards, and evenly distributed on each node based on routing rules. In general, all shards in the same collection have the same configuration. Each shard usually has one or more replicas; one replica of each shard will be elected as a leader [43; 44]. In this study, the collection was split into three shards, and each shard had three replicas.  
In addition, there is an overseer node in the cluster that is responsible for maintaining cluster state information. It will monitor the status of each Leader node, acting as a master controller. When one shard's leader falls offline, the overseer node will initiate the automatic disaster recovery mechanism, and another node in the same shard will be designated as the leader to provide service. Even if the overseer node fails, a new overseer node will be automatically enabled on another node, ensuring high availability of the cluster. In the meantime, the index replica on the off-line node will be automatically rebuilt and put to use on other machines.  
The retrieval of distributed metadata in SolrCloud is implemented as follows: once any one of the SolrCloud nodes receives a data query request, the request will be forwarded to one of the replication nodes by the internal processing logic of the cluster. Then the replication node will launch the distributed query according to the created full-text index of remote sensing data. The distributed query will be converted into multiple sub-queries, each of which will be located on any of the replications of their corresponding shard. It is worth noting that the number of sub queries is equal to the number of shards. Finally, the results of each sub-query will be merged by the replication node that received the original query, and the merged final query results will be returned to the user. In addition, automatic load balancing is also provided by SolrCloud. If the query pressure is too large, the cluster scale can be expanded and replications increased to smooth the query pressure. The SolrCloud distributed query process is shown in Figure 7.  
Figure 7: The SolrCloud distributed query process. (1) The user’s data query request is sent to any one of the SolrCloud nodes, and then forwarded to one of the replication nodes; (2) The distributed query is launched and converted into multiple sub-queries, each of which is located on any of the replications; (3) Results are returned by each sub-query; and (4) sub-query results are merged and returned to users.
## 1 Introduction  
The importance of natural language understanding (NLU) technology based on deep learning is being emphasized. Rule-based NLU is the previous approach of NLU that relied on human-generated features. Therefore, it could not know about the unknown words and requires significant time to extract the features. Conversely, deep-learning-based NLU automatically extracts features and learns language representations [1]. Many previous studies have demonstrated improved performance in various NLU tasks using deep learning. However, as various deep-learning-based NLU models have been proposed, datasets and indicators that can objectively evaluate them are needed. Accordingly, the General Language Understanding Evaluation (GLUE) benchmark [2], which offers diverse genres of NLU datasets, was introduced. Based on experiments that applied several deep learning architectures (e.g., BiLSTM and ELMo [3]), the GLUE dataset became the most popular measure of multi-task language models.  
In recent years, multi-task learning (MTL) models--which, having been developed from single-task learning, learn a general representation from various tasks' datasets and prevent overfitting of a specific task--have received considerable attention. Representative MTL models include the multi-task deep neural network (MT-DNN) [4] and bidirectional associative memory (BAM) [5]. In MT-DNN, various tasks are simultaneously learned by passing through shared layers and each task-specific layer. In contrast, BAM applies a knowledge-distillation method that uses single-task models to teach a multi-task model. However, MTL models have some drawbacks in performing a central task. The model may become confused because all tasks are simultaneously trained with the same weights. Moreover, the model cannot learn the central task thoroughly during MTL.  
We compensate for these limitations by proposing a Sequential and Intensive Weighted Language Modeling (SIWLM) scheme, inspired by [PERSON] et al. [6]. SIWLM consists of Sequential Weighted Learning (SWL) and Intensive Weighted Learning (IWL). In SIWLM, central task and auxiliary tasks have an initial task weight and all tasks have individually adjusted weights during training. These adjusted weights are multiplied by the loss functions. In SWL, the focused task is changed sequentially every 20 iterations. Furthermore, the weights of all tasks except for the focused task are multiplied by 0.1. Thus, the model can intensively learn the focused task over a given 20 iterations. In IWL, during all iterations, the central task becomes the focused task. That is, the weights of auxiliary tasks are multiplied by 0.1. Consequently, the model can sufficiently learn the central task.  
We propose the optimal weight for each task belonging to the GLUE datasets. By applying that weight, the MTDNN-SIWLM achieves equal or higher performance on all GLUE datasets except CoLA and RTE than the baseline model, MT-DNN, and SMART [7]. Furthermore, we found that the optimal weight is affected more by the data size than the type of the task. Therefore, we propose a generalized range of weights based on the data size.
## 2 Equipment, Materials, and Methods  
### Equipment  
The equipment used in this test is a consolidation instrument with a temperature-controlling device installed, as shown in Figure 1. The equipment consists of two parts: a consolidation instrument and a temperature-controlling device.  
The consolidation instrument includes a consolidation container, pressure equipment, and deformation measurement equipment. The consolidation container consists of a cutting ring, retaining ring, permeable stone, and top cap. The load application method used was lever-type loading, and the loading ratio was 1:24. The pressure range was 0-400 kPa. The cutting ring used is 61.8 mm in diameter and 20 mm in height. The mechanical dial gauge used to read the strain of the sample has a maximum range of 10 mm and accuracy of 0.01 mm.  
The temperature-controlling device includes a temperature controller, temperature control room, heating belt, and temperature sensor. The consolidation container of the consolidation instrument was placed in a temperature control room, and a heating belt was  
Figure 1: Schematic of the consolidation instrument with temperature control.  
densely wrapped around external surface of the temperature control room. We formed a miniature water bath in the temperature control room by adding water into the room through the water inlet to heat the samples stably.  
As shown in Figure 2, the sample room of the bioreactor is made of organic glass and is 57.2 cm in diameter and 200 cm in height. The layered settlement of the sample could be read by the surveyor's rod on the outer wall of the organic glass sample room. A 10 cm thick gravel layer was arranged on the pressure-bearing bottom plate, which was chosen to be a perforated plate to drain leachate. The top of the sample was mechanically loaded with an accuracy of 0.1 kPa. The outer wall of the sample room was densely wrapped with the heating belt to control the boundary temperature during testing.  
The functions of the device are as follows:  
1. This device can provide a stable boundary temperature for the sample through the temperature control device.
2. The device can simulate the stress and settlement of MSW at different depths by applying different loads, and the load is stable and controllable.
3. This device can collect leachate and gas during the experiment through the leachate collection system and gas collection system. At the same time, water or leachate can be reinjected through the reinjection port.
4. This device can detect the pore gas pressure, pore water pressure, and temperature during the experiment due to installed observation instruments such as pore pressure sensors, thermometers, and a tensiometer.  
Figure 2: \(Cont.\)
## 4 Results and Discussion
### RQ4: What Performance Metrics Are Used in DL-Based Web Attacks Detection Literature?
Indeed, even if Deep Learning can extract abstract and complex features in the course of model training, it is important to identify whether the input to the DL model is a result of external feature extraction and selection or of a simple conversion from textual to numerical format and that feature selection and extraction is performed by the classification model during training. Yet, the studies that pay more attention to feature engineering are those using traditional ML models for classification and DL models for feature selection and extraction.
## 4 Evaluation
### Determination of the Weights for Each Disturbance  
We first investigated the performance of the predictor for the speech magnitude spectrum according to the weights for symmetrical and asymmetrical disturbances (\(w_{s}\) and \(w_{a}\) in (12)). This provided the necessary information for calculating the PD in future experiments. The number of the HE filter coefficients was set as 3, and the results appear in Figure 5. It was clear that the PESQs and STOIs had a decreasing trend as the weight of the asymmetrical disturbance increased. This was confirmed by the fact that the experimental correlation coefficients of the PESQs and STOIs with the asymmetrical weight values were \(-0.9286\) and \(-0.9467\), respectively. An asymmetrical disturbance was imposed on the PD so that negative differences would be perceived differently from positive ones due to masking effects [34]. The experimental results, however, showed that no remarkable benefit was gained by adopting an asymmetrical disturbance. This was somewhat different from previous studies that employed the PD metric [34, 37].  
The LSD revealed different trends by increasing the weights of the asymmetrical disturbance. A weak correlation (=0.5789) with asymmetrical disturbance was observed. The minimum for the LSD was obtained in cases where the weight of the asymmetrical disturbance was set as 0.2. The experimental results commonly indicate that a relatively small value of the asymmetrical disturbance would be helpful to improve the quality of the reproduced speech signals (higher PESQ, higher STOI and lower LSD). As a consequence, only symmetrical disturbance was used (e.g., \(w_{s}=1,\ w_{a}=0\) in (12)) in the subsequent experiments.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Property** & **Value** \\ \hline Average number of utterances (per subject) & 1062 \\ Average duration of utterances (s) & 6.44 \\ Standard deviation of duration (s) & 1.33 \\ Maximum duration of utterance (s) & 12.26 \\ Minimum duration of utterance (s) & 3.23 \\ Average number of phonemes (per subject) & 69,982 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Dataset properties.
# MoSe\({}_{2}\) with Ultra-Fine Pt Decoration for Efficient Photodegradation
## 2 Materials and Methods
### Photodegradation Measurements  
The photocatalytic properties of the synthesized MoSe\({}_{2}\)-Pt catalysts were studied by degrading Methylene blue (MB). During each photocatalytic reaction, 50 mg of the catalyst was put in 50 mL of MB solution (10 mg/L). Reactions were carried out under a 300 W Xe lamp. We used a cutoff filter (#84-689, TECHSPEC, Co., Ltd., Gauteng, South Africa) to block the light under the wavelength of 400 nm. Before degradation tests, MB solutions with catalysts inside were stirred under a dark environment for half an hour to dispose of the adsorption/desorption effects. During photocatalytic degradations, 2 mL of the solution was taken every 30 min and was centrifuged to remove the MoSe\({}_{2}\)-Pt powder. Visible-light absorption spectra ranging from 400 nm to 800 nm were measured through a Lambda 950 UV-Vis spectrophotometer. During measurements, the absorption peak of MB was set at 664 nm and the concentration of residual MB could be determined by the integration of the MB absorption peaks. The photocatalytic reactions were therefore presented by time-course decrease in MB concentration.