esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Configuration of the Proposed Wireless Battery Charging System for a Quadcopter
### Control Part
#### 2.3.2 Hill Climbing Algorithm  
Hill climbing is an optimization technique that is used to find an optimum solution to a computational problem. It starts off with a solution that is normally very poor compared to the optimal solution and then iteratively improves from there. It does this by generating other solutions which are better than the current solution. It repeats the process until it finds the optimal solution where it can no longer find any improvements.  
Generally, drones tend to land at any place upon the drone's wireless battery charging station. That means the centroid of the receiving coil on the drone may not be aligned with any of the transmitting coils. In this case, transmitting coils are moved using the \(XY\) table in an arbitrary direction to get closer to the receiving coil. By measuring terminal voltages of each transmitting coil, the controller can detect whether the receiving coil gets closer to one of the transmitting coils. This kind of arbitrary movement of the \(XY\) table continues until it detects the decrease in terminal voltages of the transmitting coils, as shown in Figure 7. When one transmitting coil, which corresponds to transmitting coil 2 in Figure 7, is chosen, the hill climbing algorithm is activated to move the transmitting coil to the proper position where the voltage measured from that transmitting coil reaches its minimum.  
Figure 8 shows the overall flowchart of the proposed method. Figure 9 shows the flowchart of the hill climbing algorithm used in this system. Previously, authors tried to solve a different kinds of control problems using the hill climbing algorithm [42; 43]. In this work, the hill climbing algorithm starts by sending the required PWM value to the stepper motor driver to move the \(XY\) table. At the start, when there is no change in the voltage, i.e., the drone is not on the charging station, there is no movement and the voltage of the transmitting coil is constant. When the drone lands on the charging station, there is a change in the voltage value, and the voltage decreases in the presence of a drone with a receiving coil. The hill climbing algorithm is activated and one transmitting coil with the maximum change in the voltage value is selected. The current terminal voltage \(V_{n}\) of the selected coil is measured and compared with the previous measured voltage \(V_{n-1}\). The minimum value is stored as \(V_{min}\). After that, the controller sends the required PWM to move the \(XY\) table to the position of the minimum value. This kind of process keeps repeating until it reaches the minimum voltage. In Figure 9, \(n=1\) to 4 is the number of transmitting coils, and \(k\) is the number of the sample.  
Figure 7: (**a**) Receiving coil’s initial position. (**b**) Receiving coil gets close to a transmitting coil after arbitrary movement.  
Figure 8: Overall flowchart of the proposed method.  
Figure 9: Flowchart of the hill climbing algorithm.
## 5 Simulations  
The most important result of the simulations is the plot of the force vs. time comparing the reference force with the actual force imparted by the perturbator to the stricken mass in several combinations of pneumatic actuators (\(C_{a}\) and \(C_{b}\)) and proportional valves (\(V_{a}\) and \(V_{b}\)). Moreover, some conditions, like the effect of a different initial distance between the end striker and the stricken mass on tracking accuracy, have been investigated. Some significant output such as the tracking error (difference between actual and desired force during phase III), the actual force impulse, i.e., the integral over time, and its difference with respect to the desired impulse (accuracy error), were calculated in each simulation. The parameters required to run the model (Table 2) were set according to datasheets (for valves and actuators) and data obtained from characterization of the test-bench (e.g., for the dampers). Unknown parameters, such as the friction force on the piston rod, were estimated by iterative tuning of the model, in order to match the behavior observed during experimentation.  
Simulations have been performed with several values of the proportional and integral gains of the PI controller, \(k_{p}\) and \(k_{i}\). In general, by raising \(k_{p}\), an increase in accuracy error and a decrease in tracking error were observed. Moreover, the impulse value was significantly affected by variation of \(k_{p}\). In particular, \(k_{p}\) values between 0 and 20 provided the most significant changes in the impulse. By means of an iterative procedure, it was possible to individuate the value of \(k_{p}\) = 5 as a good trade off.  
In Figure 12a, a comparison between the performance provided by cylinders \(C_{a}\) and \(C_{b}\) is shown. The simulation was conducted at fixed constrains (both dampers locked). The behavior with cylinder \(C_{b}\) seemed to be more reactive, probably because of the lower friction given by metal seals rather than NBR seals of \(C_{a}\). These results were obtained considering the characteristics and response time of the \(V_{b}\) type valves.  
Another point of interest was the effect of the initial distance between the load cell and the stricken mass. Piston speed at the impact can affect the accuracy of the force tracking during phase III. In a clinical environment, it would be impractical to ask the operator to maintain a fixed value of initial distance between perturbator and subject. Figure 12b shows the results for an initial distance \(dx\) variable from 0.02 to 0.1 m, using cylinder \(\mathrm{C_{a}}\). As shown by the plots, the performance of the force tracking is largely unaffected by the different initial positioning of the perturbator with respect to the subject, proving the reliability of our control solution.
## 3 Methodology
### Spatio-Temporal Visual Similarity Modelling
#### 3.2.3 Boundary Prediction  
Based on Equation (1), the lower the value of \(S_{ij}\), the more dissimilar frames \(x_{i}\) and \(x_{j}\) are. Thus, we calculate the shot boundary by thresholding \(S_{ij}\). In our experiments, 0.4 was used as the threshold value. The entire shot boundary detection algorithm is shown in Algorithm 1.  
```
Input: List of frame-level objects and places tags Output: Shot boundaries
1 shots = [] for\(i\) =1:mdo
2 for\(j\) = 1:mdo
3\(place\_sim(x_{i},x_{j})=||\psi(x_{i})||\cdot||\psi(x_{j})||\) // \(\psi(x_{i})=\) normalised vector of place tags for frame \(x_{i}\) \(obj\_sim(x_{i},x_{j})=||\psi(x_{i})||\cdot||\psi(x_{j})||\) // \(\psi(x_{i})=\) normalised vector of object tags for frame \(x_{i}\) \(sim(x_{i},x_{j})=\frac{w_{1}(plac\_sim)+w_{2}(obj\_sim)}{w_{1}+w_{2}}\) \(d_{1}^{2}(\psi(x_{i}),\psi(x_{j}))=1-sim(x_{i},x_{j})\) \(S_{ij}=exp\left(-\frac{d_{1}^{2}(\psi(x_{i}),\psi(x_{j}))+\alpha\cdot d_{2}^{ 2}(x_{i},x_{j})}{2\sigma^{2}}\right)\)
4 for\(i\) = 1:mdo
5 if\(S_{i,i+1}<\) thresholdthen
6\(shots\_append(i)\)
```  
**Algorithm 1**Shot boundary detection
# Performance Evaluation of a Mesh-Topology LoRa Network
## 6 The ns-3 Simulation Model
### Join Procedure  
The joining procedure is beyond the scope of this current study. The following settings are assigned to nodes during the join procedure:  
* Transmission parameter; default SF, BW, and transmission power values are assigned to all nodes at the start.
* Routing tables; the current study assumes a global view of the gateway and the nodes. Using the default setup information (SF, BW, Node locations, and transmission power) available, the simulation model assigns the nodes parent/child nodes where applicable.
* Channel assignment; based on the LoRaWAN Regional parameter specifications, we only consider the three network channels that are required to be implemented in all EU863-870 nodes, namely, 868.10 MHz, 868.30 MHz, and 868.50 MHz. During the joining procedure, a network channel is assigned to each parent-node. All child-nodes are required to use this network channel, as this will minimise packet collisions.
## 5 Influence of Shear Lag Effect and Accortion Effect on the Dynamic Characteristics of a CBGCSW  
### Shear Lag Effect  
In order to investigate the impact of the shear lag effect, we further carried out degenerations of the vibration equations of a CBGCSW. Figure 5 describes the impact of shear lag effect on the natural vibration frequencies of a simply supported and fixed-support CBGCSW. \(e_{c}=\big{|}f_{s}-f_{ns}\big{|}/f_{s}\) is the impact of shear lag effect; \(f_{s}\) is the natural vibration frequencies of CBGGSW considering the shear lag effect; and \(f_{ns}\) is the natural vibration frequencies of CBGCSW without considering the shear lag effect.  
As can be seen from Figure 5, the shear lag effects of six CBGCSW models all increased significantly with the increasing of the mode order. The largest shear lag effect of the six CBGCSW models was \(15.11\%\). Further, there was a weakening impact of the span-width ratio on the shear lag effect. Under the same mode order and boundary condition, the shear lag effect of a CBGCSW with a smaller span-width ratio was larger. This is an indication that the shear lag effect cannot be ignored in obtaining the dynamic characteristics of CBGCSWs, especially when the span-width ratio is small.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Span–Width Ratio**} & \multirow{2}{*}{**Calculation Methods**} & \multicolumn{6}{c}{**Natural Vibration Frequencies (Hz)**} \\ \cline{3-6}  & & **1 st** & **2 nd** & **3 rd** & **4 th** & **5 th** \\ \hline \multirow{4}{*}{6.72} & EBT & 48.06 & 115.71 & 199.10 & 290.62 & 387.22 \\  & ANM & 44.88 & 104.56 & 176.82 & 255.60 & 338.52 \\  & FEM & 44.12 & 105.03 & 179.55 & 260.45 & 342.96 \\  & \(e_{EBT}\)/\% & 8.93 & 10.17 & 10.89 & 11.58 & 12.91 \\  & \(e_{ANM}\)/\% & 1.73 & \(-\)0.44 & \(-\)1.52 & \(-\)1.86 & \(-\)1.29 \\ \hline \multirow{4}{*}{8.12} & EBT & 34.38 & 85.15 & 149.92 & 222.82 & 300.48 \\  & ANM & 32.63 & 78.30 & 134.80 & 197.35 & 264.04 \\  & FEM & 32.07 & 78.48 & 136.80 & 201.48 & 269.47 \\  & \(e_{EBT}\)/\% & 7.20 & 8.50 & 9.59 & 10.59 & 11.51 \\  & \(e_{ANM}\)/\% & 1.73 & \(-\)0.22 & \(-\)1.46 & \(-\)2.05 & \(-\)2.02 \\ \hline \multirow{4}{*}{9.24} & EBT & 27.06 & 68.44 & 122.39 & 184.14 & 250.67 \\  & ANM & 25.94 & 63.66 & 111.09 & 164.41 & 221.54 \\ \cline{1-1}  & FEM & 25.61 & 63.78 & 112.69 & 167.88 & 226.66 \\ \cline{1-1}  & \(e_{EBT}\)/\% & 5.66 & 7.31 & 8.61 & 9.69 & 10.59 \\ \cline{1-1}  & \(e_{ANM}\)/\% & 1.31 & \(-\)0.18 & \(-\)1.42 & \(-\)2.07 & \(-\)2.26 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of natural vibration frequencies of a CBGCSW (fixed-support).  
Figure 5: The impact of shear lag effect of CBGCSW: (**a**) simply supported; (**b**) fixed-support.
## 3 Experiments and Analysis
### Comparisons on Mainstream Methods  
We compared our RCCD model with ten other CD methods on the S2 Looking and LEVIR-CD+ datasets, including UNet [13], FC-Diff [11], FC-Conc [11], IFNet [31], SNUNet [14], L-UNet [15], BIT [16], ICIF-Net [17], P2V [18], CDNeXt [43], and the RCCD-B baseline model (lacking GS and LS modules). We evaluated the robustness of these methods against visual style errors using Large-VS and Small-VS subsets. The results of FC-Diff, L-UNet, BIT, ICIF-Net, P2V, CDNeXt, and our RCCD were visualized on both datasets.
## 2 Concept of the Replaceable Steel Link (RSL)  
The RSL method, as illustrated in Figure 1, involves installing steel links at the ends of columns to resist seismic forces. This differs from conventional bracing methods, which are typically installed diagonally across beam-column frames. The RSL method comprises a steel link designed to resist lateral loads through compression and a steel plate that connects it to existing structural members, as shown in Figure 2. The steel plate is anchored to the structural member using chemical anchors. Designed for ease of installation and replacement, the steel link is lightweight and tubular, connected to the steel plate via a welded hinge and bolt. The hinge on the bottom plate, which attaches to the horizontal member, is designed with a sliding slot. This characteristic enables the steel link to move laterally, accommodating the lateral displacement of the reinforced concrete column. The RSL method is engineered to act as a structural fuse within the building, dispersing seismic energy during an earthquake and ensuring occupant safety.  
Figure 1: Comparison of the RSL system and conventional brace system: (**a**) brace and (**b**) RSL.  
Figure 2: Components of the RSL system.  
The construction process of the RSL method involves the preparation of the surface of the existing element followed by the adjustment of the steel plate and the connection of the steel link to the plate through a sliding bolt attached to a welded hinge. Unlike conventional bracing methods applied to beam-column frames, the RSL method can enhance the overall seismic performance of a structure when applied to a single column, which is especially beneficial in structures with limited retrofitting space or where open spaces like pilotis are required. Additionally, the ease of replacing the steel link after damage is a significant advantage of the RSL method. The steel link is joined to the existing structural member through the steel plate and anchor bolts, facilitating straightforward replacement if damaged.  
Figure 3 illustrates the behavior characteristics of the RSL system, showcasing two distinct stages contingent upon the sliding slot's length.  
Under moderate wind or seismic conditions, the lateral displacement remains confined within the acceptable range. During this stage, the column primarily sustains the load through the frictional force generated between the sliding slot and bolt, along with the confinement offered by the RSL system. As the lateral load magnitude escalates, the steel link engages with the end of the sliding slot, resisting the lateral load akin to conventional bracing methods. The adjustability of the sliding slot's length facilitates the tuning of seismic energy dissipation by the RSL system, underscoring its role as a structural fuse. Furthermore, the RSL system is devised to enhance the seismic performance of the entire column, even when exclusively installed in the plastic hinge zones where damage concentrates in reinforced concrete columns. This design allows for the straightforward replacement of damaged steel links without the need for heavy machinery.
# Systematic Review of Exoskeletons towards a General Categorization Model Proposal
## 4 Analysis and Discussion
### Body Part Focused Category  
This category is intended to represent every part of the body on which an exoskeleton may be used. It generally refers to which part of the body the exoskeleton is designed to aid. Exoskeletons are not designed only for the full, upper, or lower body. They may be designed to fit on to a specific body part.  
The first class, in order of appearance in Figure 6, is the full body class, which is the smallest class of exoskeletons containing only 4% of all the exoskeletons analyzed. This can be explained by the fact that few users demand the use of a full body exoskeleton. Despite the fact that many full body exoskeletons have been developed in the past, the current tendency is the decrease in their invention and demand. For this reason, full body exoskeletons denote a risky area of opportunity, which although full of potential, are quite risky due to their low demand.  
The lower body class encompasses more than half of the exoskeletons reviewed, with a 56% frequency. This is because most exoskeletons are designed to help a user with mobility issues. The most frequent mobility issues are associated with problems in the lower half of the body. Thus, there is a high tendency to design a lower-body exoskeleton. In contrast, the upper body class only encompasses 15% of the exoskeletons reviewed. This denotes a lower frequency of mobility issues relating to the upper body. Nevertheless, upper body exoskeletons are used in many application areas, and are imperative in the exoskeleton field.  
Figure 6: Chart of the classification of exoskeletons by the body parts on which the exoskeletons are focused.  
Additionally, specific limb exoskeletons account for only 8% of all exoskeletons analyzed, and specific joint exoskeletons 16%, representing a minority of the exoskeletons designed. This demonstrates that only about a quarter of all the exoskeletons are designed for a specific body part. The specific joint category is generally any joint in which an exoskeleton can be focused, for instance, the knee, ankle, or elbow, among others. Finally, another category was included to represent any exoskeleton reviews that did not fit into any of the previously mentioned categories. This is uncommon (1% of all exoskeletons reviewed) but possible, hence the annexation of the class.
# Computer-Aided Drug Design Boosts RAS Inhibitor Discovery
## 3 Application of CADD Methods in the Development of RAS Inhibitors
### Pharmacophore Modelling  
The pharmacophore is a concept that represents the structural characteristics indispensable for ligand-target interactions [126]. According to the official IUPAC definition: a pharmacophore is the ensemble of steric and electronic features that is necessary to ensure the optimal supra-molecular interactions with a specific biological target structure and to trigger (or to block) its biological response [127]. The pharmacophore includes a range of hydrogen bond acceptors and donors, acidity, alkalinity, nucleophilicity, or electrophilicity of the functional group [128]. Based on the pharmacophore modeling of active ligands, vHTS can screen compounds with similar pharmacophore properties. If a compound has multiple pharmacophore features described in the pharmacophore modeling of active ligands, it is a multitarget compound [129].  
Pharmacophore modeling in conjunction with vHTS is widely used as a reliable and rapid ligand-based CADD approach to discover inhibitors of RAS upstream and downstream molecules [128]. RAF kinase inhibitor protein (RKIP) is a critical regulator of the RAS-RAF-MEK-ERK signaling pathway. In recent research on RKIP inhibitors by [PERSON] et al., a pharmacophore model with a series of hydrogen bonds, hydrophobic groups, and aryl rings of locoastatin (the most potent RKIP inhibitor known to date) was created [130]. Based on the pharmacophore model, compounds that have an analogous structure can be selected from the library by vHTS. As a result, the model has assigned a total of 2557 compounds out of 14,492 compounds in the Marine Natural Product Library. By optimizing the model, the number of compounds was significantly reduced, to 134 for further research.  
Pharmacophore modeling was also applied to the structural orientation of QSAR modeling. A reliable 3D QSAR model was established by pharmacophore models with similar structural properties and molecular comparison for RKIP inhibitor discovery by [PERSON] et al. [131]. Moreover, pharmacophore modeling also helps in the discovery of inhibitor targets RAS upstream and downstream molecules such as PI3K-\(\alpha\) and PKC-\(\eta\)[59] (Table 5).
# Trust and Mobility-Based Protocol for Secure Routing in Internet of Things
## 2 Background
### Attacks in RPL  
RPL is prone to several attacks associated with its features inherited from WSNs, as explained in [14]. Since IoT is an enhanced type of WSN, some attacks are thus inherited from the layout and execution of routing in WSNs. In this research, only Rank and Blackhole attacks are considered, as they are severely disruptive and have a high impact on routing topology.
# Multi-Tier Heterogeneous Beam Management for Future Indoor FSO Networks
## 3 MTH Beam Management Protocol
### Beam Management  
A basic instantiation of the MTH protocol can assume that individual UDs operate with a greedy approach and attempt to connect to the tier with highest resolution and link capacity; however, UD mobility, reliability/security requirements, and other device characteristics can also be considered when determining the tier that maximizes average throughput. An extension of the single-UD protocol would account for aggregate system capacity and resource allocation across multiple UDs. To couple the beam management across tiers for both single-UD beam refinement and multi-UD network configuration, the MTH beam management protocol should sit within the radio resource control (RRC) between the Network/Internet layer and the Link layer (Figure 4). This abstract view of the protocol implementation demonstrates the importance of tight integration with both the Data link/Physical layers and the Network layer. The protocol must integrate with the Data link/Physical layers in order to manage the PAT process across tiers. It should also integrate with the Network layer in order to manage routing of data traffic across tiers during beam refinement and/or accommodate resource allocation decisions in scenarios where the optimal tier is related to the UD context-of-use.  
Figure 3: Multi-Tier Heterogeneous (MTH) beam refinement protocol. Device assessment compares UD mobility characteristics and application reliability requirements with next tier characteristics prior to acquiring a higher resolution link.
# A Survey on MLLMs in Education: Application and Future Directions
## 2 Preliminaries of MLLMs
### Key Technologies and Architectures
#### 2.3.4 Encoder and Decoder Architectures  
The architecture of MLLMs often builds upon the transformer framework, extending it to handle multiple modalities. Two primary architectural approaches are used:  
* **Encoder-only models**: Models like CLIP focus on creating embeddings for different modalities that can be compared or combined [14]. The encoder processes input data to generate a fixed-size representation, capturing the essential features of the input regardless of its modality. This approach is effective for tasks that require matching or retrieving information across modalities.
* **Encoder-decoder models**: Models used in tasks like image captioning process input data through an encoder and generate outputs via a decoder, allowing for generative tasks [5]. The encoder transforms the input data into a latent representation, which the decoder then uses to generate a sequence of outputs in another modality. This architecture is well suited for tasks that involve translation between modalities, such as generating descriptive text from images.
## 6 Evaluation  
In this section, we evaluate the performance of ACTOR and ACTOR-D, and we compare them with standard RPL (with the maximum transmission power) and our two selected benchmarks, TPP and XRPL. The evaluations are based on both simulations and a physical testbed that consists of NRF5340 boards. Due to compatibility issues with the hardware platform in our testbed (NRF5340), XRPL was evaluated only in the simulations. We first took a look at the setup for the simulations and the physical testbed and then analyzed the results obtained using each of them.  
We used a single-channel CSMA for the communication between nodes to show that the proposed method succeeds in reducing channel contention. RPL's objective function is set to _Minimum Rank with Hysteresis Objective Function_ (MRHOF) with ETX as the routing metric. We repeated the experiments during working hours and after working hours to confirm the results, with each run lasting 10 min (both in experiments and the simulation),  
Figure 4: In ACTOR, the parent node (node B) commits to the requirements of children (nodes C and D) to keep the topology stable.  
which gave the protocol enough time to converge and be fairly compared. To support reproducible results and measurable confidence, we benefited from a recent framework called TriScale [40]. At least five independent runs are required to estimate the mean of a measured metric with a probability of 95 percent. We show the 95% confidence interval (a minimum and a maximum of five runs) in the results using error bars.