esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
RPL is the IPv6 routing protocol for low-power and lossy networks in the Internet of Things which supports point-to-point (P2P) communication. However, the partition of two modes of operations (MOPs) in downward routing complicates achieving high performance. In the non-storing mode, a downward route with the longest path length is often picked. In the storing mode, the downward routes to some child nodes cannot be stored by their parent because of the limitation of memory space, which makes some nodes unreachable. In addition, there are extra performance costs of mixing or switching the two modes in the existing hybrid-MOPs works. Therefore, this article proposes TB-RPL to achieve an enhancement of RPL with a better performance of P2P communication. It allows all nodes to behave in a single and uniformly fused MOP that solves the problems mentioned above. The proposed mode uses a modified routing header format and introduces a threshold to the number of route entries. We implemented and compared TB-RPL with related mechanisms in Cooja simulator based on the Contiki-NG operating system. Simulation results verify that TB-RPL eliminates the three identified problems. Consequently, it significantly improves the performance of P2P communication in LLN.  
RPL; mode of operations (MOPs); hybrid; downward routing; point-to-point (P2P); low-power and lossy network (LLN); internet of things (IoT) 2023
# Position-Monitoring-Based Hybrid Routing Protocol for 3D UAV-Based Networks
## 4 Experimental Evaluation
### Results and Discussion
#### 4.3.2 Delay(s)  
Figure 7 shows the average delay(s) results of the 20, 40, and 60 (m/s) speed scenarios. At 20 m/s, all proposed routing protocols initially perform well with little difference, while GEOD has less delay as compared to other benchmark schemes. The average delays of other versions of GEO (GOEC and GEOMF) and AODV linearly increase as compared to all versions of the proposed routing protocol in the case of connection-10. The centroid version (PHCC) of the proposed routing protocol has about 1% to 70%, 6% to 75%, 7% to 80%, and 2% to 60% lower delay than the AODV, GEOC, GEODMF, and GEOD routing protocols, respectively, as shown in Figure 6. In the 40 m/s case, we further increase the number of moving UAVs in the network for analysis. The GEOC version of the position-based GEO routing protocol generates more delay than other routing protocols in its class of routing. The AODV has about 5% more delay than the GOEC routing protocol, and it has about 3% to 12% higher delay than GEOMF and GEOD, respectively, at 50 moveable nodes. When we compare GEOC with the proposed routing protocol, it has about 50% to 55% higher delay than all variants of the proposed routing protocols. Similarly, we also compare all routing protocols when a speed of 60 m/s is used. Initially, the proposed routing protocol consists of a lower delay. However, as the number of moveable UAV nodes increases, the proposed routing protocol's network delay(s) also increases. However, all proposed variants of PMHRP have low network delay(s) compared to benchmark routing protocols. The PHCS has a higher delay in proposed schemes due to the fact its PHC is selected by the static approach (not considering the position and other aspects for selection). Furthermore, the PHCC routing protocol shows up to 68% to 75% lower delay than all variants of GEO and 74% lower than AODV routing protocols. The reason behind the lower delay of the proposed variants of the PMHRP is that they have a lower network delay since they contain global routing information. On the other hand, all variants of the GEO routing protocols include local routing information; therefore, they do not control delay efficiently. The centroid variant of the PMHRP has an excellent ability to maintain the network delay, even in high call load scenarios because of its centroid position, and it provides complete routing information to all UAVs by considering the centrality measure values.  
Figure 6: PDR versus number of UAV nodes at different speeds (20, 40, and 60 m/s).
## 3 Collision-Free Hybrid MAC Protocol Based on Pipeline Parallel Transmission
### Optimization of the Handshaking and Data Transmission Schedules
#### 3.4.2 Data Packet Scheduling  
The major difference between data packet scheduling and TDMA time slot assignment is that there might be more than one channel and a variable number of source nodes in the former. Therefore, the above optimization method is modified to adapt to these changes for data packet scheduling as the following:  
1. Let \(S_{i}=(s_{i},d_{i})\), \(1\leq i\leq K\), denotes the source and destination pairs in the current transmission cycle, where \(s_{i}\), \(d_{i}\),
# On Reliable and Efficient Data Gathering Based Routing in Underwater Wireless Sensor Networks
## 5 System Model  
Figure 3 shows that nodes are randomly deployed in the network field. Depending upon the features that are represented in Table 2, we categorize the network nodes into member nodes (MNs), gateway nodes (GNs) and autonomous underwater vehicle (AUV). In this work, the following assumptions are made:  
* MNs continuously sense the required information from the surroundings.
* Nodes repeatedly measure their residual energies and deliver their energy states towards their predecessors using a control packet.
* Upon reception of location information from the in-range nodes, each member node maintains and updates its neighbors list.
* Nodes are capable to accept or discard a packet on the basis of BER. To enhance the data reliability they may also ask forsingle or double retransmissions of the erroneous data packet from the sender node.  
Furthermore, we use three different transmission ranges that correspond to different regions called slices. Nodes that belong to the same slice are the _Sibling nodes_.  
**Rule 1:**_Sibling nodes are restricted to do not exchange data packets with each other._  
This rule minimizes the number of hops between source and destination nodes. However, eligible neighbors are also minimized in number. This situation is highly undesirable during later simulation time when most of the network nodes are dead. To avoid this situation, an intermediate rule is defined, which provides the solution by dividing the 1 st slice further into two layers. Layering is conducted by partitioning the width of slice 1 into two regions, such that upper and lower region of 1 st slice makes the 1 st and 2 nd layer respectively. Source node itself lies in the 2 nd layer.
## 1 Introduction
In a scenario with a large number of users, the successive interference cancellation (SIC) limits the capacity of a single STAR-RIS in the proposed STAR-RIS-aided NOMA system to handle multiple users. A common approach is to allocate a specific region to each STAR-RIS. Therefore, each STAR-RIS is responsible for managing communication within its designated area. The problem we address is how to maximize system utilization and SE for each user by optimizing the deployment of STAR-RIS-equipped UAVs while ensuring compliance with SIC limitation. \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline  
Based on this analysis, we consider a STAR-RIS-aided NOMA network where the STAR-RIS is equipped in a UAV, and we focus on constructing the system model and conducting an approach to maximize the SE. Our work is novel and contributive in the following aspects:  
* We jointly optimize the UAV's flying altitude, antenna beamforming, and the phase shifts of the reflective elements, demonstrating significant performance gains from this integrated design. * We formulate an SE maximization problem under constraints including the total power, UAV altitude, and coverage. To address its complexity, we adopt gradient descent and semidefinite relaxation (SDR) techniques and propose an efficient algorithm for its solution. * Numerical results demonstrate that the proposed gradient descent-based algorithm significantly outperforms baseline schemes in terms of SE and BER. OrganizationThe remainder of this paper is organized as follows. Section 2 presents the system model, and Section 3 presents the problem formulation. Section 4 introduces the proposed algorithm and provides a solution to the problem. Section 5 presents the simulation results. Finally, Section 6 concludes the paper.
# A Density-Based Random Forest for Imbalanced Data Classification
## 2 Related Work
### Algorithm-Level Methods for Imbalanced Data
A new method based on an information granule (IGRF) [28] applied the idea of BRAF to crime detection. The IGRF process combines information granularity and the series of crime pairs in k neighbors to form the critical areas. Then, the same procedure as BRAF is used to construct the model. With the widespread use of deep learning, some scholars have studied the class-balanced loss function, distinct block processing of images, data augmentation techniques, etc. [PERSON] et al. [29] proposed a novel data augmentation technique based on covariate synthesis minority oversampling (SMOTE) to address data scarcity and category imbalance. [PERSON] et al. [30] proposed an efficient malware detection system based on deep learning to address imbalances in malware datasets. [PERSON] et al. [31] proposed a novel deep residual convolutional neural network (DRNN) for cassava mosaic disease detection in cassava leaf images. [PERSON] et al. [32] proposed a new data augmentation technique that uses the secondary dataset RVL-CDIP to normalize imbalanced datasets. To sum up, on the data level, oversampling methods (SMOTE, ADASYN, etc.) balance the dataset for classification by adding minority samples. If there are too many noise samples in the minority samples, the distribution of the original dataset will change after the sample expansion. Undersampling methods (ENN, etc.) balance the dataset by reducing the number of majority samples. For highly imbalanced datasets, deleting too many valid samples makes it impossible to fully train the dataset. On the algorithm level, BRAF finds the k-nearest majority neighbors for each minority sample. Then, it merges the k-nearest majority neighbors with the minority samples directly to construct critical areas. The method used in BRAF cannot identify boundary samples accurately and introduces noise and redundant samples. It also impacts the subsequent classifications. To solve this problem, we propose a DBRF algorithm. DBRF obtains a random forest using the density distribution method to build the density domain based on BRAF. DBRF constructs the density domain based on the boundary minority samples. The aim of the study was to locate samples lacking a clear decision boundary and enhance the ability to identify them.
## 2 Basic Method
### Dispersion Analysis  
Based on the TEM-, LSM-, and TLM-calculated SGFD coefficients given above, we use the time-space domain dispersion relation formulation to compare the relative error of the phase velocity for the 2D acoustic SGFD modeling, as follows [36]:  
\[\delta=\frac{1}{r\beta}\text{sin}^{-1}\bigg{\{}r\sqrt{\left[L1\right]^{2}+\left[ L2\right]^{2}}\bigg{\}} \tag{18}\]  
\[L1=\sum_{n=1}^{N}\left(a_{n}sin((2n-1)\beta)cos(\vartheta)\right. \tag{19}\]  
\[L2=\sum_{n=1}^{N}\left(a_{n}sin((2n-1)\beta)sin(\vartheta)\right. \tag{20}\]  
\[r=\frac{v\tau}{h} \tag{21}\]  
Figure 1: Variations in the absolute error of the dispersion relation (\(\epsilon\)) of the SGFD for \(M=7\) across the full wavenumber range, as determined by the TEM, LSM, and TLM. Panels (**d–f**) provide a detailed magnification of panels (**a–c**), respectively. The SGFD coefficients employed in this analysis are outlined in Tables 1–3.  
where \(\vartheta\) represents the angle of the assumed plane wave propagation direction, \(\tau\) denotes the time step, \(h\) signifies the spatial step, and \(r\) is the Courant number. Given the significantly larger absolute errors in the TLM under conditions \(L=2\) to \(L=7\) compared to \(L=1\), the dispersion analysis is simplified by exclusively employing the TLM under the \(L=1\) condition for comparative analysis. We have plotted the curves representing the numerical phase velocity dispersion relations in the time-space domain for the TEM, LSM, and TLM(\(L=1\)) across various plane wave propagation directions. Furthermore, a comprehensive comparison is conducted over the entire wavenumber range.  
Figure 2 illustrates the variation in \(\delta\) with \(\beta\) across the full range of wavenumbers and for different incidence angles \(\theta\), where the length of the SGFD operator is 7 and \(r\) is 0.16. In panels (a, b, c) of Figure 2, it is clearly demonstrated that within the small wavenumber range, the TLM(\(L=1\)) exhibits superior capability in suppressing phase velocity dispersion, whereas the LSM shows the least effectiveness. Similarly, in panels (a, d, e) of Figure 2, within the medium wavenumber range, the TLM(\(L=1\)) continues to perform best in reducing phase velocity dispersion, followed by the LSM, with the TEM displaying the weakest suppression. In panels (a, f, g) corresponding to the wide wavenumber range, the abilities of TLM(\(L=1\)) and LSM to minimize phase velocity dispersion errors are comparable, while the TEM-based results show more significant dispersion errors. Overall, the TLM(\(L=1\)) consistently demonstrates the strongest capability in mitigating phase velocity dispersion across the entire wavenumber range.  
Figure 2: _Cont._
# Research on Multicore Key-Value Storage System for Domain Name Storage
## 3 Model Description
### Cache Set Method  
The cache set method is to split the cache items according to their sizes. That is, cache items of similar size are stored in a sub-table, and each sub-table is differentiated by cache item size. As the memory space occupied by the cache items in each sub-table is allocated in advance, this can minimise memory waste. Ideally, cache items of the same size should be grouped together and evenly spaced.  
Figure 1: System design diagram.  
[PERSON] et al. [37] indicate an independent request stream that follows a similar Zipf distribution is sufficient to fit the actual web request law. To get closer to the distribution of DNS data in the network, we use a more refined split method. According to the statistics in the Verisign [38] domain name database, the domain name length distribution is shown in the Figures 2 and 3. The number of shorter domain names is significantly higher; the shorter the domain name, the easier it is for users to remember and understand. Such domain names are used more frequently. Shorter domain names on the internet are also visited more frequently.  
Therefore, during the initialisation of the sub-cache table, more space is allocated to those sub-tables that store shorter domain names. For example, 50 MB of memory is allocated for a sub-table for storing domain names of 3-bytes long, but 1 MB of memory is allocated for a sub-table for storing a domain name 65-bytes long.  
As shown in Figure 4, the system allocates memory blocks uniformly during initialisation to improve memory utilisation, and each memory block stores a sub-cache table. Each sub-cache table has a separate linked list, which is the sub-linked list.  
Figure 3: Distribution of lengths of all the registered.net domains.  
Figure 2: Distribution of lengths of all the registered.com domains.  
As shown in Figure 5, we use the hash table to locate the cache item, so that it can be found and moved. The hash table uses a linked list to resolve hash conflicts. When multiple cache items are hashed to the same key, pointers connect the cache items. The entire cache system needs to maintain a huge hash table to perform a global search on cache items. When the hash table is updated, only the hash bucket is locked to reduce the negative impact of the lock.  
When moving, adding, or deleting cache items, a linked list is used to perform operations. When locking the linked list, only the entire linked list can be locked. Thus, the locking operation has a huge impact on the linked list. To avoid the performance degradation caused by lock operations, we split the cache table into sub-cache tables so that only a single sub-cache table needs to be locked for each operation. Each sub-linked list is included in the cache sub-table, so that the cache table can only be synchronized with updated sub-tables during synchronization.
## 4 Discussion
### Strengths and Limitations  
An extensive and meticulous search strategy was implemented to ensure that every relevant article was included and to minimize bias in this review. Eligibility was assessed independently by two authors, adhering to pre-defined criteria. Since reliable translational resources were unavailable, including only English language studies in this review can be considered a limitation. The retrospective nature of the case-control study that was inherent to this research can be considered as another limitation. This study evaluated APE in the maxillary anterior teeth, but future investigations should include the posterior teeth and the mandibular teeth. Moreover, clinical examination of APE in the subjects recruitedfor this study might provide more in-depth information. The findings of this study should be further assessed through randomized control trials which account for long-term clinical and radiological changes after orthodontic treatment.
## 4 Experiments
### Ablation Study  
To demonstrate the advantage of collaborative search, The results that only search Backbone and FPN are reported in Table 1 and illustrated in Figure 9. Specifically, the default architecture of backbone is set as the architecture of DARTS [22] and the default architecture of FPN is set as the architecture of PANet [60]. The depth of network is controlled by the number of stacked normal cells \(N\). The baseline (default architectures of both backbone and FPN) only achieves 33.9% mAP when N = 1 and 38.1% mAP when N = 2. If the backbone is searched independently, the performance of the discovered model improves by 0.2% (for N = 1) and 0.6% (for N = 2); if the FPN is searched independently, the performance of the discovered model improves by 1% (for N = 1) and 0.9% (for N = 2); If the backbone and FPN are searched jointly, the performance of the discovered model improves by 2.5% (for N = 1) and 1.8% (for N = 2). In general, we can obtain the following conclusions: first of all, if compared with baseline only, it can be found that whether it is a separate  
Figure 4: Curves of precision-recall and F1-score during the search process. The supernet is trained for 30 epochs. Blue line denotes the averaged value among all 80 classes.  
Figure 3: Illustration of the performance curves during the search process. The supernet is trained for 30 epochs.  
search or a collaborative search, whether it is a Backbone search only or a FPN search only, the final structure is significantly improved compared with baseline. The results also reflect the effectiveness of our algorithm. Secondly, by comparing the collaborative search and separate search, we can find that even if everyone is better than the baseline, the effect of collaborative search is more obvious. From the empirical analysis, the results are consistent with the conjecture. Previous studies tend to search only one part and fix the other part, which will lead to ignoring the connections in different structures. End-to-end networks should be regarded as a whole rather than a segmented part. Finally, we notice that the increase in the result gradually decreases with the increase in the model size: for N = 1, the performance of joint search surpasses that of independent search by nearly 2% mAP, while for N = 2, the improvement is less than 1%. This is because the complexity of the model search increases with the increase in the model size. If the same experimental parameters are maintained, the effect will inevitably decrease.  
Figure 5: Illustration of the performance curves during the evaluation process. The searched model was trained for 300 epochs.  
Figure 6: Curves of precision-recall and F1-score during the evaluation process. The searched model was trained for 300 epochs. Blue line denotes the averaged value among all 80 classes.
## 4 Circuit Design  
### Charging Circuit  
In the process of circuit design, we considered the impact of extremely cold environments of the Polar Region. Thus, low temperature characteristics and power consumption of the circuit devices must be examined in detail. The charging circuit consists of four parts: (1) the solar and SWT charging circuit, (2) the driver circuit, (3) the detection circuit and (4) ADC circuit and auxiliary circuit. The solar and SWT charging circuit is shown in Figure 10.  
The solar charging circuit (Figure 10a) is a Buck circuit where C\({}_{1}\) and C\({}_{2}\) are electrolytic capacitors with values of 1000 \(\upmu\)F; L\({}_{1}\) is a toroidal inductor of 470 \(\upmu\)H; the resistance values of R\({}_{1}\), R\({}_{4}\) and R\({}_{5}\) are both 2 K\(\Omega\); the resistance value of R\({}_{2}\) is 1 K\(\Omega\); the resistance value of R\({}_{3}\) is 10 K\(\Omega\); and D\({}_{1}\) and D\({}_{2}\) are dual high-voltage Schottky rectifiers (MBR20H100 CT, Vishay Semiconductors, TX, USA). The MOSFETs IRF540 NPbfF (International Rectifier, El Segundo, CA, USA) are selected for charging by controlling the on-off state.  
The SWT charging circuit is shown in Figure 10b. The AC generated by the SWT is rectified to DC using 3 SRB5016 (ASEMI, Ningbo, China), with an operating temperature range between \(-55\)\({}^{\circ}\)C and \(150\)\({}^{\circ}\)C. An unloading circuit is designed in the SWT charging circuit to protect the SWT and charging circuit from damage when the wind speed in the polar region is too large and exceeds the limits of operation of SWT. C\({}_{3}\) and C\({}_{4}\) are electrolytic capacitors with values of 1000 \(\upmu\)F, and L\({}_{2}\) is a toroidal inductor of 470 \(\upmu\)H. Under the condition that wind speed reaches the cut-in value, the MCU (MSP430F5438A, Texas Instruments, Dallas, TX, USA) can collect the voltage and current of the small WT via MPPT control strategy. MCU implements the MPPT strategy by changing the duty cycle of the PWM.
## 4 Arsenic Removal Processes
### Adsorptive Processes
#### 4.4.2 Application of Nanoparticles for Arsenic Removal from Water
The titanium dioxide nanoparticles provided an effective, low-cost and single-step process for arsenic removal from contaminated water. Nevertheless, due to their particle sizes in the nanometer range, some care and extreme caution must be taken to prevent the possible dispersion of these nanoparticles into the environment [157]. This may be prevented or avoided by either granulation of these nanoparticles into micron-sized particles or loading onto very porous host materials [157]. The reported removal efficiencies achieved by some arsenic removal processes are summarized in Table 3. Evidently, the removal processes based on the use of Fe\({}_{2}\)O\({}_{3}\) filter,As(III) oxidation by (OCI\(-\)) and Fe precipitation, enhanced coagulation/filtration with ferric chloride, iron doped activated carbon, hybrid activated alumina, iron based sorbents, layered double hydroxide (LDH), modified zeolites and laterite and limonite achieved removal efficiencies \(\geq\)95%, thus demonstrating that processes involving oxidation and filtration, precipitation and adsorption are effective for removal of arsenic from contaminated water. The chosen process therefore depends on the simplicity and convenience of use if to be adopted for providing drinking water for human consumption.