esa-sceva/satcom-chunk-collection huggingface dataset:
# Bee Venom Acupuncture in Traditional Korean Medicine: A Review of Clinical Practice Guidelines
## 3 Regimen of BVA Therapy Recommended in TKM-CPGs  
Several CPGs recommended BVA as a standalone therapy for specific conditions. This monotherapeutic approach was suggested for chronic low back pain [24], degenerative arthritis [32], facial nerve palsy [37], gout [41], knee osteoarthritis [27], prostatic hypertrophy [40], rheumatoid arthritis [31], and temporomandibular joint disorder [33]. BVA was also recommended as part of a combined therapeutic approach, incorporating other modalities alongside BVA. The accompanied treatments often included acupuncture therapy (AT) [25; 29; 32], electro acupuncture (EA) [30], usual care (UC) [28; 31; 34; 35; 36; 37], analgesics [39], physical therapy (PT) [26], and herbal medicine (HM) [38] (Table 1). The selected CPGs offered specific guidance on the BVA regimens for clinical practice. Key parameters included the acupoints, the concentration and dosage of BVA administered, and the frequency and duration of treatment sessions. Table 2 summarizes the details of the BVA regimen. \begin{table}
\begin{tabular}{c c c} \hline \hline
Many different acupoints for BVA were recommended across CPGs, depending on the targeted condition. These included local acupoints near the site of pain or dysfunction (called Ashi points) and distal acupoints on the meridians associated with the affected area. The data show that Ashi points were recommended [28; 31; 32; 34; 35; 36; 39; 41], and these were often in conjunction with acupoints of the stomach (ST) [29; 33; 34; 37; 39], bladder (BL) [24; 29; 34; 40], and gallbladder (GB) [24; 29; 32; 34; 35; 36; 37] meridians. The CPGs for carpal tunnel syndrome [30], cervical pain [25], knee osteoarthritis [27], shoulder pain [26], and tension-type headache [38] did not address specific acupoints for BVA treatment. The included recommendations exhibited similarities in BVA concentration and dosage. For example, the CPGs for ankle sprain [29], chronic low back pain [24], and degenerative arthritis [32] recommended concentrations ranging from 3000:1 to 20,000:1. In terms of dosage, several CPGs advised administering 0.05 to 0.2 cc with an increasing dose when necessary [24; 29; 32; 36]. The frequency for BVA treatments was typically around two to three sessions per week [24; 32; 36]. Notably, only one CPG for stroke specified the treatment duration of 2 to 4 weeks [36]. There were many BVA recommendations which did not specify concentration, dose, frequency, or duration [30; 31; 33; 35; 37; 38; 39; 40; 41]. Most CPGs, except for tension-type headache [38] and prostatic hypertrophy [40], explicitly recommended skin hypersensitivity testing to ensure the safe administration of BVA.
## 3 Results and Discussion
### X-Ray Diffraction (XRD) Analysis  
Figure 4 shows the diffraction patterns of the biopolymer films and the active ingredient, both as a mixture and independently. A broad band (\(<\)10\({}^{\circ}\) and \(\sim\)30\({}^{\circ}\)) is observed in the TPS-CH-M blend, reaching its maximum at 13.8\({}^{\circ}\) (29). This result is attributed to the mixture of chitosan, maltodextrin, and starch, which exhibits an amorphous structure according to the analysis. Regarding acetaminophen, the XRD pattern confirms its crystalline nature, as expected, given that acetaminophen is a drug with known polymorphic forms. The identified diffraction planes at 15.6\({}^{\circ}\), 23.9\({}^{\circ}\), 26.4\({}^{\circ}\), and 32.5\({}^{\circ}\) (20) correspond to the (101), (211), (\(-\)20), and (\(-\)311) planes, respectively [41]. Based on the indexing of the X-ray pattern, the acetaminophen used in this study exhibits a monoclinic structure, also known as Form I [42]. The TPS-CH-M-A film displayed small peaks at 23.5\({}^{\circ}\) and  
Figure 3: Molecular interaction models of (**a**) TPS-CH-M and (**b**) TPS-CH-M-A.  
Figure 2: FTIR spectra of the individual and mixed components involved in the biopolymeric films studied; (**a**) global spectrum 4000 cm\({}^{-1}\) and 500 cm\({}^{-1}\), and (**b**) amplification between 1800 cm\({}^{-1}\) and 900 cm\({}^{-1}\).  
32.3\({}^{\circ}\) 20, which, according to the X-ray pattern of API, correspond to the API present in the edible film. The incorporation of the API into the TPS-CH-M matrix induces slight modifications in its crystalline structure, as evidenced by peak shifts toward lower angles. This shift suggests an expansion of the unit cell in Form I, which may be attributed to strong interactions between the API and the TPS-CH-M matrix, leading to subtle structural alterations in the API.
## 4 6G and Dependability
### Dependability in 6G Networks
#### 4.2.2 Availability  
Availability is the assurance of access to services and resources by legitimate users, or the quality of being ready or present for immediate use [53]. As mentioned in Section 2, reliability and availability are both intertwined. As a combination of highly distributed systems, 6G networks will be capable of dissemulating failures at the edge servers by rapidly offloading the assigned processes towards a nearby server that possesses the  
Figure 1: An abstract representation of enabling intelligence in 6G networks.  
required resources. In the context of EdgeAI, if an edge server fails, then its tasks are offloaded towards a neighboring edge. This is where synchronism plays a major role, and in order to achieve this, servers must be aware of the status of each other. Furthermore, predictive analysis of available resources in neighboring edge nodes will be important. Such analysis will enable performing normal routine tasks, along with the system being able to offload tasks to neighboring nodes in cases of failures, as discussed in [36]. This process will be time consuming, but the system is perceived by the user as still functioning, even with the increase in delay that task offloading represents. Similarly, load-balancing techniques that can effectively distribute tasks among available resources can also increase the availability of critical resources [54]. Although highly related, it must be noted that a system with high availability is not necessarily reliable, thereby ensuring the expected high reliability of 6G networks does not guarantee meeting the availability criteria.
# Improvement of Network Flow Using Multi-Commodity Flow Problem
## 5 Experiment
### Routing Using Multi-Commodity Flow Problem  
To confirm the superiority of the proposed method, we compared its performance against OSPF and OSPF-ECMP by generating multiple CBR (constant bit rate) UDP traffic to verify the packet arrival rate to the destination node and the average end-to-end delay. Experiment 1 compared the proposed method with OSPF, and Experiment 2 compared it with OSPF-ECMP.  
Experiments were performed on version 3.29 of ns-3 [19]. The operating system run was Linux and the distribution was Ubuntu 18.04.6. OFSWITCH13 [20], provided as open source, was used to implement OpenFlow. This library provides switches and controllers that support OpenFlow 1.3. OSPF is not implemented in ns-3. Therefore, we created a  
Figure 4: Calculation results in serial topology. (**a**) Serial topology. (**b**) Normal calculation result. (**c**) Calculation result using the proposed method.  
new class that inherits from \(Ipv4 RoutingProtocol\), which manages the routing protocol in ns-3, and created a routing table using [PERSON]'s algorithm. In addition, We extended the OSPF class so that OSPF-ECMP selects one of the equal-cost paths in a round-robin fashion. The proposed method first solves a linear programming problem with the commodities set in the simulation. When the handshake is successful between the OpenFlow controller and the switch, the results are registered in each flow table by flow-mod or group-mod messages. We used the same topology for all experiments and added OpenFlow switch functionality to each node only in the implementation of the proposed method.  
The experiment involved use of the Cost239 topology (Figure 5), which is modeled after European cities.  
Table 3 shows the parameters used in the experiment.
# Intelligent Trajectory Prediction Algorithm for Reentry Glide Target Based on Intention Inference
## 3 Parameter Value Prediction Network
### Parameter Value Prediction Network \(\varphi_{p}(\cdot)\)  
This network is designed to learn the temporal characteristic from the truth control parameter value data, through which the variation of future control parameter values can be predicted. The truth control value data of observation and prediction are brought into the network to learn temporal relationships between them. The LSTM network, with a size of \([3,128,2]\), and the fully connected network with a size of \([128,6]\), are used for training to obtain the predicted control parameter values \(K_{D}^{p},|\beta^{p}|,K_{L}^{p}\). The network is shown in Figure 2b.  
Based on the common guidance logic of reentry glide target with the purpose of attacking strategic places, it can be usually divided into two parts of longitudinal and lateral guidance. As can be seen from Equation (2), the control parameters \(K_{D}\) and \(K_{L}\) are related to the attack angle of the target, the variation of which are usually smooth, similar to the variation of bank angle \(|\beta|\) value. Therefore, taking the control parameter values \(K_{D}\),\(K_{L}\) and \(|\beta|\), the longitudinal guidance logic of the target can be obtained. On the other hand, the lateral guidance logic decided by the sign of bank angle \(sgn(\beta)\) varies with the design, which is difficult to effectively predict. Aimed at this problem, inferring the attack intention of target can be a valid method to obtain a more accurate trajectory. To realize the intention inference, the characteristic of strategic places and the functional relationship between strategic places and trajectories should be analyzed.  
Importance Map of Strategic Places and the Association Model between Strategic Places and Trajectories  
According to the analysis of the guidance mechanism of the target [27], there are strategic places to be attacked. The effective analysis of the situation relationship between target and strategic places and inferring the intention of target can provide more prior information for the prediction of target trajectory. To analyze the attack intention of the reentry glide target, the importance map of strategic places is firstly modeled.
# Energy-Aware Security Adaptation for Low-Power IoT Applications
## 4 Dynamic Security and Energy Adaptation
### Security and Functional Requirements  
The scheme proposed to secure communications against external attacks is calculated by the controller, from information on the state of the network, as previously discussed. For this purpose, various items of information are collected from several sources and used as inputs to the controller:  
* Percentage of energy remaining in the battery of the sensing device;
* Initial security level determined for the network at hand (configured in the setup phase), as defined in Table 1;
* Minimum security level allowed by the application for this network, as defined in Table 1;
* Preference threshold, which represents the importance that the application puts on the device's battery longevity (a percentage value);
* Security assessment performed by the IDS module, which reports that a particular attack has been detected. Attacks are identified in a numerical scale, from 1 up to a configured value, with each index representing a particular type of attack;
* Security profile, expressed as a table that matches each index for an external attack reported by IDS with the impact that this attack has on the system at hand (expressed as a percentage). The setup of this table is also conducted in the context of the configuration of the application;  
It is important to note that both the initial and minimum security levels considered for the application are from Table 1 and defined for the application at hand during its setup. As can be observed, the various levels assure different and complementary security properties for the application. This strategy allows one to express the preference and requirements of different applications, regarding the relative importance attributed to security and energy savings.  
In each run of the controller's logic and from the various inputs, the controller outputs the best security scheme to apply to the system at that particular instant in time. Each security level combines a certain level of confidentiality (using symmetric Advanced Encryption System (AES) encryption) with integrity, in this case supported by the Secure Hash Algorithms (SHA), as identified in Table 1.  
The usage of different security profiles allows one to employ the controller's logic with applications requiring different security and energy preferences and management approaches. Next, we discuss the algorithm developed to support the dynamic management of security an energy, in the context of the proposed architecture.
## 3 Rotary Coupling Mechanism  
Figure 3 depicts the proposed coupling mechanism, where (a) indicates the schematic diagram of the primary-side coupling mechanism, and (b) and (c) show the COMSOL simulation model of the primary side and the whole system. Power and data are transmitted through this pair of coils.  
Figure 2: The principle of data modulation.  
In the COMSOL simulation process, the type of three-dimensional magnetic field simulation is selected, and some simplification is made in the simulation process. Both the coil and the magnetic core are equivalent to a cylinder, and the simulation boundary is a sphere, while its radius is the longest size of the coupling mechanism. In the COMSOL simulation model, the primary magnetic core of the WPT system is closely attached to the outer surface of the rotating mechanism's inner cylinder. The transmitting coil composed of litz wires is wound on the primary magnetic core. At the same time, aluminum rings are placed on both sides of the magnetic core to reduce the coupling mechanism for outside electromagnetic interference. Similarly, the secondary magnetic core of the WPT system is closely attached to the inner surface of the outer cylinder, the receiving coil is wound on the secondary magnetic core, and there are also aluminum rings on both sides of the magnetic core. The specific parameters of the coupling mechanism are shown in Table 1.  
In COMSOL, by adding the materials of each part, selecting the operating frequency at 25 kHz, and setting the current of the primary and secondary coils to 1A, the simulated magnetic field distribution is obtained as shown in Figure 4. It can be seen from Figure 4 that the largest part of the magnetic field is located in the middle of the coupling mechanism, and the magnetic flux in the air gap is small. Therefore, the magnetic flux leakage of this structure is small.  
\begin{table}
\begin{tabular}{c c c} \hline  & **Primary Part** & **Secondary Part** \\ \hline Litz wire diameter (mm) & 3 & 3 \\ \hline Number of turns & 12 & 12 \\ \hline Coil diameter (mm) & 120 & 140 \\ \hline Magnetic core diameter (mm) & 117 & 143 \\ \hline Magnetic core length (mm) & 120 & 100 \\ \hline Aluminum ring length (mm) & 10 & 10 \\ \hline Coil electrical conductivity (S/m) & \(5.998\times 10^{7}\) & \\ \hline Aluminum electrical conductivity (S/m) & \(3.774\times 10^{7}\) & \\ \hline Magnetic core relative permeability & 2300 & \\ \hline The relative permittivity of all materials & 1 \\ \hline \end{tabular}
\end{table}
Table 1: Coupling mechanism parameters.  
Figure 3: The proposed coupling mechanism: (**a**) The schematic diagram of primary side coupling mechanism; (**b**) the COMSOL model of primary side coupling mechanism; (**c**) the COMSOL model of the whole system.
# Misalignment Fault Prediction of Wind Turbines Based on Combined Forecasting Model
## 2 Forecasting Method and Application
### Evaluation Criteria for Forecasting Models
#### 2.5.2 Forecasting Evaluation Index  
In order to compare the prediction effects of the combined model and the single ones, the following 3 indexes are calculated.  
Assume that \(y=(y_{1},y_{2},\ldots,y_{N})\) is the actual value of the data, \(\hat{y}=(\hat{y}_{1},\hat{y}_{2},\ldots,\hat{y}_{N})\) is the predicted value, and \(\overline{y}\) is the average of the actual value.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Precision Grade** & \(C\) & \(\mathbf{P}\) \\ \hline Level 1 (Good) & \(C\leq 0.35\) & \(0.95\leq P\) \\ Level 2 (Qualified) & \(0.35<C\leq 0.5\) & \(0.80\leq P<0.95\) \\ Level 3 (Barely qualified) & \(0.5<C\leq 0.65\) & \(0.70\leq P<0.80\) \\ Level 4 (Unqualified) & \(0.65<C\) & \(P<0.70\) \\ \hline \hline \end{tabular} Where the precision grade of grey prediction model is the maximum of the grade of \(P\) and the grade of \(C\).  
\end{table}
Table 2: The precision grade of accuracy test.  
Figure 3: The flow chart of combined prediction.  
Root Mean Square Error (RMSE):  
\[RMSE=\sqrt{\frac{1}{N}{\sum\limits_{i=1}^{N}{{{{\left({{y_{i}}-{\hat{y}_{i}}} \right)}^{2}}}}}},\ RMSE\in[0,\ +\infty) \tag{16}\]  
The closer the RMSE is to zero, the smaller the prediction error and vice versa.  
Mean Absolute Error (MAE):  
\[MAE=\frac{1}{N}{\sum\limits_{i=1}^{N}{{{\left|{{y_{i}}-{\hat{y}_{i}}}\right|}} }},\ MAE\in[0,\ +\infty) \tag{17}\]  
The closer the MAE is to zero, the smaller the prediction error and vice versa.  
Coefficient of determination (\(R^{2}\)):  
\[R^{2}{=1-\frac{\sum\limits_{i=1}^{N}{{{{\left({{y_{i}}-{\hat{y}_{i}}}\right)}^{ 2}}}}}{\sum\limits_{i=1}^{N}{{{{\left({{y_{i}}-{\overline{y}}\right)}^{2}}}}}} },\ R^{2}\in[0,\ 1] \tag{18}\]  
The larger the \(R^{2}\) is and the closer it is to 1, the better the fitting effect of the prediction model and vice versa [39].
# Arithmetic Optimization AOMDV Routing Protocol for FANETs
## 3 AO-AOMDV Routing Protocol
### Proposed Method  
The source nodes use the AOMDV protocol search for multiple routes to the destination node by sending route request (RREQ) packets without considering other factors of the routes and only selecting the route with the minimum number of hops as the transmission path. In this regard, this paper introduces a new fitness function based on the AOMDV protocol and utilizes arithmetic optimization for route selection. In the proposed method, when multiple routes to the destination node are found, the source node needs to consider the possibility of packet loss caused by link failures and thus selects a stable and optimized route with higher residual energy and lower traffic. In other words, the fitness function will consider the following factors:  
* The congestion situation of each node in the routing;
* The residual energy of each node in the route;
* The link holding time for each path in the routing.  
The selection of the route from the source node to the destination node is based on the highest fitness value of the route. The main criteria followed by the optimal route are: (a) having the highest residual energy, (b) having the highest link holding time, and (c) containing lower traffic. The source node sends data packets through a stable route with the highest residual energy and fewer congested nodes.  
In the proposed system, the UAV nodes are uniformly distributed in three-dimensional space to form a FANET. The following assumptions are made for the FANETs:  
* The mobility of nodes may lead to link disconnections and time delays;
* All moving nodes in the FANETs are initialized with equal energy and quality;
* Nodes exhibit random mobility, resulting in constantly changing distances between nodes;
* FANETs consist of mobile nodes, each having a unique identification number.
# A Systematic Review of Virtual Reality Interfaces for Controlling and Interacting with Robots
## 5 Future Directions
### Usability  
**Diversifying user-studies.** User studies on average are with a small group of people, typically 20 or less, and skew towards more males and in the 20-30 age bracket. This is understandable as researchers usually take advantage of university students in their department, which is often in the areas of computer science and engineering. However, this group is not representative of the broader community that could make use of these interfaces. There are already several applications robots are actively being deployed in, such as factories, assisting in bomb disposal, or search and rescue missions. Therefore, it is important to have users in these domains included in user studies. Additionally, in general it is important to have a diverse group of individuals as different groups will bring in different viewpoints, such as, expert vs. non-expert, or younger generation vs. older generation, etc.  
**Conducting real-world application user-studies.** Presently, most user-studies in understanding usability of VR robot interfaces are simplified, fabricated tasks and are not well grounded in real-world applications. They are also not conducted in real-world environments and instead are often done inside of a lab or structured environment that is controllable. However, the real-world is unstructured and often unpredictable. Therefore, it is important to test these systems in real-world environments for real-world applications in order to more accurately understand the usability of these systems.
# Topology-Aware Retinal Artery-Vein Classification via Deep Vascular Connectivity Prediction
## 3 Results and Discussion  
### Dataset  
We experimented on two retinal image datasets, namely DRIVE [18] and IOSTAR [19], which were acquired using a fundus camera and a scanning laser ophthalmoscope (SLO) camera, respectively. For training the networks in Figure 3, GTs for optic disc segmentation, AV classification, and topology estimation are required, along with those for binary vessel segmentation.  
The DRIVE dataset, which consists of 40 images, was divided into the first and last half for testing and training, respectively. Two AV GT datasets, called AV-DRIVE [20] and CT-DRIVE [9], are publicly available for the DRIVE dataset. In the AV-DRIVE dataset, all vessel pixels were marked as arteries or veins for all training and testing images. On the other hand, vessel centerline pixels were labeled only for the test set images in the CT-DRIVE dataset. We evaluated a model trained with the AV-DRIVE training set on the CT-DRIVE test set. The GTs for optic disc segmentation and topology were manually achieved by the first and second authors, who both have more than five years of experience in developing technology for vessel analysis. Furthermore, the achieved GT was inspected and verified by a board-certified ophthalmologist to ensure its validity.  
For the IOSTAR dataset, we used available AV classification and topology GTs introduced respectively in [11; 19], and acquired our own GTs for optic disc segmentation. Since the topology GTs were made for the downsampled version of the images, we accordingly transformed the other GTs to coincide with the size. For 18 images that had all the types of GTs, the first and last halves were used for training and testing, respectively.