esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In this paper, the series-fed circularly polarized and beam-tilted microstrip patch antennas operating at 5.84 GHz were proposed, simulated, and fabricated. The proposed series-fed antennas have a radiating patch with truncated corners to implement CP without a complex feeding network. The phase between radiating patches was controlled by the length of the U-shaped bend transmission line to achieve beam-tilted, maintaining compact antenna size. The performance of the proposed series-fed antennas is confirmed by measurements, and the measurement results closely align with the simulation outcomes. The proposed antennas have peaks of measured RHCP realized gain of 14.59 dBic, 13.09 dBic, 12.07 dBic, and 10.71 dBic at the tilted-beam angles of \(\theta=0^{\circ}\), \(15^{\circ}\), \(30^{\circ}\), and \(45^{\circ}\), respectively. In addition, AR performance from each tilted angle shows that it lies below 3 dB or slightly above, within HPBW. From these results, the proposed antennas are expected to be applied in various fields, such as wireless power transmission, radar, and communication systems.  
Writing: M.Y.P. and J.H.K.; Antenna design and simulation: J.H.K. and M.Y.P.; Laboratory tests: M.Y.P. and J.H.K.; Methodology: K.C.H.; Conceptualization: M.Y.P., J.H.K., S.-h.Y., W.L., Y.Y. and K.C.H.; Writing Review and Editing: K.C.H.; Resources: S.-h.Y. and W.L.; Project administration: S.-h.Y.; Supervision: K.C.H. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Korea Electrotechnology Research Institute (KERI) Primary research program of the Ministry of Science and ICT (MSIT)/National Research Council of Science and Technology (NST) (No. 24A01068).  
Not applicable.  
The data presented in this study are available in the article.  
The authors declare no conflict of interest.
# Enhancing Sign Language Recognition Performance Through Coverage-Based Dynamic Clip Generation
## 3 Methods
### Dynamic Clip Generation  
Our dynamic clip generation method involves adjusting the number of clips based on target coverage \(T_{Cov}\), ensuring an optimal trade-off between inference accuracy and inference time. The process begins by setting a target coverage \(T_{Cov}\), a heuristic value determined from the experimental results. This target coverage represents the proportion of video content that should be covered by the selected clips.  
The optimal number of clips \(N_{opt}\) is then dynamically calculated using the total number of frames \(F_{total}\), the number of frames per clip \(F_{clip}\), and the target coverage \(T_{Cov}\). The method ensures that the number of clips is adjusted to meet the target coverage, taking into account that videos of different lengths may require varying numbers of clips to maintain an appropriate level of coverage.  
The key steps of the dynamic clip generation process are described in Algorithm 3. As an example, consider a video with \(F_{total}=100\) frames, a target coverage of \(T_{Cov}=1.0\), and \(F_{clip}=16\) frames per clip. In this case, the number of clips \(N_{opt}\) can be calculated as shown in Equation (9).  
```
1:Input:\(F_{total}\), \(F_{clip}\), \(T_{Cov}\)
2:Output:\(N_{opt}\) (Optimal number of clips)
3:Step1:Calculate the Optimal Number of Clips
4:\(N_{opt}\leftarrow\max\Big{(}1,\Big{|}\,T_{Cov}\times\frac{F_{total}}{F_{clip}} \Big{|}\Big{)}\)
5:return\(N_{opt}\)
```  
**Algorithm 3** Dynamic clip generation algorithm  
\[N_{opt}=\max\Big{(}1,\Big{|}\,1.0\times\frac{100}{16}\Big{|}\,\Big{)}=\max(1,7)=7 \tag{9}\]  
* The optimal number of clips, \(N_{opt}\), is calculated by taking the maximum of 1 and the ceiling of the ratio \(\frac{T_{Cov}\times F_{total}}{F_{clip}}\). The ceiling function is used because the number of clips must be an integer value. If the computed value is not an integer, it is rounded up to ensure that all frames are sufficiently covered. For instance, if the calculated number of clips is 6.2, it is rounded up to 7 to ensure that enough clips are generated to cover the total number of frames.
* As shown in Equation (9), our dynamic clip generation method adjusts the number of clips in real time, maintaining an optimal balance between inference time and accuracy. This leads to improved system performance and efficiency.  
Once the optimal number of clips is generated, each clip is independently processed by the classification model to obtain prediction probabilities over the target classes. To aggregate these results, we apply a simple yet effective post-processing step: the final prediction is computed by averaging the prediction probabilities across all clips. This ensemble-style approach mitigates variability among individual clips and enhances the robustness of the final classification. By considering multiple temporal segments and aggregating their outputs, the system is able to better capture diverse temporal patterns present in the video.
# Vulnerability of Satellite Quantum Key Distribution to Disruption from Ground-Based Lasers
## 4 Discussion
### Challenges Facing the Laser Terminal  
The effectiveness of the laser terminal will be severely impacted by weather conditions, such as scattered or thin cloud. However, the QKD ground station is also reliant on  
Figure 8: Additional QBER generated from two symmetrically placed 1 kW laser terminals both 1000 km from the receiving station so that one overlaps the early and the other the late part of the transmission window. At no point during the transmission window from the Satellite is any secure key generated as the additional QBER always exceeds 11%. Green trace, detected QKD signal photon rate; blue trace, detected excess noise photon rate; red trace, additional QBER due to excess noise photons; dashed red line, QBER limit at which no secure key can be generated.  
good weather conditions. If cloud cover and other weather conditions near the ground station are suitable for effective QKD operation, then it is reasonable to assume that a laser terminal within 100 km of the ground station will be experiencing similarly suitable weather conditions. For the case where the laser terminal is around 1000 km away, cloud cover and other weather conditions could be very different. Deploying the terminal to a favourable position, or deploying multiple terminals (particularly in the case of scattered cloud) would be the only way to overcome adverse weather conditions. A malicious party would likely require multiple laser terminal to provide effective continuous, rather than sporadic, disruption from large distances from the ground station.  
High ground will usually be more favourable for the laser terminal because it would allow the terminal to track the satellite at lower elevations, though the greatly increased loss through the atmosphere at low elevations will significantly reduce the excess photon rate.  
Accurately locating the satellite will require accurate knowledge of the terminal's own position. GPS will likely be more than adequate for accurate location of the laser terminal, however, satellites can be several hundred metres from their orbital track predicted from previous orbits. The laser terminal will have to acquire the satellite via scanning, optical tracking, or up-to-date information from radar or other tracking methods. A satellite such as _Micius_ might be located relatively easily due to the transmission of tracking beacons, however, these could likely be phased out in future systems in order to counter the threat of disruption from simple laser terminals.  
Pulsed lasers would greatly increase the effectiveness of the laser terminal by enabling greater disruption at lower average power levels (higher peak output for the same average power), but would require knowledge of the QKD detector gating. The extent to which it is possible to retrieve this information will need to be investigated, but could include monitoring the transmissions from the QKD transmitter.
## 2 Related Work  
### Conventional Routing Protocol  
Traditional routing protocols have developed rapidly in the last few decades. The authors of [23] carried out performance tests and comparisons concerning classical routing protocols for AODV, OLSR, Dynamic Source Routing (DSR), and the geographic routing protocol (GRP). The simulation results indicate that different routing protocols can be used in different UAV communication network scenarios. Moreover, some studies have proposed amending the traditional routing protocols. The authors of [24] introduced a predictive OLSR (P-OLSR) protocol that takes advantage of the GPS information to predict the quality of the links among the nodes. The experiment's results show that the P-OLSR protocol performs better than the classic OLSR in average goodput. The authors of [25] proposed the mobility- and load-aware OLSR (ML-OLSR) routing protocol. The results of the study show improved delay performance and packet delivery rate compared to OLSR. However, each node consumes large amounts of energy for movement and communication, especially for energy-limited small UANETs. Thus, the ML-OLSR protocol lacks an energy metric to achieve a better performance. The authors of [26] proposed a new mobility model based on spiral line (SLMM) for aerial backbone networks. With the help of the mobility model, it can improve AODV. These routing protocols are all based on topology and have a low degree of autonomy. They cannot achieve a better performance to adapt to the high mobility, the complex flight environment, and the diverse flight tasks. In addition, the routing protocols based on topology aim at building the whole network routing table, which increases the maintenance costs. Thus, location-aided routing (LAR) [27] uses geographic location information to reduce the routing overhead. The later classic greedy peripheral stateless routing (GPSR) protocol [11] is proposed. Every UAV periodically broadcasts geographical location information to assist packet forwarding. GPSR uses greedy forwarding to reduce the end-to-end delay. With the increasing number of network nodes, the GPSR protocol has stronger expansibility, which is more suitable for UANETs. However, it only achieves local optimality and causes excessive routing overhead when nodes are faced with a void area. To overcome the excessive overhead caused by perimeter forwarding. In [28] a game theory was used to select the next-hop node considering the forwarding angle and distance in the perimeter forwarding stage. In reference [29], the authors proposed ECORA, which considers the positioning prediction and link expiration time to provide a better quality of experience (QoE).
## 2 Background and Example
### Digital Calibration Certificates
XML is advantageous as a data exchange format for DCCs because it is both machine-readable and human-readable, allowing for easy processing by computers and understanding by humans [26]. DCCs are electronic documents that use XML as their data exchange format and are validated against schema definitions. The use of XML allows for the consistent and predictable formatting and structuring of data, which is essential for information exchange between different systems, and the use of XML schema definitions (XSDs) ensures that the data are structured and formatted in a consistent and predictable way. The use of XML as the data exchange format for DCCs is a vital component of the digital transformation of the manufacturing industry, as it enables easy and efficient information exchange, ensures compliance with international standards, and provides secure and long-term storage of data. They are also more accurate, using authenticated, encrypted, and signed transmissions to ensure the reliability of calibration results. Figure 2: Schematic of element type definitions in the DCC [28; 38; 39].
## 3 Design and Implementation of the Proposed CBC Method
```
0: SelectedFeatures, Data, NumClusters, SamplingMethod, Classifiers
0: OverallPerformance
1: Load the selected features from the dataset
2: Scale and normalize the data
3: Encode categorical data (if any)
4: Scale and normalize the data
5: Split the data into training and testing sets
6: This step varies depending on the algorithm variant
7: Variant A: Convert data from 34 classes to 2 classes (attack and normal)
8: Variant B: Convert data from 34 classes to 8 classes (types of attacks)
9: Apply K-Means clustering with _NumClusters_ clusters
10:for each cluster \(C\) in clusters do
11: Extract data points belonging to cluster \(C\)
12: Apply _SamplingMethod_ to balance the data
13:for each classifier \(clf\) in Classifiers do
14: Train \(clf\) on the balanced data
15: Evaluate the performance of \(clf\) using metric \(P_{clf}\)
16:endfor
17:endfor
18: Aggregate the performance metrics for each cluster
19: Evaluate the overall performance using: \[OverallPerformance=\frac{1}{NumClusters}\sum_{i=1}^{NumClusters}OverallMetrics_{i}\]
10: Step 11: Calculate Accuracy
20: Calculate the accuracy of the model's predictions: \[Accuracy=\frac{TP+TN}{TP+TN+FP+FN}\]
11: Step 12: Report Performance Metrics
21: Report additional performance metrics:
22: Precision: \[Precision=\frac{TP}{TP+FP}\]
23: Detection Rate: \[DetectionRate=\frac{TP}{TP+FN}\]
24: F1 Score: \[F1\ Score=2\times\frac{Precision\times DetectionRate}{Precision+DetectionRate}\]
25: return OverallPerformance
```  
**Algorithm 1** Feature Selection, Data Preprocessing, Clustering, and Classification Algorithm  
A scatter plot showing the cluster mapping for a binary classification problem with two clusters is shown in Figure 4. Two primary components are used to visualize each data point. While the genuine classes vary slightly from the projected clusters in shape or color, the two clusters are represented by red and green colors. Areas of overlap or misclassifications in the feature space between the two classes are indicated by overlapping regions. Figure 3: Cluster mapping for 8-class classification using 2 clusters, showcasing the effectiveness of clustering in separating the classes. Figure 2: Cluster mapping for 2-class classification using 8 clusters, showcasing the effectiveness of clustering in separating the classes.
## 2 Related Work  
CWS is a basic NLP task, and research on it has a history of several decades, with three streams of methods, i.e., dictionary-based, statistical machine learning, and deep learning approaches [2]. Early CWS models were based on predetermined dictionaries, which are built based on manually created criteria. Although these methods can segment text simply and effectively, an obvious shortcoming is that predetermined domain dictionaries and rules do not always cover the new words. Moreover, these models necessitate substantial human resources and a robust background in geoscience.  
Since [PERSON] [25] formulated CWS as a sequence labeling task using statistical machine learning and obtained excellent performance, attention has been devoted to the study of statistical machine learning. Assigning a BEMS label to each character of a string, each to determine whether the character is the beginning (B), the middle (M), or the end (E) of a word or a separate word (S). Statistical machine learning methods utilize statistical machine learning models for the prediction of text segmentation, such as CRF and HMM [26; 27; 28]. However, these methods rely on huge feature engineering, and designing handcrafted features is challenging, labor-intensive, and time-consuming.  
Recently, researchers have been utilizing deep learning techniques for CWS and have achieved significant results [29; 30; 31]. In particular, with the development of pretrained language models, such as BERT [22], ZEN [32], and RoBERTa [33], several studies have performed CWS based on pretrained language models, and the performance of these methods almost reaches the human level [1; 34; 35; 36]. [PERSON] et al. [18] proposed a weakly supervised model, which combined predetermined dictionaries into sentences and fed them into a neural network model for training. [PERSON] et al. [17] proposed the self-learning word segmentation model with ontology support in the geological domain, which integrates domain knowledge and self-learning techniques to automatically create the domain training corpus without the need for human interaction. However, most of these methods rely heavily on predetermined knowledge and labeled data. Regardless of the fact that [PERSON] et al. [23] proposed a purely unsupervised method for domain-specific CWS with only unlabeled data, the absence of labeled data guidance restricts the accuracy of model learning and generalization. In addition, the ambiguity of complicated geological word boundaries is not taken into account by these models.  
Therefore, we propose a semi-supervised deep learning framework for CWS in the geoscience domain. In the proposed method, we focus on the existing problems in the geoscience domain CWS task and introduce a memory network module to learn the boundary information of pseudo-lexicon for delineating geological word boundaries. The self-training strategy is put in place to improve the generalization ability of the model with unlabeled data.
###### Abstract  
Bio-electrical impedance analysis (BIA) and dual-energy X-ray absorptiometry (DXA) are methods to estimate human body composition. This study aimed to compare sex-specific outcomes for estimating segmental and whole-body composition in 83 healthy participants (21.9 \(\pm\) 1.5 years, 56% men) using Inbody S10 BIA and Norland Elite DXA devices. One-way repeated measures ANOVAs showed significantly lower whole-body fat% and whole-body fat mass values alongside higher whole-body lean mass values resulting from BIA when compared to DXA (both sexes: \(p<0.001\)). In men, whole-body bone mineral content was significantly higher using BIA against DXA (\(p<0.001\)). Regardless of sex, no significant BIA versus DXA difference was found in arm fat mass (men: \(p=0.180\), women: \(p=0.233\)), whereas significantly lower leg fat mass values were found with BIA versus DXA (both sexes: \(p<0.001\)). Additionally, significantly higher arm lean mass (both sexes: \(p<0.001\)) and leg lean mass (only women: \(p<0.001\)) were found in BIA versus DXA. Moderate to very strong positive associations (\(p<0.05\)) between BIA and DXA outcome measures were found, except for arm fat mass (men: \(p=0.904\), women: \(p=0.130\)) and leg fat mass (only men: \(p=0.845\)). This study highlights (sex-dependent) differences in corresponding test outcomes between BIA and DXA both at the segmental and whole-body level.  
sub:http:; lean mass; fat mass; bone mineral content; body fat percentage; segmental analysis; Inbody S10; Norland Elite; arm; leg; upper and lower extremity
## 6 System Design and Operation Mode  
First, we placed the wireless IP cameras in strategic places to watch interesting zones. Then, we placed wireless IP sensors in some critical points with more risk. Both, sensors and cameras are under the coverage area of the access points.  
The mode of operation is as follows. All cameras have been recorded with the coordinates where they have to move and focus for each sensor placed in their visual coverage. The server has a database with the position of the sensors and the name of the cameras placed in the rural area close to every sensor (they are stored by name: sensor_1, sensor_2, etc.). When a sensor detects a fire, it sends an alarm directly to the server. This alarm message has the name of the sensor. When the server receives this message, it searches in its database the closest wireless cameras to that sensor and sends them a message with the name of the sensor that has sent the alarm and the position they must move to in order to watch the image of that zone. Finally, the cameras move their objective to the coordinates of the sensor. The camera will show what is happening in that zone and the fire fighter can corroborate if there is a fire or not.  
When there is an alarm, a firefighter sees the video streams from all the wireless IP cameras of the affected zone. On the other hand, the images could also be watched by other users when they request it. Figure 8 shows the flowchart with the mode operation from the point of view of the system.  
The system is highly scalable because a camera can cover as many sensors as positions can be recorded. On one hand, if it is placed at the top of a mountain, more area can be viewed by the camera. On the other hand, the database of the server can have many entries. There has to be one entry for each sensor. In our design plan we have considered that every sensor has to be seen by two wireless IP cameras at least. We have programmed all these instructions over http protocol to be easily implemented in other systems. Figure 9 shows the messages sent when there is an alarm.  
Figure 8: System Mode Operation Flowchart.  
Figure 9: Messages when there is an alarm.
# A Software-Defined Networking Framework to Provide Dynamic QoS Management in IEEE 802.11 Networks
## 3 Proposed SDN-Based Framework for Implementing DEDCA
### Detecting Requesting Terminals  
In our scenario, we considered two situations in which a normal terminal can become a _requesting terminal_. The first one considers that there is a sensor-based application associated with each camera that launches an Alarm when something unusual happens. In the second case, it is the security officer at the security center who detects a problematic situation and launches an Alarm. In both cases, the alarm messages, whose structure is _ALARM:0_, are encapsulated into UDP packets. The associated camera will improve its frame resolution. Consequently, a higher bandwidth is needed in order to be able to transmit a video of higher quality.  
In a SDN network, the well-known OpenFlow protocol [5] offers a direct communication between the controller and the network elements that allows the programming of the forwarding plane of the network elements. However, terminals and the controller are not directly accessible. In this SDN-based  
Figure 3: Schematic representation of the outdoor parking lot.  
proposed framework, we will use the fully programmable forwarding plane of the SDN application point to solve this limitation, as it is described next.  
The Alarm messages, which are received by the SDN 802.11 Access Point, have to reach the SDN controller, where the system intelligence is located. To enable this to be done, an adequate flow table entry is proactively inserted in the OpenFlow flow table of the access point (step 1 in Figure 4). Thus, the Alarm messages will match the corresponding entry of the flow table at the access point, and will be sent to the controller as _packet_in_ messages (step 2 in Figure 4).  
The SDN controller, which has a global knowledge of the network (among other parameters, the location of the cameras and their networking configuration), will be able to identify the _requesting station_ after receiving the _packet_in_ message and parsing the encapsulated packet.
# Application of Augmented Reality in Waterway Traffic Management Using Sparse Spatiotemporal Data
## 4 Methodology
### Multi-Source Data Fusion
#### 4.4.2 Multi-Source Data Timeline Synchronization  
Due to the inconsistent time intervals in the transmission of AIS data, we first need to synchronize the AIS data with the video data along the time axis to avoid insufficient fusion accuracy during the data fusion stage caused by these time discrepancies.  
At the initial stage of collecting video timestamps, we are actually collecting AIS data from both before and after the timestamp. Therefore, we first need to calculate the vessel's position at the video's starting timestamp and then perform interval sampling of the subsequent AIS data at consistent time intervals. The specific calculation formula is as follows:  
\[\mathrm{Position(t)=Position(t_{1})+\frac{(t-t_{1})}{(t_{2}-t_{1})}\times(Position (t_{2})-Position(t_{1}))} \tag{32}\]  
Here, \(t_{2}\) and \(t_{1}\) are two time points, with \(\mathrm{Position(t_{2})}\) and \(\mathrm{Position(t_{1})}\) representing the vessel's positions at these two time points, and \(t\) is the timestamp to be calculated.