esa-sceva/satcom-chunk-collection huggingface dataset:
# Integrating IP Mobility Management Protocols and MANET: A Survey
## 4 IP Mobility Management in MANET and Comparative Summary
### Reactive Gateway Discovery-Based Mobility Management Solutions  
Solutions under this category deploy a mobility management protocol over MANET and enable mobile nodes to take over the responsibility for discovering gateways. In order to discover gateways, nodes broadcast or unicast solicitation messages that can be answered or forwarded by intermediate nodes. In response, gateways send a reply message to the initiators to notify them of their existence. The main goal of this approach is to cut the overhead of proactive approach, however it comes with a cost of increasing the discovery delay. This section introduces five proposals in this category that use mobile IP. Table 2 summarizes the characteristics of those proposals. _QoS-based gateway selection in MANET with internet connectivity_[49] integrates MANET and the internet, using MIPv4 to support mobile nodes mobility between ad hoc networks. The AODV protocol was used for MANET routing. The proposed system focuses on the gateway selection process to enhance the feasibility of integrated MANET. The gateway selection procedure is based on three metrics; gateway load, path quality between MANET nodes and the gateway, and finally, the hop counts along the used path. The system is based on reactive discovery approach, where MANET nodes broadcast a solicitation message and gateways reply with the advertisement message in a unicast mode. The paper does not explain the handover procedure, however. It can be seen that the selection of gateway will take place only when a node connects to the internet for the first time. This would be the case especially when a gateway advertisement is unicast to the initiator of the gateway solicitation message. _Robust and flexible internet connectivity for mobile ad hoc networks_[50]: Figure 11 proposed a system for flexible and robust internet connectivity. The proposed system was designed to work over AODV routing protocol, however the authors claimed that the procedure used could be adopted to work with any MANET routing protocol with minor modification. Their proposed procedure is mainly based on extending the AODV route discovery mechanism to serve gateway discovery in addition to route setup. Mobile IPv4 was used to provide mobility management for mobile nodes. The mobile IP agent discovery task was eliminated from the implemented MIP, however. The functionality of agent discovery was assigned to the AODV and included within the route request mechanism. The gateway is responsible for checking destination locality using a prefix check or visitor list. The system, however, does not support intermediate node replies for internet destination discovery, as stated by the authors, and therefore nodes need to flood the network with route request messages.
# QoS-Aware Flexible Handover Management in Software-Defined Mobile Networks
## 4 Performance Evaluation
### Handover Signaling Cost
\begin{table}
\begin{tabular}{c l} \hline
**Parameter** & **Description** \\ \hline \(\lambda_{8}\) & Session generation rate of the CN \\ \hline E(s) & Average session length (in numbers of packets) \\ \hline P & The cell crossing rate \\ \hline E(T) & The average cell residence time \\ \hline d\({}_{a-b}\) & Hop distance between a and b \\ \hline L\({}_{\rm{Data}}\) & Data packet length \\ \hline B\({}_{w}\) & Bandwidth in wired link \\ \hline B\({}_{wl}\) & Bandwidth in wireless link \\ \hline \(\alpha\) & Latency of the wired link \\ \hline \(\beta\) & Latency of the wireless link \\ \hline T\({}_{L2}\) & L2 handoff time \\ \hline C\({}_{a-b}\) & Unit cost from a to b \((=d_{a-b}*(\frac{1}{B}+\eta))\) \\  & where \(L\) is message length and \(B\) is the bandwidth in wireless or wired link (\(\eta=\alpha\) or \(\beta\)) \\ \hline \end{tabular}
\end{table} 
Table 2: Parameters for total cost analytical model. \[ \begin{array}{l}\small\begin{array}{l}\small\begin{array}{l}\small{S}{\rm{C}}^{ \rm{prop}}_{\rm{class1}}=\frac{1}{L(T)}{\cdot}p{\cdot}({S}{C}_{\rm{F}U}+{S}{C}_{ \rm{L2}})=\frac{1}{L(T)}{\cdot}p{\cdot}({d}_{\rm{ASI}-Ctrl}{\cdot}(\frac{L_{ \rm{F}U}}{L_{\rm{F}U}}+\alpha)+({d}_{\rm{C}Ut-A{\rm{S}}2}+{d}_{\rm{C}Ut-C{\rm{S} }}){\cdot}(\frac{L_{\rm{F}U}}{L_{\rm{F}U}}+\alpha)+{d}_{\rm{MN}-A{\rm{S}}2}{ \cdot}(\frac{L_{\rm{F}U}}{L_{\rm{F}U}}+\beta)\\ {d}_{\rm{ASI}-Ctrl}{\cdot}(\frac{L_{\rm{F}U}}{L_{\rm{F}U}}+\alpha)+{d}_{\rm{C}Ut- A{\rm{S}}2}{\cdot}(\frac{L_{\rm{F}U}}{L_{\rm{F}U}}+\alpha)+{d}_{\rm{ASI}-A{\rm{N}} 2}{\cdot}(\frac{L_{\rm{F}U}}{L_{\rm{F}U}}+\beta)\end{array}\end{array}\end{array}  \tag{4}\]
## 4 Characterization of the Fiber Compound-Ring Lasers
### Fiber Laser Power Tunability and its Switchable Dual-Output Port Operation
Figure 6 illustrates the output power from both output ports, OUT1 (circles) and OUT2 (crosses) as a function of the reflectivity of the SLM\({}_{1}\). Note that both output ports behave similarly and the 3-dB bandwidth of the light beam from OUT1 (filled triangles increased as the reflectivity of the Sagnac loop mirror, SLM1, increased while the output power decreased as shown in Figure 6, due to the strong feedback (i.e., reflected light beam) from each SLMs. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**SOA\({}_{1}\)**\(I_{B1}\) (mA)** & **SOA\({}_{2}\)**\(I_{B2}\) (mA)** & **P\({}_{\text{OUT1}}\) (dBm)** & **P\({}_{\text{OUT2}}\) (dBm)** \\ \hline
26 & 180 & \(-\)36 & \(-\)1.5 \\
50 & 200 & \(-\)32 & 5 \\
75 & 250 & \(-\)29.5 & 7.8 \\
100 & 300 & \(-\)28.9 & 9.3 \\
150 & 400 & \(-\)28.6 & 11.1 \\
200 & 500 & \(-\)28.9 & 11.85 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Optical power from the fiber laser output ports OUT1 and OUT2, at different bias current \(I_{B}\) (mA) levels and a 1550-nm center wavelength. In a dual-output port configuration, both output ports can be fixed and also adjusted to any output power between +11.9 and \(-\)28.9 dBm. We firstly set both output ports, OUT1 and OUT2, to +8.94 and +8.95 dBm by adjusting the reflectivity of both Sagnac loop mirrors, SLM\({}_{1}\) and SLM\({}_{2}\) at \(\leq\)0.1%, as shown in Figure 7. Then, we gradually tuned the output power from the output port, OUT1, from +8.94 to \(-\)28.9 dBm by adjusting the reflectivity of the SLM\({}_{1}\) from 0.1% to more than 99.9% while optimizing the reflectivity of the SLM\({}_{2}\) in order to keep the output power at OUT2 constant at +8.95 dBm. The reflectivity of the SLM\({}_{2}\) was around 50% when the one of the SLM\({}_{1}\) was around 99.9% in order to maintain the output power at OUT2 constant at +8.95 dBm.
## 4 Control, Localization, and Communication Strategy  
The control scheme of the robot is illustrated in Figure 5. The DSP (Digital Signal Processor)-based electronic control unit manages the information from the receiver of the tele-manipulation to generate the correct references for the power drivers of the propeller and the hydroplane. The selected motors for the propulsion are indicated in the section devoted to the propulsion design, while each hydroplane is actuated with a Direct Current (DC) electric motor supplied at 48 V and coupled with a 9.3:1 reducer for a total power of 960 W, a nominal speed of 400 rpm and a nominal torque of 68.7 Nm. The motors and the electronic boards are supplied by the battery pack located in the hulls.  
The localization of the victim is performed by combining the information coming from a camera located in the front of the robot, a temperature sensor providing a temperature map of the robot surroundings, and an infrared sensor. The real-time data of these devices are processed onboard, compressed, and transmitted to the remote robot operator who drives the propellers, the rudders, and the orientation of the camera accordingly.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline  & **a** & **b** & **c** & **d** & **e** & **f** \\ \hline Overall dimensions [m] & 2355 \(\times\) 1505 & 2340 \(\times\) 1534 & 2284 \(\times\) 1739 & 2100 \(\times\) 1112 & 2332 \(\times\) 1475 & 2490 \(\times\) 1400 \\ Unladen mass [kg] & 52 & 39 & 45 & 43 & 51 & 54 \\ Required engine power & 1.1 & 1.21 & 1.85 & 1.36 & 1.02 & 1.14 \\ Number of actuators & 4\(p\) + 2\(m\) & 2\(p\) + 2\(p\) + 2\(m\) + 2\(h\) & 2\(p\) + 2\(m\) & 2\(m\) + 4\(v\) & 4\(yr\) + 2\(pm\) & 2\(h\) + 2\(m\) \\ Longitudinal moment & 84.34 & 51.82 & 32.85 & 18.13 & 57.25 & 23.36 \\ of inertia [kg m\({}^{2}\)] & 78.57 & 36.78 & 33.56 & 15.67 & 112.21 & 58.5 \\ Maximum carrying capacity [kg] & 142 & 81 & 75 & 75 & 75 & 104 \\ Shots & inf. & inf. & inf. & inf. & inf. & inf. \\ Repeatability & inf. & inf. & limited & inf. & inf. & inf. \\ \hline \end{tabular}
\end{table}
Table 1: Trade-off analysis. \(p\): pump, \(m\): motor, \(v\): valve, \(h\): hydroplane, \(a\): actuator, \(pr\): vertical propeller.  
The communication between the remote operator and the robot is performed by exploiting the buoyant communication system illustrated in Figure 6. The information coming from sensors located on the robot are compressed on board and sent through a data cable (2) from a transceiver (1) to an antenna (4) located on a buoy (3). A second antenna is close to the operator and receives the data that are processed by a remote transceiver (5) and sent to the operator (6). The acquired data are used by the operator to drive the robot in the approach, rescue, and return phases. Since the robot is designed to work at a maximum depth of 1.5 m from the water surface, the buoyant communication solution can be considered simple and highly reliable. Nevertheless, other possibilities presented in the literature can be investigated, like active and passive sonar communication systems and direct underwater communication [25; 26; 27; 28].
## 4 Experiments
### Analysis and Discussion  
#### 4.5.1 Ablation Study  
To better understanding the effectiveness of our methods, we conducted an ablation study with our proposed methods on the OPUS-100 dataset, as shown in Table 5. In the first three rows, we analysed the impact of SOBT with different selection criteria. The first row shows zero-shot translation results based on ROBT. From (1) to (3), SOBT with both the batch-level selection criterion (BLS) and the global-level selection criterion (GLS) brings a significant improvement of 3.28 BLEU. We attribute this improvement to SOBT generating more suitable training examples for zero-shot translation than ROBT. From (2) to (3), GLS can deliver a decent performance gain of 1.36 BLEU with proper data sampling strategies. In the last three rows, we study the advantages of CLOS with hard substitution (HS) and soft substitution (SS), respectively. As detailed in Section 3.5, we only use CLOS methods in the substitution fine-tuning stage based on a better initialization obtained with SOBT. We observed that CLOS can provide improvements up to 1.92 BLEU with soft substitution from (3) to (5). Hard substitution performs worse compared to soft substitution due to its discrete nature. The result of (6) confirms our claim before: without ROBT, the BLEU score of CLOS severely degrades from 15.80 to 3.38 with a poor initialization for zero-shot directions.
# Inertial Sensors-Based Assessment of Human Breathing Pattern: A Systematic Literature Review
## 3 Data Analysis, Results, and Discussion
### How Reliable Are IMUs at Identifying Signals Associated with Adult Human BPs?  
Inertial sensors proved to be capable of detecting breath rates, apnea events, respiratory frequency, and amplitude, even in different body positions and conditions. Additionally, accelerometer data showed strong correlations with other reference devices, such as spirometers and pressure sensors, enhancing signal event reliability. These findings suggest that inertial sensors can provide reliable measurements of breathing-related signal events, making them valuable tools for respiratory assessment in various scenarios.
# Geometric Accuracy Evaluation Method for Subway Stations Based on 3D Laser Scanning
## 3 Research Method
### The Coordinate Registration of the Point Cloud and BIM  
If the survey coordinate system predefined in BIM is not used during the point cloud measurement, it is necessary to register the point cloud with BIM. The BIM grid, as the construction release benchmark, is the main framework of architectural mapping and is closely related to the positioning of the building structure. A laser scanner uses the survey coordinate system in the process of measurement, and it can obtain the precise coordinates of the building structure under the survey coordinate system during operation [51]. Since the scanner itself includes a gyroscope to achieve vertical orientation of the acquired data, the registration of the measured laser point cloud with the BIM can be decomposed into XOY plane coordinate registration and \(Z\)-axis coordinate registration. Thus, the coarse-to-fine registration method proposed in this study consists of two steps: coarse registration based on the BIM grid and fine registration based on the inner wall lines.
# Quantum-Secure Coherent Optical Networking for Advanced Infrastructures in Industry 4.0
## 7 Discussion
### Directions for Future Research  
The layered framework described herein sets a trajectory for next-stage research. Incorporating machine learning models trained to detect minute fluctuations in phase masks or overhead fields could proactively signal ongoing attacks. The authors continue the previous work in [21], whose authors proposed machine learning for detecting anomalies in SDN-controlled networks. New QKD protocols intended for dynamic operation with reconfigurable optical add-drop multiplexers would increase distributed large-scale network security through network rerouting and maintenance periods. Further quantitative benchmarks are needed on how multi-homodyne coherent detection scales when integrated with non-quantum safe encryption layers [41]. Our results hint at minimal latency penalties, but large-scale, multi-hop validations under real traffic patterns remain crucial. Building on the notion that vulnerabilities at overhead fields can cascade into deeper layers, an automated cross-layer defense mechanism, where anomalies in overhead bytes trigger immediate re-keying or path reconfiguration, could block sophisticated time-based exploits before they spread.
## 3 On-Site Monitoring Program
### Component Arrangements  
According to existing reports [30; 31; 32], the measurement of stress or strain at the steel arch was mostly conducted by arranging steel stress gauges or strain gauges parallel to the inner and outer flange sides of the steel arch. Thereafter, the test results were converted into axial force or bending moment of the steel arch, and then the internal force of the steel arch could be analyzed. There were few experimental results related to the force or deformation investigation in any abdominal region of the steel arch. If the strain gauges are arranged on inner and outer flanges of the steel arch in parallel, the changing trend of the contact pressure between the liners cannot be reflected sensitively. The strain gauge arrangement on the web axis of the steel arch can not only reflect the deformation of the steel arch effectively, but also indirectly reflect the changing trend of the contact pressure between the liners.  
A fixing device was utilized to weld the strain gauge to the center of web along the axis of steel arch, and a protective shell was welded to steel arch to ensure that the strain gauge would not be damaged during the shotcrete process, as shown in Figure 4. The stress gauge was welded to the reinforcement of secondary lining in the direction of tunnel circumference to measure the main circumferential axial force of reinforcement. The strain gauges were fixed to the reinforcement of secondary lining by using ties at both ends to measure the strain of the second liner concrete. The vault settlement was measured by a micrometer with a high precision. The tunnel perimeter convergence was measured by a digital convergence meter.
# Kinematic Modelling and Experimental Validation of a Foldable Pneumatic Soft Manipulator
## 2 Kinematic Modelling of the Foldable Pneumatic Soft Manipulator  
### Design Schematic  
The foldable pneumatic soft manipulator is designed to have the foldability in its volume. When it is evacuated, the soft manipulator is in a contraction state, which can greatly save the storage space. When it is inflated, the soft manipulator can switch to an expanded state, which can result in a large workspace. This kind of foldability is realized by a structure composed of several foldable pneumatic modules which are all standardized and identical. The design of a foldable pneumatic module is shown in Figure 1.  
Figure 1: _Cont_.  
In Figure 1, it is seen that the foldable pneumatic module is composed of a pneumatic actuator, an inflatable straight arm, as well as the connection plate between the two parts. The pneumatic actuator is made up of three parallel and symmetric inflatable bellows, which are connected by a series of rigid constraint plates. The straight arm adopts a two-level structure, the inner level and the outer level. The inner level is a bellows made of silicone rubber, which is flexible and is responsible for the sealing. The outer level is a cylinder beam made of nylon fabric, which limits the lateral and longitudinal expansion of the inner silicone rubber. In Figure 1, the air-driven principle of the soft module is shown. The pressured air is inputted into the inflatable bellows and the inflatable straight arm independently. The pneumatic actuator can make the directional motion when the air pressure is different in the three bellows. The inflatable straight arm can switch between a compact state and an expanded state when the pressured air is evacuated or filled. In Figure 1, the expanded state of the soft module is shown. The total length of the inflatable straight arm is limited by the outer level fabric. The directional movement of the pneumatic actuator is the bending accumulation of a large number of basic inflatable units. By controlling the movement of the pneumatic actuator, the motion control of the soft manipulator can be realized.
# LiDAR-Based Dense Pedestrian Detection and Tracking
## 2 Methodology  
### Overall Framework  
The framework of the proposed dense pedestrian detection and tracking method is shown in Figure 1, which mainly contains the following modules: ground elimination, clustering, fine segmentation, pedestrian detection, and pedestrian tracking. The ground elimination is the process of removing point clouds from ground. Random Sample Consensus (RANSAC) algorithm [32] can be used to fit the ground plane for ground point clouds elimination. After filtering out the ground points, the remaining point clouds can be clustered to separate pedestrians from each other. It is common to obtain undersegmented clustering results using existing clustering methods such as the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm [33] and the Radially Bounded Nearest Neighbor (RBNN) algorithm [34] when pedestrians gather together and their point clouds are close to each other.  
In this section, kernel density estimation is used for the fine segmentation of dense pedestrians based on RBNN. Subsequently, preprocessing is performed to remove high walls and large buildings, and then the extracted features related to the posture and figure of the human body are used for pedestrian detection. According to the detection results and the geometric relationship between pedestrians, the tracking strategy of the occlusion area is designed to improve the tracking performance. The specific implementation of fine segmentation, dense pedestrian detection, and dense pedestrian tracking are introduced in Sections 2.2-2.4.
## 4 Experiments and Analysis
### Main Results  
This section presents the main experimental findings, as illustrated in Table 4 and Figure 9. Drawing on the data displayed in the table, the following conclusions can be made:  
Under the \(F1\) metric, our proposed BiLSTM-BGAT-GCN model achieves scores of 73.36%, 58.05%, 58.61%, and 69.14% on the \(D_{1}\) datasets, respectively. For the \(D_{2}\) datasets, the performance results are 71.55%, 60.83%, 58.81%, and 66.18%, respectively. A careful review of Table 3 clearly demonstrates that our BiLSTM-BGAT-GCN model outperforms all existing pipeline models, including Peng-two-stage + IOG, IMN + IOG, CMLA, RINAITE, Li-unified-R, and Peng-two-stage. Moreover, it surpasses all MRC-based methods, such as Dual-MRC and BMRC. Further comparison with end-to-end methods reveals that our model also exhibits a superior performance, outdoing GTS-CNN, GTS-BiLSTM, OTE-MTL, and JET-BERT. Not  
Figure 9: The results on \(D1\) and \(D2\) datasets. only that, but in comparison with the best baseline model EMC-GCN, our BiLSTM-BGAT-GCN model also shows significant progress. On the \(D_{1}\) dataset, it secures improvements of 1.65% (73.36-71.71%), 1.25% (58.05-56.80%), (58.61-58.61%), and 0.45% (69.14-68.69%) in \(F1\) scores across the four sub-datasets. On the \(D_{2}\) dataset, the performances of the BiLSTM-BGAT-GCN model are only slightly below the EMC-GCN on \(F1\) in Restaurant 15. Furthermore, the remaining performances of the BiLSTM-BGAT-GCN model in Restaurant14, Laptop14, and Restaurant16 also achieves 1.78% (71.55-69.77%), 5.23% (60.83-55.60%), 0.08% (66.18-66.10%) increases, respectively. In conclusion, as demonstrated by the results, our proposed BiLSTM-BGAT-GCN model has proven to effectively perform the ASTE task. To provide a more intuitive demonstration of the effectiveness of our improvements, we compared the two following models, as illustrated in Table 5 and Figure 10, where BGAT-GCN represents the model without the improved biaffine attention mechanism. The experimental results show that, by introducing bidirectional long short-term memory to improve the biaffine attention mechanism, the model achieved significant improvements in three key indicators: precision (\(P\)), recall (\(R\)), and \(F1\) score. This improvement measure not only effectively enhances the overall performance of the model, but also further proves the effectiveness and practicality of this improvement measure in model optimization.
# Distributed Information Compression for Target Tracking in Cluster-Based Wireless Sensor Networks
## 5 Simulation
### Noise  
Because of the limited capability of a sensor and a extremely small sample size for particle filtering, the fundamental estimation problems are to estimate the distance between the reference sensors and the target and to determine the property of the signal (e.g., the signal arrival angle). In a nutshell, accurate tracking is highly related to precise TOA/AOA measurements and the signal processing capability. In this set of experiments, we explore the impact of measurement noise on system performance. With the variance of angle estimation \(\sigma_{\theta}=1\) and with varying the uncertainty of distance estimation \(\sigma_{d}\), as shown in Figure 23, the increment of the measurement noise leads to a larger  
Figure 21: Impact of the sampling points on the average data size.  
Figure 22: Impact of the sampling points on the average energy consumption.  
error distance of TCAT and CTCI. Observe that with an increasing measurement uncertainty \(\sigma_{d}\), the performance of the proposed CTCI method tends to be close to the CRLB.