esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiments
### Case Study  
To showcase validate the effectiveness of our method, we selected two AVI vehicle trips and applied the proposed method to reconstruct their trajectories in space and time, as shown in Figures 6 and 7. Figure 6 illustrates the process of the spatial-temporal reconstruction of an AVI trajectory in a simple road scenario. The vehicle travels from AVI collection point 1 to collection point 8 on the Inner Ring Expressway in Chongqing. Figure 5(a) displays the original AVI trajectory, consisting of eight AVI records. After spatial reconstruction, the spatial trajectory is as shown in Figure 5(b), where a definite spatial trajectory exists between each pair of points. In the case of a simple road scenario like the Inner Ring Expressway, there is almost no path selection issue between adjacent AVI collection points. Finally, based on the known spatial trajectory, fixed sampling points are placed on the road, and the model predicts the timestamps at these sampling points, resulting in the determination of the spatiotemporal trajectory, as shown in Figure 5(c).  
Figure 7 presents the process of AVI trajectory reconstruction in a complex urban road scenario. Similar to Figure 6, Figure 7 represents the original AVI trajectory, the reconstructed AVI spatial trajectory, and the reconstructed AVI spatiotemporal trajectory, respectively. However, unlike the simple road scenario, in Figure 6(b), there is a path selection issue between the spatial trajectories of AVI record 1 and AVI record 2, where the selected path is depicted in black and the unselected paths are shown in grey. Similarly, there is a path selection issue between records 4 and 5. The occurrence of such path selection issues increases as the number of sparsely deployed AVI collection points in the city increases.
## 3 Methodology
### Regression Analysis  
To estimate the glucose level, regression analysis was performed on the PPG signals acquired through a smartphone camera and their references. A different regression model was trained on raw signals, preprocessed signals, and the signal features that were extracted from the preprocessed signals to evaluate and compare the performance in noninvasive quantitative estimation.  
Regression techniques such as CLS (Classical Least Squares), PCA (Principal Component Analysis), PCR and PLS have been studied in the past for quantitative analysis. Both PLS and PCR have widely been used in chemometrics as multivariate calibration methods, and these methods can be applied when that dataset has correlated predictor variables [40]. Both regression methods create new predictor variables (components) as linear combinations of the original predictor variables. PLS creates these components while considering the observed response values. On the other hand, PCR creates components without considering the response values at all. Both regression methods have reliable predictive power. PCR was chosen for this work due to its reported ability to predict glucose levels from NIR (Near Infrared) spectra with 94-95% accuracy [20].  
Additionally, models were trained using SVR and RFR. SVR is a modification to the commonly used classification algorithm called Support Vector Machine (SVM). While SVM is primarily used in classification problems with discrete finite labels, SVR can be used to predict continuous variables such as blood glucose level. In SVR, instead of trying to minimize the error outright as with traditional regression techniques, hyperplanes are constructed to fit the error observed within a small threshold of \(\varepsilon\) (epsilon). Previous studies have used support vector-based techniques on PPG data to predict other continuous biological variables such as blood pressure estimation [43].  
Finally, ensemble learning was used in the form of RFR--a technique based on using several decision trees to predict a continuous value. Classical Decision Tree Regression (DTR) involves gradually developing a decision tree from smaller subsets of the training data and can work reasonably well in mapping nonlinear functions. Outputs from several decision trees are averaged together to provide an ensemble estimate of the predicted value. Other studies have demonstrated the efficacy of RFR in the prediction of biological variables from ECG or PPG data, often outperforming other regression techniques [44].  
The following sections discuss the experiment performed on each of these learning methods in detail. For the purpose of clarity in discussion, we refer to PLS and PCR as statistical learning, and refer to SVR and RFR as machine learning, in order to reflect the commonly used terminology for each technique.
## 4 Result and Discussion
### Results for PDR with Random UAV Movement  
In this experiment, the same UAV-GS system is used, but the movement of the UAV is changed to random, as shown in Figure 8. Experiments were conducted to study the QoS using the BRFFNN and LSGB models for bandwidth allocation.  
In scenarios involving random movement, the BRFFNN and LSGB models achieved an average PDR of 95.15% and 95.13%, respectively. These results indicate that the BRFFNN model slightly outperforms the LSGB model in maintaining higher packet delivery rates, showcasing its robustness in more dynamic conditions.
# Heterogeneously Integrated Multicore Fibers for Smart Oilfield Applications
## 2 A Case Study  
To make the design process more targeted, the proposed heterogeneously integrated multicore fiber in this paper is based on the demand of a company named Beijing Perception Technology Company [26]. Figure 1 shows the schematic of the smart oilfields composed of oil production area, data distribution area, and office area.  
The company is in urgent need of multifunctional fiber to support the construction of smart oilfields. In the production area, simultaneous distributed intrusion detection and temperature monitoring of pipelines are needed in order to achieve real-time alarm on excavation, theft, leakage, and other potential threats. This could be accomplished by implementing a Raman optical time-domain reflectometry (ROTDR) and phase-sensitive optical time-domain reflectometry (\(\varphi\)-OTDR) hybrid sensing system, where the ROTDR is used to monitor the temperature change and \(\varphi\)-OTDR is used for real-time vibration detection [18]. As for data interaction, the data throughput of the oilfield is approximately 1 Tbits per day, and it might keep increasing at a high speed in the near future. Therefore, we propose a three-mode six-polarization core for digital transmission and set the highest baud rate as 25 G with a quadrature phase-shift keying (QPSK) modulation format, which could obtain 0.3 Tbit/s and enough would be available for the situation, even for an instantaneous sharp increase of data. To realize the real-time transmission, a big challenge is the DSP complexity due to the limitation of the processing capacity of field programmable gate arrays (FPGAs). Although some companies, such as XLIINX and Altera [27; 28], have made a breakthrough in high-performance FPGAs, these products are not cost-effective and cannot be widely used at present. We set the filter taps as 32, which means a common FPGA could meet the demand. Moreover, the oilfields are mostly located in remote areas with weak signals. In this sense, it is essential to transmit analog signals to build a temporary network for connecting the devices and correspondence. Compared with long-haul transmission, fibers in smart oilfields might be used for short-distance transmission, so the maximum transmission distance is set as 50 km.
# A Lightweight and High-Accuracy Model for Pavement Crack Segmentation
## 4 Results
### Limitations  
Our model demonstrates excellent segmentation performance and application potential on the Crack500 and AsphaltCrack300 datasets, but there are still certain limitations. First, although the analysis of the comparative experimental results in this paper thoroughly discusses specific conditions in the datasets, such as road wear, stain interference, fine cracks, and strong light interference, there are differences between the actual application scenarios and the samples in the datasets (e.g., extreme weather, different paving materials). These differences may lead to a decrease in segmentation accuracy in certain situations, posing challenges for generalization. Future research will focus on expanding the dataset types and improving the robustness and generalization capability of the model.  
Second, the current experiments have only been conducted on a computer platform, and the model has not yet been optimized using tools such as TensorRT and ONNX. Future work will focus on deploying the model to mobile platforms or handheld devices to enable intelligent road crack detection.  
Finally, the current research is mainly focused on static image crack detection. To further enhance the application capability of the model, future research will explore the combination of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), to perform time-series analysis and crack segmentation in more complex road scenarios. This will help improve the accuracy and adaptability of the model in dynamic environments.
## 2 Related Works  
The IoT has grown significantly in the industrial era 4.0. The utilization of the IoT has tremendous market potential, with an economic value of 5.5 trillion to 12.6 trillion dollars in 2030 [24]. In addition, IoT is also widely used for lifestyle fulfillment, process efficiency, and health. Privacy of data transmitted via IoT is of particular concern and handling [25]. Information security in transmission is an important aspect that must be considered.  
MQTT is an IoT communication protocol that plays an important role in sending messages from publishers to subscribers. MQTT security can be done at the network, transport, and application levels. Research that has been done related to data security in the MQTT protocol at the application layer, among others, is the study of [PERSON] et al. [16] about secure communication models for IoT and MQTT. This research uses a symmetric key algorithm, namely PRESENT, LBLOK, AES-128, Trivium, and GRAIN. The use of symmetric keys, especially LWC in this study, is due to the consideration of limited memory usage on embedded devices [16].  
A similar study was conducted by [PERSON] [17], regarding the end-to-end security scheme of the MQTT protocol using the ChaCha20-Poly1305 algorithm with AEAD mode. The choice of this algorithm is due to the limitations of devices that do not support the use of TLS at the transport layer. ChaCha20-Poly1305 is a popular lightweight stream cipher for devices with limited memory [17].  
Research related to end-to-end encryption on MQTT was also carried out by [PERSON] et al. [18], who proposed an IoT security design in the form of Joining Encryption and Delegation for IoT (JEDI). This scheme allows for end-to-end encryption with a key delegation system on each principal. Another feature that JEDI has is that it allows the encryption to be implemented in a decentralized key system, with decoupled communication and a tiered encryption system using the identity-based encryption algorithm with wildcard key derivation (WKD-IBE) [18].  
Our research proposes a secure end-to-end encryption design for IoT communication protocols, especially MQTT, using a secret sharing scheme and a block cipher-based lightweight cryptography algorithm, i.e., AES-128 GCM mode, GIFT-COFB, ROMULUS N, and Tiny JAMBU. The secret sharing scheme used is secret sharing with the XOR model proposed by [PERSON] et al. [19]. Table 1 describes the comparison of our research with previous research.
## 4 Simulation Results and Analysis
### Analysis of Average False Detection Rate  
The average false detection rate is defined as the ratio of the average number of non-interference frequencies detected per detection run, while correctly detecting all interference frequencies, to the total number of frequencies in the signal. That is  
\[\text{The average false detection rate}=b/2048 \tag{6}\]  
where \(b\) represents the average number of non-interference frequencies detected per detection run.  
The lower the average false detection rate, the better the algorithm's performance, and vice versa.  
Figure 6: Detection probability of partial-band interference.
## 5 Experiments
### Impact of the Number of Resources
#### 5.2.2 Impact of Edge Resources  
Figure 11a-d are comparing different resources in (6, 1, 1), (6, 2, 1), (6, 3, 1), (6, 4, 1) settings, respectively. From the figure, it can be seen that when there are fewer edge servers, the TGHR algorithm shows its unique advantage of better convergence of the solution set of the Pareto front to find the closest solution to the extreme value. The TG algorithm and HR algorithm in Figure 11a may fail to complete convergence in 200 generations of evolution due to the inability to find enough non-dominated solutions in the early stage of evolution.  
The box plot Figure 12 shows that as the TGHR algorithm has stable performance. As the edge server resources increase, the tasks are assigned more on the edge server with higher computational efficiency, so (6, 2, 1) has lower minimum time than (6, 1, 1). However, when the edge resources become abundant and all computational tasks will be offloaded to the edge servers, the minimum time is no longer reduced. As the edge resources increase, the proportion of edge genes in the initial population of the TGHR algorithm increases, and therefore, it no longer has the advantage of low-cost. This can show that the TGHR algorithm is less affected by the change of resource allocation and can perform more stable performance.  
Figure 11: The Pareto frontier in different edge resources setting. (**a**), (**b**), (**c**), (**d**) are comparing different resources in (6, 1, 1), (6, 2, 1), (6, 3, 1), (6, 4, 1) settings, respectively.
In comparison to the work conducted in [34; 35], in which only the VC-CMV mode with a square flow pattern was simulated, in the present work, we show that simulations of different ventilation modes, i.e., the VC-CMV mode and PC-CMV mode, with different flow and pressure patterns, respectively, can be obtained using the proposed numerical codes. In [3], MathWorks(r) Simscape foundational blocks were used to create a model in order to evaluate the sharing of a ventilator between two patients (co-venting). However, in contrast to our models, the system connecting the ventilator to the two patients and their lungs was simulated as an electrical circuit. Thus, the tubes were simulated as resistors, the lung and chest wall as a capacitor, the pressure source as a voltage source, a one-way valve as a diode, and an open/close valve as a switch. Furthermore, only the PC-CMV mode with constant square pressure pattern was simulated. In [36], the electromechanical lung simulator xPULM, a respiratory simulation system presented in [37], was used to test the interaction between a patient and a ventilator. The simulator acted as a ventilated patient and replicated spontaneous sinusoidal breathing, while supported by different modes of assisted ventilation. In addition, flow simulation produced by two proposed designs of low-cost mechanical ventilators was investigated in [38] during the VC-CMV and PC-CMV modes. In this work, the Simulink model is composed of blocks from the Simscape Fluids (gas) library [39; 40], which allowed us to simulate the pneumatic circuit and all the components of the ventilator. Conversely, the patient's lungs and the trachea are reproduced in the model by setting the lung compliance and the resistance of the respiratory system, respectively. In this way, the fundamental parameters and variables of the mechanical ventilation can easily be calculated and used by doctors to evaluate how the mechanical ventilator responds to different patients. Furthermore, the model can also be used by start-ups and manufacturers, both to accelerate the production of new mechanical ventilators and to predict their performance.
# CT-RPL: Cluster Tree Based Routing Protocol to Maximize the Lifetime of Internet of Things
## 3 Cluster Tree-Based Routing Protocol
### Route Establishment  
In CT-RPL, the route is established based on the standard RPL protocol. It uses queue utilization (\(QU\)), expected transmission count (\(ETX\)), residual energy (\(RER\)), and the cluster with the highest node count to pick the optimum \(CH\) for effective data transfer. Thus, CT-RPL deals with the hotspot problem effectively to a certain extent, while adding metrics to balance the load among the \(CH\) nodes in the network. In general, the DODAG root sends the DIOC message frequently to nearby CH nodes. The participant \(CH\) node receives the parent information, such as the \(ETX\) and \(QU\), through the DIOC control message. The participant \(CH\) sends the DAOC to its parent. The DODAG root sends the DODAG Advertisement Object Cluster-Acknowledgement (DAOC-ACK) message to the participant \(CH\) node. Once the route is created from the participant to the root node, the \(CH\) receives its \(CM\) data and forwards it to their respective parent \(CH\).  
While establishing the route, routing metrics, objective function, and \(CH\) ranking calculations are crucial to assess the performance of the selection. These parameters are defined in the following subsection.
# Constant Force Control of Centrifugal Pump Housing Robot Grinding Based on Pneumatic Servo System
## 2 Analysis of the Grinding Force in the Inner Circle of the Centrifugal Pump Shell  
The grinding force generated in the grinding and polishing process can usually be decomposed into three forces perpendicular to each other, namely the tangential grinding force \(F_{t}\) along the tangential direction of the grinding wheel, the normal grinding force \(F_{n}\) along the radial direction of the grinding wheel, and the axial friction \(F_{a}\) along the axial direction of the grinding wheel. The grinding force generated during internal grinding and polishing is shown in Figure 1.  
In the process of grinding and polishing in the inner circle of the centrifugal pump shell, the axial grinding force \(F_{a}\) is small and can be ignored. Since the abrasive grains on the grinding wheel have a large negative rake angle, the normal grinding force \(F_{n}\) is greater  
Figure 1: Force analysis diagram of internal grinding.  
than the tangential grinding force \(F_{t}\). Generally, \(\frac{F_{t}}{F_{t}}\) is in the range of \(1.5\sim 3\) (referred to as the grinding ratio). Therefore, controlling the normal grinding force in the inner circle is equivalent to controlling the grinding force in the grinding and polishing process of the centrifugal pump shell.  
The empirical formula of the tangential grinding force is [33]:  
\[F_{t}=169f_{r}^{0.62}f_{av}^{0.44}v_{w}^{0.69} \tag{1}\]  
where \(f_{r}\) is the grinding (radial) depth, mm; \(f_{av}\) is the axial feed speed, m/min; and \(v_{w}\) is the linear speed of workpiece rotation, m/min.  
According to the grinding conditions, the tangential grinding force was calculated from Equation (1) and then the normal grinding force was calculated according to the grinding ratio, which can be used as the expected input force for contact force control in the grinding process.
# Communication Transport Protocol Strategies for Rail Applications
## 5 ACS Emulator
### ACS Emulator Software  
We designed and implemented the software that manages the gateways (whose architecture scheme is represented in Figure 9), which has been written to adopt the SIP protocol (a SIP proxy/dedicated server plays this role) and to manage the tunnels via a component named Tunnel Manager (TM). The ACS TM relies on Linux Tunnel Addressing Protocol (TUN/TAP) module [17] to create virtual network devices used to represent the endpoint interfaces of multi-path tunnels. Such software also relies on existing Linux third party software applications such as dnsmq [18] for supporting DHCP and DNS protocols.  
On each ACS gateway a Tunnel Manager executes, along with a SIP server (which have been integrated on the same application), a DNS server and a DHCP server (implemented by dnsmq). The Tunnel Manager is responsible for handling the tunnel multi-path based on different transport protocols (such as UDP and TCP). Each tunnel is represented via a virtual network device driver created using the TUN/TAP Linux module. IP datagrams transmitted by a gateway through a tunnel interface are handled by the Tunnel Manager. This component receives IP datagrams sent or forwarded by the Linux Kernel to the tunnel interfaces (in outbound), envelopes them in transport protocol messages (or segments) with an additional header, and finally sends such application messages to each bearer interface of the tunnel, thus implementing a multi-path. Further optimizations can be included such as those implementing reinforcement learning [19].  
Figure 9: ACS GW simulator software architecture scheme.  
The Tunnel Manager running on the receiving gateway receives the transport messages containing the inner IP datagrams from each bearer interface. It discards any duplicates and extracts and announces the original IP datagram to the Kernel through the local tunnel interface (handled by TUN/TAP module). The Kernel protocol stack layer, in turn, decides the final destination of the datagram. An example of such flow is represented in Figure 10. This implementation hides any bearer redundancy complexity, offering a simple outbound gateway from/to on-board/network side of ACS.
# Global Resource Scheduling for Distributed Edge Computing
## 5 Experimental Analysis
The changing trend of the interval distance for each curve in Figure 9 indicates that the convergence time of the four algorithms is the same when the node size is small. However, with an increasing number of nodes, the advantages of SMACA become more apparent, making it more suitable for large-scale datasets. The improvement in pheromone updating rules significantly expands the difference in pheromone updating amounts on paths with different costs. This makes it easier to choose paths with low costs, greatly optimizing the convergence speed of the algorithm. In the throughput experiment, we recorded the total number of task scheduling executions within the same time interval (one minute) (the ratio of scheduled tasks to the total number of tasks). As the execution results of heuristic algorithms such as ACO exhibit a certain degree of randomness, this study repeated the same set of experiments 20 times. The boxplot tool was used for statistical analysis of the experimental data. Figure 10 shows the results of the throughput experiment. As shown in Figure 10, among the four compared algorithms, the proposed SMACA algorithm exhibits superior throughput. This is primarily attributed to the algorithm in this paper optimizing the ACO parameters through PSO, leading to a higher scheduling success rate and fewer iterations, resulting in higher throughput within a unit of time. Additionally, the stability of the SMACA algorithm is the highest, as indicated by the concentrated experimental result data. This suggests that the algorithm proposed in this  
Figure 8: Cost comparison of different algorithm scales. paper has a higher probability of achieving favorable scheduling results when dealing with different application scenarios. Based on the experimental results mentioned above, SMACA demonstrates superior performance in the adaptive migration of large-scale distributed resources. Figure 10: Throughput comparative experiment. Figure 9: Comparison of convergence time of different algorithms based on scale.