esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Performance Evaluation
### Feedback Scheduling vs. Traditional Design Method  
In the first set of simulations, the proposed CLAFS method and the traditional design method without any feedback schedulers (denoted Non-FS) are compared. Since the three control loops are identical and WLAN adopts a random medium access control mechanism without distinguishing between them, all loops are equivalent in principle. Therefore, only the responses of one control loop are given below.  
Fig. 7 depicts the step responses of loop 1 under different scenarios when the traditional method is used. It can be seen that the system performs quite well when the transmission rate of WLAN is 11 Mbps. However, under the second scenario, i.e., when the transmission rate drops to 5.5 Mbps with interfering signals, the system finally becomes unstable.  
Fig. 8 shows the system performance when CLAFS is adopted. The system not only performs well under Scenario I, but also achieves good QoC under Scenario II.  
The sampling periods and the DMRs under different schemes are shown in Figs. 9 and 10, respectively. With the traditional method, the sampling periods of all control loops are fixed at runtime, i.e., h \(\equiv\) 15 ms. When WLAN runs at 11 Mbps (i.e., under Scenario I), the DMR is small with a mean of 0.9%. Consequently, the QoC is good. Under Scenario II, the DMR remains nearly 100% when time t \(>\) 2s, implying that almost all data packets transmitted on the WLAN miss their deadlines. This inevitably gives rise to system instability.  
Figure 9: Sampling periods. Figure 10: Deadline miss ratio.  
As can be seen from Figs. 9 and 10, the CLAFS scheme effectively controls the DMR through dynamically adjusting the sampling periods. Under Scenario I, the sampling periods of the control loops decrease from time t = 0, and remain at a steady level i.e. around 8 ms after time t = 5 s. The DMR also keeps at a low level, with a mean of 1.7%. Finally, it approaches its setpoint 5%. The levels of the DMR under both schemes are close, but the sampling periods are smaller when CLAFS is used.  
Under Scenario II, CLAFS successfully avoids high DMRs by increasing the sampling periods gradually. After a transient process, the DMR keeps around the setpoint 10%. It can be seen that both the sampling periods and the DMR increase on average under Scenario II relative to Scenario I, which may have some negative effects on the QoC. Consequently, the QoC is slightly worse in Scenario II than in Scenario I, as shown in Fig. 8.  
The above simulation results show that the proposed CLAFS scheme is able to effectively attack the problem of transmission rate changes and ambient noise interference, thus improving the quality of control of the whole system.
# An Integrated Platform for the Internet of Things Based on an Open Source Ecosystem
## 2 Open Source Ecosystem for the IoT
### Open Source IoT Technology for Device Access
#### 2.1.2 Long-Distance Communication  
1. LoRa/LoRaWAN [19; 20]: This technology is used for low-power long-distance communication in the IoT. LoRAWAN is the long-range communication protocol and architecture, and LoRA is the physical layer. They use a long-distance star structure to achieve communication between nodes and servers, enabling sensor data acquisition. LoRAWAN open source technologies include Lora-gate-bridge, Loraserver, Lora-app-server, packet forwarder, and the things network. Lora-gate-bridge provides a service that converts UDP packet to JSON or MQTT format. Loraserver provides Lora network server functions, which are responsible for uplink data reception and scheduling downlink data transmission. Lora-app-server, as an application server, mainly provides the LoRAWAN infrastructure node inventory, handling the received uplink application payload and downlink payload queue. It also comes with a web GUI for device management and provides the developer with a RESTful, JSON and gRPC interface. LoRAWAN also supports publishing the received payload to an MQTT broker. The Lora packet forwarder runs on the Lora gateway to convert between RF signals and IP/UDP. The things network provides Cloud Platform for accessing LoRa devices. Currently, many LoRa manufacturers use the things network to provide services where there are more than a thousand gateway devices in operation. The Lora server supports Linux, OS X, and Microsoft Windows operating systems. The Lora-gate-bridge, Lora packet forwarder, and Lora-app-server support Linux operating systems. Together, these open source software systems build a data acquisition, transmission, and application development platform for the IoT, as shown in Figure 1.  
SIGFOX open source technology: This is an IoT access technology in competition with LoRA. It is a business-operated protocol with features such as simple usage, low power consumption, low cost, and short messages. It cannot create a private network and the user can only use the \"connect as a service\" (Caas), which is provided by the SIGFOX operator. Currently, the open source technology related to SIGFOX is the Sigfox Platform, which was developed and maintained independently of SIGFOX as a visualization cloud platform for SIGGOX terminal device access [21].
3. Narrowband-IoT (NB-IoT) open source technology: The NB-IoT technology uses the operator's base station as a gateway for data access and transmission. Currently, searching on the GitHub.com website using the keyword \"NB-IoT\" shows that there are open source platforms implementing the NB-IoT development framework for terminal modules (including open source hardware platforms) and cloud platforms. Relatively complete and highly active frameworks include EasyIoT [22] and Huawei's CodeLab [23].
# Femtosecond-Laser-Assisted Fabrication of Radiation-Resistant Fiber Bragg Grating Sensors
## 4 Results and Discussion  
### Fabrication of Bragg Gratings inside Fused Silica Glass  
To optimize the irradiation conditions of the femtosecond laser beam, we fabricated Bragg gratings 62.5 \(\upmu\)m inside (the middle of the gratings) the fused silica glass samples by controlling the beam expansion (BE), the focal length of the cylindrical lens (_f_), the phase mask's distance from the glass surface (_2_), and the laser power (P). The glass samples were exposed under the laser beam for five (05) minutes. When the focal length of the cylindrical lens was 12.7 mm, no Bragg gratings evolved inside the fused silica glass samples, as illustrated in Figure 4a-d, even though we varied the beam expansion and the distance between the phase mask and the glass samples.  
Figure 3: Beam profile of the infrared femtosecond laser beam. (**a**) Before beam expander; (**b**) after the \(\times 1.88\) beam expander; (**c**) after the \(\times 2.25\) beam expander.  
Similarly, no Bragg gratings were detected inside the glass samples when the focal length of the cylindrical lens was 15 mm, as depicted in Figure 4e-h. As we know, the Bragg gratings were fabricated by light interference caused by the phase mask. The amount of power in the layer with light interference is expected to be different for different cylindrical lenses having different focal lengths due to the variation in spherical aberrations [37]. The amount of power at the interfering points for the cylindrical lenses having a focal length of 12.7 mm and 15 mm might be lower than the threshold value required to fabricate Bragg gratings inside the fused silica glass sample, which in turn is responsible for the fact that no Bragg gratings were formed inside the fused silica glass sample. We were able to fabricate Bragg gratings inside glass samples only when the distance of the phase mask from the glass sample was 1000 \(\upmu\)m or 1500 \(\upmu\)m with the cylindrical lens having a focal length of 19 mm (as shown in Figure 4i-l). Although both kinds of beam expander were able to form Bragg gratings, the beam expander having a magnification of \(\times\)2.25 consistently showed Bragg gratings in the laser-irradiated area. The best-quality Bragg gratings were observed inside the glass samples when \(f\) = 19 mm, \(BE\) = \(\times\)2.25, and \(z\) = 1000 \(\upmu\)m (as illustrated in Figure 4l). These parameters can be considered the optimal irradiation conditions of the femtosecond laser beam for fabricating FBGs inside the fiber core.
## 6 Empirical Results and Analysis
### Results of Baseline Model Regression
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **(I)** & **(2)** & **(3)** & **(4)** & **(5)** & **(6)** & **(7)** \\ \hline \multicolumn{6}{c}{**Variables**} \\ \hline lnHSRFreq1 & 0.101 *** & 0.090 *** & 0.088 *** & 0.086 *** & 0.087 *** & 0.088 *** & 0.091 *** \\  & (4.80) & (4.13) & (4.14) & (4.06) & (4.08) & (4.09) & (4.21) \\ sci\_GDP & & 0.556 * & 0.506 * & 0.548 * & 0.588 * & 0.594 * & 0.581 * \\  & & (1.86) & (1.68) & (1.80) & (1.92) & (1.94) & (1.91) \\ Inbooks\_100\_1 & & & 0.105 * & 0.139 *** & 0.145 *** & 0.144 *** & 0.144 *** \\  & & & (1.90) & (2.98) & (3.13) & (3.12) & (3.12) \\ lnmobile1 & & & & \(-0.093\) ** & \(-0.088\) ** & \(-0.087\) ** & \(-0.086\) ** \\  & & & & (\(-2.53\)) & (\(-2.46\)) & (\(-2.43\)) & (\(-2.41\)) \\ Fin\_GDP & & & & & \(-0.131\) ** & \(-0.126\) ** & \(-0.121\) ** \\  & & & & & (\(-2.46\)) & (\(-2.40\)) & (\(-2.33\)) \\ unemp\_Pop & & & & & & \(-0.086\) ** & \(-0.085\) ** \\  & & & & & & (\(-2.30\)) & (\(-2.23\)) \\ gyfs\_GDP & & & & & & & (\(-2.60\)) \\  & & & & & & & (\(-2.60\)) \\  & & & & & & & \\  & & & & & & & \\ Constant & \(-3.368\) *** & \(-3.385\) *** & \(-3.713\) *** & \(-3.434\) *** & \(-3.220\) *** & \(-3.178\) *** & \(-3.088\) *** \\  & (\(-48.43\)) & (\(-47.59\)) & (\(-20.68\)) & (\(-16.02\)) & (\(-13.82\)) & (\(-13.48\)) & (\(-12.73\)) \\ Observations & 2840 & 2840 & 2840 & 2840 & 2840 & 2840 & 2840 \\ R-squared & 0.348 & 0.350 & 0.351 & 0.355 & 0.357 & 0.358 & 0.360 \\ Number of cities & 284 & 284 & 284 & 284 & 284 & 284 & 284 \\ City FE & Yes & Yes & Yes & Yes & Yes & Yes \\ Time FE & Yes & Yes & Yes & Yes & Yes & Yes \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The empirical results of the baseline model. and low environmental protection requirements, which work against the development of green technology creation.
# A Dynamic Distributed Deterministic Load-Balancer for Decentralized Hierarchical Infrastructures
## 7 Experimental Study
### Performance Evaluation for \(D^{3}\)-Tree
#### 7.1.2 Cost of Element Insertions/Deletions  
To measure the network performance for the operation of element updates, we conducted experiments for three different value ranges of _criticality_: [0.90 1.10], [0.67 1.50], [0.53 1.90] formed from values of constant \(c=\{1.1,1.5,1.9\}\) correspondingly. For a network of \(N\) nodes and \(n\) elements, we performed \(n\) element updates.  
In a preliminary set of experiments with mixed operations (insertions/deletions), we observed that load-balancing operations rarely occurred, thus leading to negligible node update costs. Hence, we decided to perform only one type of update, \(n\) insertions.  
Figure 7a shows the average amortized load-balancing cost, while Figure 7b shows the load-balancing rate. Both average cases and worst cases are depicted in the same graph. The average cases for \(c\) values of 1.5 and 1.9 led to negligible amortized cost, so they were disregarded. The same node (the leftmost leaf) is the host node for all element insertions in the worst case. Note that we have only considered the amortized cost of element insertions causing load-balancing operations since otherwise, the amortized cost is negligible.  
Figure 6: Node update operations. (**a**) average case, (**b**) worst case, (**c**) redistribution rate of winner \(D^{3}\)-Tree, (**d**) redistr/ion height of winner \(D^{3}\)-Tree.  
Conducting experiments, we observed that in the average case, the \(D^{3}\)-Tree outperforms BATON, BATON\({}^{*}\), and P-Ring. However, in \(D^{3}\)-Tree's worst case, the load-balancing performance is degraded compared to BATON\({}^{*}\) of _fanout_ = 10 and P-Ring.  
Moreover, we observed that in the average case, during element insertions, the Invariant 3 is rarely violated to invoke the load-balancing mechanism (Figure 7b). This also depends on the value range of criticality. When the range is narrowed, more load-balancing operations occur during element insertions, but the amortized cost is low since the subtree isn't very imbalanced. However, the majority of load-balancing operations occur in subtrees of high height, as shown in Figure 7c. On the other hand, when we use wide ranges, more element insertions occur before the load-balancing mechanism is activated, leading to more frequent and costly load-balancing operations.
## 4 Results and Discussion
### Robustness of Deep-Learning Models under Various Communication Systems
#### 4.3.1 CNN-based Models Used in the Experiment
For training, Stochastic Gradient Descent (SGD) optimizer and cross entropy-loss was used for both models, with learning rates ranging from 0.01 to 0.00001 and 0.1 to 0.0001 for ResNet152V2 and EfficientNetV2-B0, respectively, using the reduce learning rate (ReduceLR) technique. Furthermore, various augmentation techniques, such as 20-degree rotation, horizontal and vertical shift with factor 0.1, nearest fill mode, and 0.25 to 1.25 factor zoom and brightness, were applied to achieve high validation accuracy during training (as discussed in Section 3.2). The accuracy of a DL model is commonly evaluated using three metrics, training accuracy, validation accuracy, and test accuracy. The training accuracy reflects the model's ability to accurately predict data that were used for training. The validation accuracy assesses the model's ability to generalize to new data during training, while the test accuracy measures the model's performance on unseen data after training. In the experiment, ResNet152V2 achieved 98% training accuracy, and EfficientNetV2-B0 achieved 100% training accuracy. The training accuracy is comparable to related classification study one GTSRB dataset using ResNet152 [83] and EfficientNetV2 [84] family which achieved 96% and 98%, respectively. The validation accuracy of the models showed significant difference with and without applying augmentation techniques in the experiment. ResNet152V2 achieved 84% validation accuracy without augmentation, indicating overfitting and poor generalization; however, when the augmentation techniques were applied, the validation \begin{table}
\begin{tabular}{c c c} \hline \hline  & **ResNet152V2** & **EfficientNetV2-B0** \\ \hline Model Input Size: & & 224 \(\times\) 224 \\  & Max Pool, 2048 & \\  & Flatten, 2048 & Average Pool, 1280 \\ Top Layers: & Fully Connected, 4096 * & Flatten, 1280 \\  & Fully Connected, 4096 * & Fully Connected, 256 * \\  & Dropout, 4096 & Fully Connected, 7 + \\  & Fully Connected, 7 + & \\ Total parameter: & 83,534,343 & 6,249,047 \\ Trainable parameter: & 83,390,599 & 6,188,439 \\ Non-trainable parameter: & 143,744 & 60,608 \\ \hline \hline \end{tabular}  
* ReLU Activation + Softmax Activation.  
\end{table} 
Table 4: Parameters of the DL models used in the experiments. accuracy improved to 96%. Similarly, EfficientNetV2-B0 achieved 95% validation accuracy without augmentation, which improved to 99% with augmentation. Both the training and validation of the two models were completed on pristine (original) images. The inference (test) accuracy of the models on noisy images from different communication systems is further discussed.
## 2 Materials and Methods  
The video material acquired for this study, the visual recognition procedure, the producing of the heatmaps and the data analysis are described below. Publicly available videos in TV broadcast (N = 62) were shortlisted for data collection by two table tennis experts. Official competitive matches of male table tennis players of the top 100 of the ITTF (International Table Tennis Federation) world ranking were screened (types of tournaments: World Table Tennis Championships, ITTF World Tour, (Youth) Olympic Games, T2 Diamond, European Championships, ETTU--Europe Top 16, ETTU Champions League, Japan Open, Swedish Open, Czech Open, Hong Kong Open, German Open, Chinese Super League, T2 Asia-Pacific League, French League, Polish League, German League, Olympic Qualification, Marvellous 12, Dusseldorf Masters). The matches were played in best-of-five and best-of-seven modes. Our automatic extraction pipeline provided sufficient detections and dense data for 45 of the shortlisted videos due to a beneficial camera perspective (N = 198 sets). The remaining videos were rejected due to unreliable person or table detections. These videos were of high-level and championship games, each consisting of an entire match and lasting around 60 min. For each video, the focus was on one player. The analyzed matches were played from 2006 to 2020. Following [PERSON] et al. (1992), the players were categorized by table tennis experts according to handedness, playing strategy and racket holding [28]. The dominant hand of the players was established according to which hand was used to hold the racket [32]. The classification of the playing system was determined by two scientists who independently identified whether the player was closer to the table and played more offensive strokes (e.g., topspin: players normally tilt the paddle forward and brush it over the ball from a low to a high position in order to give the ball a forward rotation [33]) or further away from the table and played more defensive strokes (e.g., backhand chop: this is normally used by defensive players to impart backspin on the ball which makes it harder to attack. It is mostly played against a topspin stroke). Additionally, the racket position was determined for the players with whom the analyzed match had been played. Out of the 45 videos, 31 players were right-handed and 14 players were left-handed; 32 players were attack/offensive players and 13 had a more defending playing style; and 25 had the shakehand grip while holding the paddle and 20 had the racket/paddle in a penholder grip. The videos were edited and recorded in the manner of TV broadcasts, with cuts, jumps and replays. Therefore, the parts of the video that contained the actual game had to be extracted first. After data collection, the methodology was developed using one video and then applied to all of the other videos.
# An Adaptive Data Rate Algorithm for Power-Constrained End Devices in Long Range Networks
## 4 LoRa Network Performance Evaluation  
### Network Throughput Model  
In a LoRa network, EDs use the pure ALOHA protocol for random access packet transmission. Assume that nodes generate packets according to a Poisson process, with an average generation rate of \(\lambda\) packets per second, and the packet length is \(L\). EDs in the network use \(m\) different data rates for packet transmission, with \(N_{i}\) EDs using each rate \(R_{b,i}\). Let \(G_{i}\) represent the number of packets generated by EDs transmitting at the rate \(R_{b,i}\). Under the pure ALOHA protocol, the probability of a LoRa ED successfully transmitting a packet without collision at rate \(R_{b,i}\) is given by Equation (21).  
\[P_{success}=e^{-2G_{i}} \tag{21}\]  
The time required for ED \(i\) to transmit a packet \(T_{i}\) is given by Equation (22).  
\[T_{i}=\frac{L}{R_{b,i}} \tag{22}\]  
The data rate \(R_{b,i}\) can be calculated using Equation (23).  
\[R_{b,i}=SF\cdot\frac{BW}{2^{SF}}\cdot\frac{4}{4+CR} \tag{23}\]  
where \(SF\) represents the SF used by the ED, \(BW\) is the modulation BW during the packet transmission, and \(CR\) represents the coding rate, with values ranging from 1 to 4.  
The total traffic generated by all EDs transmitting at the rate \(R_{b,i}\) per unit time is given by Equation (24).  
\[\lambda_{i}=\frac{N_{i}}{T_{\text{interval}}} \tag{24}\]  
The traffic generated by EDs transmitting data packets at a rate \(R_{b,i}\) is \(G_{i}=\lambda_{i}T_{i}\), and the collision probability is given by Equation (25).  
\[P_{collision\_i}=1-e^{-2\lambda_{i}T_{i}}=1-e^{-2\cdot\frac{\lambda_{i}\cdot L }{R_{b,i}}} \tag{25}\]  
The average throughput of the ED is given by Equation (26).  
\[\Gamma_{i}=\lambda\cdot L\cdot P_{success}=\lambda\cdot L\cdot e^{-2\cdot \frac{\lambda_{i}\cdot L}{R_{b,i}}} \tag{26}\]  
When a total of \(m\) different data rates are used for transmission in the network, and there are \(N_{i}\) EDs using data rate \(R_{b,i}\), the total network throughput \(\Gamma\) is given by Equation (27).  
\[\Gamma=\sum_{i=1}^{m}\Gamma_{i}\cdot N_{i} \tag{27}\]  
When only one data rate is used in the LoRa network, the impact of the number of EDs on network throughput is investigated. From Figure 3a-c, it can be seen that network throughput first increases and then decreases as the number of EDs increases. Different SFs significantly affect the peak throughput and the number of devices. Increasing the SF reduces network capacity and throughput. Smaller SFs (e.g., SF = 7) and larger BWs (e.g., 250 kHz) significantly improve network throughput, while larger SFs (e.g., SF = 12) and smaller BWs reduce data rates and throughput. Reasonably adjusting the SF and BW can effectively enhance the performance and capacity of the LoRa network.
## 5 Performance Evaluation
### The Utilization of Energy  
(1)The average hops  
Figure 5 shows average hops with varying network size. It is shown that with the increase of network size, the average hops increases in the fluctuation. Obviously, the average hops of MCRP  
Figure 6 shows the average energy consumption per hour in the whole network of the three algorithms under different network size. As can be seen from Figure 6, the average energy consumption of the three algorithms increases with the increase of the number of nodes in the network, and the EBRA-NLE proposed in this paper is always kept at the lowest level. First of all, the average energy consumption in the network will increase with the increase of the number of nodes, the average number of hops of EBRA-NLE is low, and the data transmission path is as close to the shortest path as possible, so it can effectively reduce the overall energy consumption of the network. Nevertheless, MCRP ignores the distance factor in route planning, which may lead to energy waste due to detours.
# Cross-View Attention Interaction Fusion Algorithm for Stereo Super-Resolution
## 3 Experimental Results
### Quantitative Analysis
Compared with the stereo super-resolution networks PASSRnet, SPAM and iPASSR, the network constructed by the combination of our algorithm and SRResNet can achieve the best super-resolution effect on KITTI 2012 and KITTI 2015 datasets. The results show that the proposed attention stereo fusion module effectively combines the information in the left and right views after vertical sparse connection. The horizontal dense connection method proposed in the enhanced cross-view interaction strategy also further combines various information in view, between views and in attention stereo fusion modules.