esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Structural Configurations of the APRV
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Control Condition** & **Parameter** & **Signal Source** & **Signal Source** & **Signal Source** & **Pressure of Air Tank** & **Pressure of Air Tank** & **Structural** \\  & **(SC\_1)** & **(SC\_2)** & **(SC\_3)** & **(Electronic Control)** & **(Manual Control)** & **Configuration** \\ \hline Electronic control & 1 & 0 & 0 & 0.7 MPa & 0 \\ Manual control (0.3 MPa) & 0 & 1 & 1 & 0 & 0.3 MPa & All structural \\ Manual control (0.7 MPa) & 0 & 1 & 1 & 0 & 0.7 MPa & configuration \\ \hline Coupled control & 1 & 1 & 1 & 0.7 MPa & 0.7 MPa & 0.7 MPa \\ Switch between manual and electronic control modes & 0-0.5 s \(\circ\) & 0-0.5 s \(\circ\) & 0-0.5 s \(\circ\) & 0.7 MPa & 0.3 MPa & Structural \\  & 0-0.5 s \(\circ\) & 0-0.5 s \(\circ\) & 0-0.5 s \(\circ\) & 0 & 0.3 MPa & 1, III, IV \\ Switch between manual and coupled control modes & 0-5-1 s \(\circ\) & 0-1s \(\circ\) & 0-5-1 s \(\circ\) & 0.7 MPa & 0.3 MPa & 1, III, IV \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Parameters of the simulation models for the pressure regulation characteristics of the APRV of the different structural configurations in the ECPBS of commercial vehicles. In the simulation results, when the control mode switches from the manual control mode to the coupled control mode, the pressure response time increases, and the outlet pressure cannot reach the target level. One possible explanation is that the outlet of the pedal valve is connected to the air tank (electronic control), and the outlet pressure of the pedal valve is higher than the inlet pressure; thus, the air leaks. Compared with switching to the coupled control mode, switching to the electronic control mode avoids such phenomenon, and the pressure response is better. To verify the correctness of the simulation results, the equivalent models for the APRV of the different structural configurations are built to perform tests of the pressure regulation characteristic under different control modes and verify the simulation results. Test-Based Comparative Analysis on the Pressure Regulation Characteristics of the APRV of the Different Structural Configurations in the ECPBS of Commercial Vehicles  
According to the performance requirements of the commercial vehicle braking system, the time when the pressure reaches 75% of the target pressure is taken as the index to evaluate the pressure regulation characteristics of the APRV of the different structural configurations under different control conditions.
## 4 Experiments
### Comparison Experiments
#### 4.5.3 Results on Real Datasets  
Additional testing was performed on the actual RealRain dataset in order to evaluate the suggested algorithm's practical applicability. Due of the lack of clean reference photographs that correlate to actual rainy images, the NIQE and PIQE metrics, which are judges of image quality without reference, were utilized to assess the deraining effectiveness of many techniques. Lower values indicate better results.  
1. Quantitative Comparison  
The most effective and inferior scores are marked with bold and pointed out, specifically, in Table 6, which depicts the statistical findings on the RealRain dataset.  
From Table 6, evidently, the suggested algorithm achieves the best values with a NIQE of 11.52 and a PIQE of 9.13, while SSID_KD achieved the second-best values, clearly demonstrating more effective deraining execution of the suggested algorithm in real-world applications.  
2. Qualitative Comparison  
Figure 14 presents the qualitative results of different deraining algorithms on the RealRain dataset. From Figure 14, It is evident that DARAGNet and DSMNet leave somewhat residual rainfall traces, and the restored background appears blurry. IDT and RCHNet  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Metrics** & **DARAGNet** & **DSMNet** & **IDT** & **RCHNet** & **MOSS** & **LSNet** & **SSID\_KD** & **MSWT-SSIDA** \\ \hline NIQE & 13.88 & 13.74 & 13.69 & 13.61 & 12.46 & 12.17 & 11.79 & **11.52** \\ PIQE & 10.63 & 10.41 & 10.16 & 9.85 & 9.72 & 9.53 & 9.46 & **9.13** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Quantitative results of different algorithms on the RealRain real dataset. (Highlight the best and second best results in bold and underlined).  
Figure 13: Comparison of results of different algorithms on the Rain1400 dataset. (**a**) represents the original rainy image, (**f**) denotes the ground truth clean background, (**b–e**,**g–i**) show the results of the selected comparison algorithms, and (**j**) presents the result of the proposed algorithm.  
eliminate most of the rain tracks. but result in overly smooth background restoration, reducing image quality and leaving some residual rain streaks. MOSS and LSNet suppress most of the rain stains most rain stains, but some large streaks are still not effectively handled. Compared to other methods, SSID_KD and the proposed method effectively identify and eliminate the streaks of rain, and as seen in Figure 12, the proposed method leaves the fewest residual rain streaks while maintaining good image details, achieving excellent deraining results.
## 5 Results  
Figure 5 shows the performance of CNN, LSTM, MLP, hybrid combined with CNN+Bi-LSTM and the proposed model that change as the number of epochs increases when the data size is fixed at 20 k. Figure 5a shows the accuracy. In Figure 5a, the CNN model shows the rate of change of decreasing accuracy up to epoch 10, and the accuracy is maintained after epoch 10. Next, the LSTM model increases the accuracy up to epoch 15, and after that, a stable rate of change is observed. Next, the hybrid model increases the accuracy up to 3 epochs and has remained unchanged since then. The MLP model shows that it maintains a low accuracy of 0.7. Finally, the proposed model shows a sudden increase in accuracy up to epoch 12, and the accuracy remains stable after epoch 15. Figure 5b shows the F1 score. CNN, LSTM, hybrid, and the proposed model showed similar patterns as in the accuracy, and only the MLP model showed that the rate of change did not stabilize.  
Figure 6 shows the performance of the model changing as the number of epochs is fixed at 10 and the data size is increased from 5 k to 15 k. Figure 6a,b show the accuracy and F1 score, respectively. In Figure 6a, the hybrid model and the proposed model show that the accuracy increases as the data size increases. The CNN model shows a certain level of accuracy after the data size is 11 k.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Total** & **Training** & **Test** & **Category** & **Length (Max)** & **Words (Avg)** & **Vocabulary\_Size** \\ \hline
50,000 & 25,000 & 25,000 & 2 & 500 & 237.71 & 10,000 \\ \hline \end{tabular}
\end{table}
Table 1: Data set.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Word Embedding** & **Embedding Size** & **Dropout** & **Batch Size** & **Optimizer** & **Regularizer** \\ \hline skip-gram & 500 & 0.2 & 128 & Adam & L2 \\ \hline \end{tabular}
\end{table}
Table 2: Parameter setting.  
And the LSTM model shows stable accuracy after 10 k. In the case of the MLP model, it shows that the accuracy between 0.70 and 0.75 is maintained regardless of the data size. Next, in Figure 6b, the models other than MLP show a similar pattern to the accuracy graph, and in the case of MLP, the range of change is very large and it does not show a stable value.  
Figure 5: Model performance as the number epochs increases: (**a**) Accuracy; (**b**) F1 Score.
## 2 Spatial Audio Recording Techniques for Soundscape Design
### Recording Techniques
#### 2.1.1 Stereo and Surround Recording  
Sound recording can be conducted in the form of one, two or any number in microphone array. Compared to a single microphone recording, stereo recordings could provide more spatial information on the sound field, including sense of direction, distance, ensemble of the sound stage and ambience. There are basically four types of stereo recording configurations: coincident pair, spaced pair, near-coincident pair, and baffled pair. With the evolution of sound playback system from stereo to surround sound, it is straightforward to record the sound field in surround format, even though most of the surround sound comes from mixing process. Most surround recordings are to be played (directly or after some mixing) via 5.1/7.1 surround sound system. There are various types of surround sound microphone settings. Examples of such surround microphone system include Optimized Cardioid Triangle (OCT) surround, which employs five cardioid mics with one facing front, two facing the sides, and two facing rear; IRT surround, which employs four cardioid mics placed in a square with 90 degree angles; Hamasaki square, which employs four bidirectional mics facing the sides; and a few other configurations. For more practical guidelines on microphone recordings please refer to [30; 31].
## 5 Discussion
During the research, it was also observed that there were few cases where the LDS U-Net did not perform well. For instance, in Figure 14, the demarcation between the T12 level and 1 st LBF is obscure. In these scenarios, even the other architectures failed to give proper segmentation of this section. Upon closer scrutiny, it was found that the quality of the input image was not adequate for automatic segmentation and experts were able to generate the truth mask only due to their prior knowledge on six lumbar bony features. Hence, in such cases, the image segmentation becomes quite challenging both by the human eye and by machine learning algorithms. As an improvement and for a clearer demarcation of the T12 level and 1 st LBF region, the authors postulate that other images, from different depths, can be included in the segmentation process. Second, as an overall improvement in segmentation performance, future work should be aimed at better removal of the excessive noise by making the architecture denser. Additionally, as the research is restricted to the availability of 109 image datasets, the consistency of the performance can be improved by including more image datasets from diverse patient groups.
## 3 Case Study
### Data Standardization and Preprocessing  
#### 3.3.1 Standardization of Stratum Parameters  
The stratum parameters reflect the physical and mechanical properties of soil or rock. Therefore, these parameters must be considered [50; 51]. As shown in Figure 6, the shield machine traversed various strata. The values of stratum parameters vary across different soil strata. In machine learning models, the ideal scenario is that the input for each ring involves parameters from only a single stratum. However, the reality is more complex. During the shield tunneling process, each ring segment may encounter varying amounts and thicknesses of soil layers. For example, ring 303 might traverse two layers of soil with different thicknesses, while ring 253 may pass through just one layer. Such variability results in dimensional inconsistencies in the input data. To solve this problem, the weighted average of the main stratum parameter values is used to define the stratum parameters input to the model [35], with the following equation.  
\[\beta=\sum_{i=1}^{k}\left(\theta_{i}t_{i}/\sum_{i}^{k}t_{i}\right) \tag{7}\]  
where \(k\) is the number of layers, \(t_{i}\) represents the thickness of the \(i\)th soil layer, \(\theta\) denotes the stratum parameters, and \(\beta\) denotes the weighted average of the stratum parameter values, as shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & **Maximum** & **Minimum** & **Average** & **Unit** \\ \hline E\# jack stroke (E\# JS) & 2803 & 2391 & 2690 & mm \\ F\# jack stroke (F\# JS) & 2795 & 2517 & 2680 & mm \\ A\# oil pressure (A\# OP) & 134 & 22 & 64 & Bar \\ B\# oil pressure (B\# OP) & 189 & 30 & 118 & Bar \\ C\# oil pressure (C\# OP) & 185 & 61 & 136 & Bar \\ D\# oil pressure (D\# OP) & 191 & 35 & 122 & Bar \\ E\# oil pressure (E\# OP) & 161 & 20 & 68 & Bar \\ F\# oil pressure (F\# OP) & 163 & 10 & 60 & Bar \\ Grouting quantity (GQ) & 37 & 6 & 14 & m\({}^{3}\) \\ Slurry inlet flow (SIF) & 3065 & 2350 & 2722 & m\({}^{3}\)/h \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._  
Figure 6: Geological profiles of the studied project.
# Deep Reinforcement Learning for RIS-Aided Multiuser MISO System with Hardware Impairments
## 3 Proposed Algorithm
### Transmission Scheme  
For the existing transmission schemes for RIS-assisted communication systems, the instantaneous CSI is adopted to adjust the beamforming and RIS phases shift, which requires channel estimation in each channel coherence time interval, as shown in Figure 2. However, this method has some drawbacks, as summarized as follows. For the instantaneous CSI-based scheme, the beamforming matrix and phase shift matrix need to be calculated in channel coherence interval, which increases the computational complexity. Furthermore, phase shifts of the RIS need to be updated frequently and sent back to the RIS controller, which incurs significant feedback overhead.  
To address this issue, in this paper, we consider the design of the transmission scheme based on statistical CSI. As shown in Figure 2, for the statistical CSI-based scheme, the BS only needs to estimate the statistical CSI at the start of the transmission, and the rest of several channel coherence time intervals will be fully used to transmit the information, which significantly reduces the computational complexity and feedback overhead. Once the network is trained, it can be directly applied in real-time with only simple mathematical calculations. The neural networks only need to be retrained once the statistical CSI changes.
## 2 Fundamentals of Aerial Wireless Sensor Networks for Oceanographic Monitoring.
### Sensor Nodes  
Figure 1 details the elements commonly used in the design and implementation of a sensor node. As we can see, it is normal to include a flotation device such as a buoy to keep part of the node out of the water. This out-of-the-water part always includes an antenna for RF transmission, optionally a harvesting system (solar panel, eolic generator, _etc._) to supplement the power source, and in some cases one or more external sensors essentially to monitor meteorological data (windspeed, air temperature, atmospheric humidity, _etc._). The submerged part of the node is composed of one or more sensors, which may be placed at different depths (sensor strings), a sonde to transmit the data collected  
to the buoy, and finally some means of anchoring the buoy to the seabed in order to prevent it from moving (due to marine currents, wind, waves, _etc._).  
The mote's electronics include: a module for RF transmissions, a power supply regulation and management system, a set of interfaces for accessing the sensors, a module for amplification, conversion (analog to digital) and multiplexing of the data read from the sensors (surface and underwater), a FLASH-type permanent read/write memory, a clock to act as a timer, scientific instruments (e.g., improved meteorological packages, acoustic recording packages, biological samplers, _etc._), and lastly a CPU (microprocessor) to centralize the whole process and implement the user-defined monitoring functions.
## 3 Results and Discussion
### Method Validation of the HPLC Assay for Quality Control of DSHT  
The regression equation (\(y=\text{ax}+\text{b}\)) for the calibration curve of each marker measured at seven concentration levels was calculated using the area (\(y\))-to-concentration (\(x\), \(\mu\)g/mL) ratio with standard solutions. As shown in Table 1, all coefficient of determination (\(r^{2}\)) values representing linearity in the prepared calibration curve were \(\geq\)0.9990, indicating good linearity, and LOD and LOQ were calculated to be 0.07-0.68 \(\mu\)g/mL and 0.21-2.07 \(\mu\)g/mL using Equations (1) and (2) in Section 2.6, respectively. Recovery studies of all markers calculated from Equation (3) were determined to be 94.68-105.86%, within 3.0% RSD (Table 2). The RSD value of precision was calculated from Equation (4). The repeatability of the method was established by measuring the retention time and peak area in six repeated assays. The results confirmed good repeatability of the assay system \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Analyte** & **Spiked Conc. (\(\mu\)g/mL)** & **Measured Conc. (\(\mu\)g/mL)** & **Recovery (\%)** & **SD** & **RSD (\%)** \\ \hline \multirow{3}{*}{Gallic acid} & 6.00 & 31.72 & 98.90 & 0.58 & 0.59 \\  & 15.00 & 40.84 & 100.34 & 0.84 & 0.84 \\  & 30.00 & 56.61 & 102.73 & 0.60 & 0.59 \\ \hline \multirow{3}{*}{Albiflorin} & 4.00 & 19.29 & 98.11 & 0.76 & 0.78 \\  & 10.00 & 25.50 & 101.36 & 2.20 & 2.17 \\  & 20.00 & 34.93 & 97.83 & 1.57 & 1.61 \\ \hline \multirow{3}{*}{Paeoniflorin} & 16.00 & 91.01 & 98.16 & 2.64 & 2.69 \\  & 40.00 & 115.71 & 101.01 & 0.82 & 0.81 \\  & 80.00 & 151.04 & 94.68 & 0.96 & 1.01 \\ \hline \multirow{3}{*}{Naringin} & 10.00 & 60.20 & 99.50 & 0.76 & 0.76 \\  & 25.00 & 75.05 & 99.19 & 1.16 & 1.17 \\  & 50.00 & 102.52 & 104.53 & 0.73 & 0.70 \\ \hline \multirow{3}{*}{Benzoic acid} & 2.00 & 10.93 & 97.93 & 0.80 & 0.82 \\  & 5.00 & 13.96 & 99.94 & 2.24 & 2.24 \\  & 10.00 & 18.95 & 99.82 & 2.00 & 2.00 \\ \hline \multirow{3}{*}{Baicalin} & 10.00 & 56.43 & 102.95 & 1.28 & 1.24 \\  & 25.00 & 71.81 & 102.70 & 0.92 & 0.90 \\  & 50.00 & 97.33 & 102.38 & 0.28 & 0.28 \\ \hline \multirow{3}{*}{Poncirin} & 16.00 & 91.09 & 100.23 & 1.64 & 1.64 \\  & 40.00 & 117.40 & 105.86 & 1.04 & 0.99 \\  & 80.00 & 155.60 & 100.68 & 0.33 & 0.33 \\ \hline \multirow{3}{*}{Wogonoside} & 16.00 & 99.93 & 99.37 & 1.05 & 1.06 \\  & 40.00 & 124.48 & 101.12 & 0.30 & 0.30 \\  & 80.00 & 165.32 & 101.62 & 0.21 & 0.21 \\ \hline \multirow{3}{*}{Baicalein} & 8.00 & 47.77 & 99.97 & 1.37 & 1.37 \\  & 20.00 & 59.84 & 100.37 & 0.58 & 0.58 \\  & 40.00 & 79.70 & 99.83 & 0.96 & 0.96 \\ \hline \multirow{3}{*}{Wogonin} & 2.00 & 9.78 & 95.90 & 0.30 & 0.32 \\  & 5.00 & 12.90 & 100.74 & 1.89 & 1.88 \\  & 10.00 & 18.39 & 105.33 & 2.27 & 2.15 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Extraction recovery (%) of the 10 markers in the developed HPLC assay (\(n=5\)).
# Medium Access Control Protocols for Cognitive Radio Ad Hoc Networks: A Survey
## 3 Survey of State-Of-The-Art MAC Protocols
### Hybrid Protocols
#### 3.3.4 Opportunistic MAC Protocol  
A protocol that integrates spectrum sensing and packet scheduling is that of [PERSON] et al., in [83]. It is a protocol with multi-transceiver opportunistic cognitive MAC, and the sensing is done at the PHY layer and the scheduling at the MAC layer. One transceiver is dedicated to CCC and one to the data transmission. For the data transfer over the licensed channels, opportunistic MAC divides time into different slots. CCC is divided into two different parts. The first part is the reporting phase, and it is partly slotted. It assigns a mini-slot to each channel. Toward the start of every time slot, the CR operator detects one of the channels. In the event that any channel is observed to be unmoving, the CR user sends a reference point over the CCC during that mini-slot. The second part is the negotiation phase, which is based on random access. Figure 9 illustrates the principle of opportunistic MAC. Similar to IEEE 802.11 and \(p\)-persistent carrier sense multiple access, CR operators exchange via contention-based algorithms. Each CR user selects randomly and independently a channel to guarantee that all of the channels are identified. Maintaining time synchronization places extra overhead on the protocol. Moreover, since the channel for sensing is arbitrarily selected, the neighboring nodes do not have the earlier information of this occasion and do not quiet their own transmissions to enhance the detecting accuracy, which is a disadvantage of this protocol.  
Figure 9: Opportunistic MAC principle.
# Dual Clustering-Based Method for Geospatial Knowledge Graph Partitioning
## 2 Methods
### Super-Node Partitioning Based on the LDG Algorithm  
The geospatial knowledge graph is compressed into a smaller-scale weighted graph after the process of Sections 2.1 and 2.2. To achieve the optimization goal of load balancing in graph partitioning, the LDG algorithm [12] is applied to partition the weighted graph (Figure 6). The main idea is to construct a scoring function based on the feature of vertices,  
Figure 5: Graph structure discovery.  
their neighboring nodes, and the existing partition. Then, the nodes are assigned to the partition with the highest score under the load balancing constraint. The scoring function for a node is defined from two aspects: one is the sum of the edge weights between the node and its neighbors already assigned to the partition, and the other is the capacity constraint term. The calculation formulas are as follows:  
\[g(v,P_{i})=\sum_{u\in N(v),u\in P_{i}}w(v,u)\times\left(1-\frac{|P_{i}|}{C}\right) \tag{5}\]  
\[C=\beta\times\frac{\sum_{x\in V}w(x)}{k} \tag{6}\]  
where \(N(v)\) represents the set of neighbors of node \(v\), and \(w(v,u)\) represents the weight between nodes \(v\) and \(u\). \(P_{i}\) represents the partition, and \(C\) represents the capacity constraint for the partition. \(\beta\) (\(\beta>1\)) is a user-defined parameter. \(V\) represents the total set of nodes in the graph, and \(k\) represents the number of partitions.  
Since the smaller-scale weighted graph may contain some nodes with larger weights (Figure 6a), the optimization goal of load balancing needs extra attention. In our method, the k nodes with the largest weights are first selected and assigned to different partitions. Then, the remaining nodes are loaded based on a breadth-first search strategy and assigned based on the aforementioned scoring function. The specific implementation process (Figure 6) is shown as follows:  
(i) **Pre-allocation**. As shown in the lower part of Figure 6b, nodes with the largest weight are first selected for each partition to prevent significant load imbalances in later partitioning stages.  
(ii) **Node Assignment Order**. A node is randomly selected from the remaining nodes, and the node partitioning order is determined using the breadth-first search strategy.  
(iii) **Data Allocation**. One node is loaded each time. If the sum of the weight of the current node and the total weight of the current partition does not exceed the capacity constraint \(C\) defined by Equation (6), the partition is considered a candidate partition. Then, the scoring function described by Equation (5) is used to calculate a score for the node and each candidate partition. The node is assigned to the partition with the highest score (Figure 6b). If multiple partitions have the same maximum score, the partition with the smallest weight is selected. The final result of partitioning is obtained after all nodes are assigned to corresponding partitions (Figure 6c).