esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Field Measurements
### Measurement Equipment and Solutions
#### 2.2.1 Measurement Equipment  
The measurement devices are mainly vibrational chord strain gauges and string pressure sensors, and the relevant parameters are shown in Table 1. The strain gauge is used to measure the strain and stress within the CPB roof during the mining process, and its model number is YHZ-0130. The strain gauge is buried in CPB with a range of \(\pm\) 1500 micrometer strain (\(\mu\epsilon\)), a resolution of 1 \(\mu\epsilon\) and an accuracy of \(\pm\) 0.5% full-scale (F.S). The strain change within the CPB roof is measured by this strain gauge, and the strain can be converted to stress by Equation (1).  
\[\sigma=E\cdot\epsilon \tag{1}\]  
where \(\sigma\) is the stress in the CPB roof, \(E\) is the elastic modulus of the CPB roof and \(\epsilon\) is the strain in the CPB roof.  
The pressure sensor is used to measure the load inside the CPB roof during the mining process and its model number is YHZ-0701. The sensor is buried in concrete sustaining a maximum pressure of up to 1 MPa with a resolution of 0.1 KPa and an accuracy of \(\pm\) 0.5% F.S.
## Abbreviations \begin{table}
\begin{tabular}{l l c c c c} \hline \hline
**Model Name** & **Type** & **75 Users** & **100 Users** & **125 Users** & **150 Users** \\ \hline \multicolumn{5}{c}{Single-Instance Models} \\ \hline
**qwen2-2b-1-replica** & VLM & \(35.6471\pm 21.5337\) & \(48.3445\pm 27.6978\) & \(65.3267\pm 35.5868\) & \(89.1086\pm 48.6269\) \\
**qwen2-7b-1-replica** & VLM & \(57.4285\pm 30.0506\) & \(72.2567\pm 42.4952\) & \(82.1418\pm 46.4424\) & \(109.6259\pm 59.9394\) \\
**Ilava-13b-1-replica** & VLM & \(52.1012\pm 29.8732\) & \(68.8172\pm 38.2909\) & \(90.2259\pm 50.8535\) & \(106.5157\pm 60.0830\) \\
**Ilama-1b-1-replica** & LLM & \(62.6997\pm 35.7966\) & \(67.5163\pm 42.8536\) & \(97.8888\pm 55.8919\) & \(121.9497\pm 73.0799\) \\
**Ilama-3b-1-replica** & LLM & \(86.7498\pm 50.5465\) & \(102.4427\pm 55.4608\) & \(130.1832\pm 73.8914\) & \(153.1903\pm 85.6271\) \\
**Ilama-8b-1-replica** & LLM & \(119.6549\pm 66.4862\) & \(146.5137\pm 82.7816\) & \(188.2520\pm 115.2399\) & \(237.6892\pm 131.8673\) \\ \hline \multicolumn{5}{c}{Multiple-Instance Models (Ray)} \\ \hline
**qwen2-2b-20-replica** & VLM & \(9.5376\pm 4.9529\) & \(12.7513\pm 6.4241\) & \(15.4664\pm 6.9279\) & \(19.9774\pm 10.2574\) \\
**qwen2-7b-12-replica** & VLM & \(14.2411\pm 6.8963\) & \(17.4584\pm 10.2561\) & \(18.2418\pm 9.7270\) & \(22.9139\pm 12.3596\) \\
**Ilava-13b-12-replica** & VLM & \(12.9215\pm 5.9919\) & \(16.4164\pm 8.3392\) & \(23.0050\pm 12.2063\) & \(26.2898\pm 13.1351\) \\
**Ilama-1b-20-replica** & LLM & \(12.3633\pm 7.5692\) & \(19.8064\pm 10.1389\) & \(20.3427\pm 10.7773\) & \(26.2847\pm 13.4261\) \\
**Ilama-3b-20-replica** & LLM & \(22.6402\pm 10.9646\) & \(27.8081\pm 14.8628\) & \(36.1693\pm 16.2197\) & \(42.9934\pm 22.5223\) \\
**Ilama-8b-12-replica** & LLM & \(31.2869\pm 18.0103\) & \(35.6251\pm 19.3717\) & \(47.2479\pm 26.6813\) & \(55.7640\pm 31.7094\) \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Performance comparison: single-instance vs. replica models.
# Channel-Hopping Using Reinforcement Learning for Rendezvous in Asymmetric Cognitive Radio Networks
## 3 Methodology
### Action Space and State  
The actions in this paper are divided into two types. A binary decision, where selecting 1 corresponds to channel-hopping and selecting 0 indicates no hopping, aligns with the mapping of the JS algorithm. If the decision is made to perform channel-hopping, the channel is selected by adding 1 to the index of the previously chosen channel, and this  
Figure 3: Reinforcement learning framework for rendezvous.  
is determined as the final channel for the rendezvous attempt. This approach introduces flexibility to the mathematically defined sequential channel exploration of the JS algorithm by leveraging reinforcement learning. Additionally, the action can be expanded to include both the decision to hop and the extent of the hop when selecting a channel. To allow for these options, the actor network can be designed to split its outputs into two parts.  
The state is defined in two ways as shown in the following equations. The first state, denoted as \(State_{R_{t}}\), is defined by the sequence of recent actions. This state utilizes a rendezvous technique based on an M-sequence, which relies on the state of \(k\) shift registers. It serves to guide the learning process by selecting the next optimal action based on the most recent \(k\) failed attempts. The second state, denoted as \(State_{W_{t}}\), represents the number of attempts made for each type of action up to time \(t\). While the first state accurately captures the action information over a short period, this second state summarizes the action information over the entire period, allowing the agent to be guided towards a more balanced selection of actions.  
\[State_{R_{t}} =\{A^{i}_{t-k-1},A^{i}_{t-k-2},\ldots,A^{i}_{t-1},0,..\} \tag{5}\] \[State_{W_{t}} =\{Count(Ch_{1}),Count(Ch_{2}),\ldots,Count(Ch_{n})\} \tag{6}\]
## 2 Routing Algorithms for Wireless Sensor Networks
### Location-Based Protocols  
Location-based protocols make use of position information to relay data to the desired regions, instead of the entire network. Before a packet can be sent, the position of the destination must first be determined. Typically, a location service is responsible for this task. Existing location services can be classified according to how many nodes host the service. This can be either a specific node or all of the network nodes. Furthermore, each location server may maintain the position of a specific node or all the nodes in the network.  
In position-based routing, a node's forwarding decision is primarily based on the position of a packet's destination and the position of its immediate one-hop neighbor. The position of the destination is contained in the header of the packet. If a node has a more accurate position of the destination, it may choose to update the position in the packet before forwarding it. The position of the neighbors is typically learned through a one-hop broadcast beacon. These beacons are sent periodically by all nodes and contain the position of the sending node.  
We can distinguish three main packet-forwarding strategies for position-based routing: greedy forwarding, restricted directional flooding, and hierarchical approaches. For the first two, a node forwards a given packet to one (greedy forwarding) or more (restricted directional flooding) one-hop neighbors that are located closer to the destination than the forwarding node itself. The selection of the neighbor in the greedy case depends on the optimization criteria of the algorithm. The third forwarding strategy is to form a hierarchy in order to scale to a large number of mobile nodes.  
Minimum Energy Communication Network (MECN) [20] establishes and maintains a minimum energy network for wireless networks by utilizing low-power geographic positioning system (GPS). The main idea of MECN is to find the sub-network with the smallest number of nodes that requires the least transmission power between any two particular nodes (shortest path). The Small Minimum Energy Communication Network (SMECN) [21] is an extension of MECN. The major drawback of MECN is that it assumes that every node can transmit to every other node, which is not always possible. One advantage of SMECN is that it considers obstacles between pairs of nodes. Geographic Adaptive Fidelity (GAF) [22] is an energy-aware location-based routing algorithm primarily designed for ad-hoc networks that can also be applied to sensor networks. GAF conserves energy by turning off unnecessary nodes in the network without affecting the level of routing fidelity. Finally, Geographic and Energy Aware Routing [23] uses energy-awareness and geographically informed neighbor selection heuristics to route a packet toward the destination region.
# 3D Numerical Simulation and Performance Analysis of CO2 Vortex Tubes
###### Abstract  
In view of the extensive application of swirl flow pipes (vortex tubes) in refrigeration systems, the parameters of swirl flow pipes were investigated to provide optimal cooling and heating conditions. Three-dimensional numerical simulations were carried out using available experimental data and models. The analysis verified that the heat pipe with a length of 175 mm performed better than the swirl flow pipe with a length of 125 mm, confirming experiments by [PERSON]. Meanwhile, by comparing different pressures, it was found that in the single-nozzle swirl flow pipe, the greater the increase of pressure (0.1-1.0 MPa), the greater the burden on the vortex chamber and the more serious the wear is, which can be seen in the higher inlet pressure. In order to improve the durability of the swirl flow pipe, we suggest using a swirl flow pipe with more nozzles. Finally, according to the simulation results, with the rise of carbon dioxide pressure potential energy at the inlet, the cooling effect of the swirl flow is first increasing and then decreasing. When the swirl flow pipe is used as a refrigeration device to determine the minimum cooling temperature under the maximum pressure, the lowest temperature of the 125 mm swirl flow pipe was 252.4 K at 0.8 MPa, while the lowest temperature of the 175 mm swirl flow pipe was 246.0 K. Secondly, the distance from the inlet to the hot outlet of the swirl flow pipe had little effect on the cooling temperature and radial velocity, but increasing its distance increased the wall temperature of the swirl flow pipe because it increases the contact time between the airflow and the hot end of the tube wall. When the swirl flow pipe is used as a heat-producing device, increasing the tube length of the swirl flow pipe appropriately increases its maximum heat-producing temperature.  
CFD; swirl flow pipe; temperature separation; inlet pressure; carbon dioxide +
Footnote †: journal: Applied Sciences
# An Efficient Approach for LBS Privacy Preservation in Mobile Social Networks
## 3 Preliminaries
### BV Homomorphic Encryption  
BV homomorphic encryption (BVHE) is based on the Ring Learning With Errors (RLWE) problem [30], and supports both additive homomorphisms and multiplicative homomorphisms. The main algorithms of BVHE include key generation (KEYGEN), encryption (ENC), evaluate (EVAL), and decryption (DEC). The details are as follows.  
* KEYGEN: For some given parameters n, prime q, error parameter \(\delta\), and \(f(x)=x^{n}+1\). \(n\) is the power of 2, and \(q\equiv 1\mod 2n\). The Ring \(R=Z[X]/\langle f(x)\rangle\) and the Ring \(R_{q}=Z_{q}[x]/\langle f(x)\rangle\) can be selected by using \(n,q\) and \(f(x)\). A discrete Gaussian error distribution \(\chi=D_{Z^{n},\delta}\) can be defined by taking \(\delta\) as the variance, where the integer \(D>0\). The message space \(R_{t}=Z_{t}[x]/\langle f(x)\rangle\) can also be defined by choosing prime \(t\in Z_{q}^{*}\). Under the setting above, a public-private key pair (\(pk\), \(sk\)) can be generated by randomly choosing \((s_{i},e_{i})\) from \(\chi\) and \(a_{1}\) from \(R_{q}\), where \(sk_{i}=s_{i}\), \(pk_{i}\) = \((a_{0},a_{1})\), and \(a_{0}=-(a_{1}s_{i}+te_{i})\).
* ENC: For a plaintext \(m\in R_{q}\) that needs to be encrypted, the encryption process (i.e., \(C=E_{p}k(m)\) ) is as follows: The ciphertext \(C=(c_{0},c_{1})\) can be calculated by choosing samples \(u\leftarrow\chi\), and \(f,g\leftarrow\chi\), where \(c_{0}=a_{0}u+tg+m\) and \(c_{1}=a_{1}u+tf\).  
* EVAL: Taking additive homomorphisms as an example. For some given plaintexts \(m_{1}\), \(m_{2}\), and \(m_{3}\). If \(m_{3}=m_{1}+m_{2}\), then the Formula (1) is hold. \[DEC(ENC(m_{3}))=DEC(ENC(m_{1})+ENC(m_{2})).\] (1)
* DEC: For a ciphertext m that needs to be decrypted, the decryption process (i.e., \(m=D_{sk}(C)\)) is as follows: The corresponding plaintext of m can be calculated by \(m=(c_{0}+c_{1}\times s_{i})\mod t\).
# Single-Phase, Bidirectional, 7.7 kW Totem Pole On-Board Charging/Discharging Infrastructure
## 2 Brief Detail and Parameter Design
### Bidirectional DC-DC Converter Design  
Furthermore, in the proposed system, the second stage consists of a battery-side bidirectional (BSBD) DC-DC converter that operates in two modes, i.e., charging and discharging for G2V and V2G applications, respectively. During charging mode, it acts as a buck converter. On the other hand, it acts as a boost converter while discharging.
# Data Embedding in SHVC Video Using Threshold-Controlled Block Splitting
## 5 Conclusions  
In this paper, a tunable threshold-controlled block splitting and data embedding method is proposed. The embedding capacity is improved by making the PBs assume smaller sizes, which directly results in having more manipulable predictive syntax elements for data embedding purposes. Specifically, the strength of our proposed method is that the embedding capacity can be tuned according to the varying payload requirements. When the required embedding capacity is low, the room given to code more smaller blocks can be reduced (i.e., reducing \(\tau\)) so that the bit rate overhead and distortion can be kept to the minimum. On the other hand, when there is a need to accommodate more data, the video encoding process is made to code more coding blocks by using the smallest block size (i.e., increasing \(\tau\)). Both intra prediction mode and MVP of SHVC video are jointly utilized to embed data. The data embedding opportunity is hence increased by jointly utilizing both intra prediction mode and MVP. The encoder calculates the prediction errors based on the original and the predicted values after the predictive syntax elements are modified to match the payload bits. Therefore, the reconstructed video is a close approximation of the original video and the perceptual quality of the video is maintained without error-drift. Experimental results suggest that the achieved embedding capacity is encouraging while the video quality and bit rate are maintained. A trade-off between the increase in embedding capacity and bit rate overhead can be achieved by tuning the threshold \(\tau\) depending on the application of interest.  
As future work, we aim to explore different venues for data embedding in SHVC and investigate into their impact on the video quality as well as the bit rate overhead. We will also explore potential joint utilization of the proposed and the conventional data embedding methods.  
[PERSON] contributed to formulating the ideas and concepts data embedding in SHVC. She also implemented the ideas, conducted experiments, and compiled this article. [PERSON] contributed to the formulation of idea and experiment setup. He also supervised the overall compilation of this article and contributed to reviewing and editing, in particular, the structure of this article. [PERSON] contributed to idea formulation, technical input to the coding standard of SHVC, the setup of the experiment, as well as validation of the experimental results. He also contributed to writing. [PERSON] contributed to the supervision of this project, fund acquisition, and the technical input to the coding standard of HEVC/SHVC. He also contributed to the writing as well as comparative analysis. All authors have read and agreed to the published version of the manuscript.  
The work of [PERSON] was supported in part by the Overseas Expertise Introduction Project for Discipline Innovation (111 project: B18041).
## 4 Simulated Results and Analysis
### Sinusoidal Steering Condition  
On carsim, the road surface adhesion coefficient is set as 0.6, the initial vehicle speed is 108 km/h, the simulation time is 10 s, the steering wheel rolling angle is 0.25 Hz, and the amplitude 1 rad means sinusoidal driving. The steering wheel input is shown in Figure 5. The simulation results are shown in Figure 8a-j.  
Figure 5: The front wheel steering angle input.  
The ADUKF can well estimate the sideslip angle with low errors and can meet the demand for vehicle stability control (Figure 8a,b). When the vehicle is under control without controller intervention, the yaw rate and sideslip angle both severely fluctuate, and after about 2.7 s, the expected value cannot be traced, so the vehicle enters an unstable status (Figure 8c-e). With the intervention by AFRBF-SMC and by SMC, both controllers can effectively ensure the stability of the target vehicle. The AFRBF-SMC can better trace the ideal value and causes less fluctuation compared with SMC, and the yaw rate and sideslip angle are closer to the ideal values. AFRBF-SMC, compared with SMC and the controlless status, makes the vehicle have better dynamics performance (Figure 8e-j). Clearly, with AFRBF-SMC, the certainty term and the cylinder pressure both change (Figure 8i,j). This is because when AFRBF-SMC is used to estimate the disturbance term, the vibration control effect is better than SMC, and the control rate of additional yaw moment is finer. Nevertheless, AFRBF-SMC causes more longitudinal dynamics interventions. As for lateral dynamics, AFRBF-SMC can ensure smaller lateral displacement and lower-fluctuation lateral acceleration. During sinusoidal steering, the overall performance of AFRBF-SMC is better.  
Figure 6: \(Cont.\)  
Figure 6: The simulation results of the front wheel sinusoidal steering test: (**a**) the comparison of estimated sideslip angle and real value; (**b**) the sideslip angle estimation error; (**c**)the vehicle yaw rates; (**d**) the vehicle sideslip angle; (**e**) the phase plane of the vehicle sideslip angle and the vehicle sideslip angle rate; (**f**) the vehicle lateral acceleration; (**g**) the vehicle lateral displacement; (**h**) the vehicle longitudinal velocity; (**i**)the estimated of value \(d\); (**j**) brake pressure of the braking wheels by adaptive fuzzy radial basis function neural network sliding mode control (AFRBF-SMC) controller.
## 4 Simulation Analysis
### Scenario 2  
In this scenario, the landing procedure is simulated in windshear to examine the robustness of the SIRAC. Windshear is modeled, as in Section 2.2, and the approaching speed \(V_{0}\) is 67.4 m/s, the time \(T\)\(=60\) s during which the aircraft will fly in the windshear and the strengths of the windshear \(f_{u}\) and \(f_{w}\) are both set as 1.5. Figure 9 shows the horizontal speed and vertical speed of the windshear, respectively. The aircraft will meet an increasing horizontal headwind and the maximum speed is about \(-14\) m/s. Subsequently, the horizontal headwind will become horizontal downwind and the maximum speed is about \(14\) m/s. In the meantime, there is a downward flow, and the maximum speed is about \(-15\) m/s.  
Figure 10a shows a comparison of the altitude response between the actual trajectory and desired trajectory in a windshear environment. Additionally, Figure 10b shows the altitude error of LQR, RAC, and SIRAC. It can be seen that the largest altitude error of SIRAC is less about 10 m and 15 m than RAC and LQR in the glide slope and both 3 m in the flare segment. The velocity response of SIRAC is also smoother and more closed to desired velocity than LQR and RAC, as shown in Figure 10c. The largest velocity amplitude of SIRAC is less about 5 m/s and 6 m/s than RAC and LQR, respectively. In the windshear, it is also expected that the descent rate is slow and smooth. The descent rate of SIRAC is more closed to \(-3.5\) m/s, as the largest error of SIRAC is about 1 m/s, RAC is 2.5 m/s, and LQR is 3 m/s, as seen from Figure 10d. The pitch angle and pitch rate responses are also shown in Figure 10e,f, respectively. It is clearly seen that when the aircraft encounters a windshear, the pitch  
Figure 8: Scenario 1–No wind disturbance, (**a**) Altitude response (**b**) Altitude error. (**c**) Velocity response (**d**) Descent rate response. (**e**) Pitch angle response. (**f**) Pitch rate response.  
Figure 9: Windshear.  
angle response has a biggest variation 8 deg while using SIRAC, 15 deg using RAC, and 18 deg using LQR. Additionally, the pitch rate also has a larger variation in the transition from initial approach to glide slope and glide slope to flare segment while using SIRAC, which means that the transition time of SIRAC is shorter. As a result, it is evident that the SIRAC provides a more precise and steady landing.  
Figure 10: Scenario 2–Landing in Windshear. (**a**) Altitude response (**b**) Altitude error. (**c**) Velocity response (**d**) Descent rate response. (**e**) Pitch angle response. (**f**) Pitch rate response.
# Using Artificial Neural Network Models to Assess Hurricane Damage through Transfer Learning
## 2 Building Damage Dataset
### Data Collection and Preparation  
This study primarily focuses on the hurricane damage to buildings in the U.S. southeast region. We sourced the data from an internet search specifying criteria for photos related to hurricane damage, and a few thousand images taken from hurricane damage in Florida, Georgia, North Carolina, and South Carolina were prepared for a preliminary data cleanup. Each image in our in-house dataset was further examined for types of buildings and structures contained in the images to ensure they were characteristic of the U.S. east coast region.  
The raw dataset was further processed for two tasks: damage classification and damaged object detection. For the first task, we examined the data to be used in the classification model and identified potential classes for image categorization. This step involved the additional cleanup and removal of remaining images that were duplicates or would not be a candidate for one of the image classes. For the second task, we also examined individual images to be used in the object detection model and removed those that did not capture a damaged structure. After final examination of both versions of the data, the images were ready for pre-processing before inputting them into the neural network models using transfer learning.
## 3 Methodology
### Decision Module and Feedback Connection
The receiving window and subaperture length related to the positions of targets are fed back through this connection. For a single target, initially, the module sets the receiving aperture short in the azimuth direction. Then, it is gradually increased so that the imaging result of the target is progressively obtained from coarse to fine when the decision module decides that the necessary resolution is achieved. The decision is fed back through this connection to stop the resource allocation for this target. Figure 10: CFAR detection.
## 3 Methodology
### Spatial Modeling  
In this section, we explain the architecture of the L-Spatial Encoder and the R-Spatial Encoder. GNNs are robust architectures well-suited for processing structured data, enabling the capture of complex and interrelated structural information. However, a reliance solely on GCN modules for capturing spatial features encounters limitations inherent to the characteristics of the input graph. Previous studies have highlighted the restricted expressive capacity of the message-passing paradigm. Although increasing the depth or breadth of GCNs can augment the receptive field, challenges such as optimization instability and information oversquashing persist [44; 45].  
To address these constraints, the global attention mechanism is embraced to extract spatial features not directly correlated with graph structure, thereby mitigating some of the limitations of GCNs. Additionally, it can be interpreted as a global message-passing mechanism facilitating the computation of pair-wise interactions between arbitrary node pairs. Consequently, the fusion of global attention and GCNs facilitates a comprehensive exploration of spatial features and captures implicit dependencies, such as sudden fluctuations in passenger flow at specific nodes or latent connections and long-range interactions between arbitrary node pairs. The resulting expression is depicted as follows:  
\[X=(1-\alpha)\text{DGAN}\Big{(}X^{(0)}\Big{)}+\alpha\text{GCN}\Big{(}X^{(0)},A \Big{)} \tag{2}\]  
where \(\alpha\) is a weight hyper-parameter, and \(A\) represents the adjacency matrix.