esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Modeling of Internal Pressure for a Single Factor
### Oxygen Supply System
The oxygen mass fraction in the infiltration air \(x_{g}\) is influenced by the internal-external pressure differential, as detailed below:  
\[x_{g}=\left\{ \begin{array}{ll}x_{i}&P_{i}>P_{e}\\ x_{e}&P_{i}\leq P_{e}\end{array} \right. \tag{18}\]  
where \(x_{i}\) is the mass fraction of oxygen in the fresh air, and \(x_{e}\) is the mass fraction of oxygen in the exhaust air. According to the definition of the gradient, the formula for calculating the partial oxygen pressure can be derived as follows:  
\[P_{O}(k+1)=P_{O}(k)+\frac{n_{O}(k)RT_{i}(k)}{M_{O}V(k)f_{s}}[q_{g}(k)x_{g}(k)+ q_{n}(k)x_{i}(k)+q_{w}(k)x_{e}(k)+q_{O}(k)x_{O}-N\times m_{r}] \tag{19}\]  
In Equation (19), the polytropic index \(n_{O}\) satisfies the following relationship:  
\[T_{i}(k)\cdot P_{O}(k)^{-\frac{n_{O}-1}{n_{O}}}=T_{i}(k+1)\cdot P_{O}(k+1)^{- \frac{n_{O}-1}{n_{O}}} \tag{20}\]  
Since the analysis of oxygen changes inside the carriage is based on the assumption of a variable polytropic process--where the transition between two differential time segments is continuous, smoothly moving from one thermodynamic equilibrium state to another--the polytropic index value \(n_{O}\) from the previous differential segment can be used as a substitute for ease of calculation and to save computation time, as follows:  
\[n_{O}(k)\approx n_{O}(k-1)=\frac{\ln\frac{P_{O}(k-1)}{P_{O}(k)}}{\ln(\frac{P_ {O}(k-1)}{P_{O}(k)}\cdot\frac{T_{i}(k)}{T_{i}(k-1)})} \tag{21}\]  
In Equation (21), it can be derived that: if the oxygen change process inside the carriage is isothermal, then \(n_{O}=1\); if it is an isentropic process, then \(n_{O}=1.4\); if the oxygen content inside the carriage remains constant, i.e., \(q_{g}x_{g}+q_{n}x_{n}+q_{w}x_{w}+q_{O}x_{O}-N\times m_{r}=0\), then the oxygen change is an isochoric process, where \(n_{O}=\infty\). In this case, the calculation for the partial oxygen pressure inside the carriage is:  
\[P_{O}(k+1)=P_{O}(k)\cdot\frac{T_{i}(k+1)}{T_{i}(k)} \tag{22}\]  
The required oxygen supply can be calculated based on the established partial oxygen pressure standards \(P_{osp}\). According to Equation (19), the oxygen supply is calculated as follows:
# Study on the Vertical Stability of Drilling Wellbore under Optimized Constraints
## 3 Analysis and Discussion
### Multi-Factor Sensitivity Discussion  
The sensitivity coefficient represents the relative measure of the degree of influence of uncertainty factors on evaluation indicators [34; 35]. The formula for calculating the sensitivity coefficient is \(s_{AF}=\frac{(\Delta A/A)}{(\Delta F/F)}\), where \(\Delta A/A\) is the corresponding rate of change of the evaluation indicator \(A\), and \(\Delta F/F\) is the rate of change of the uncertainty factor \(F\). The larger the absolute value of the sensitivity coefficient, the more sensitive the evaluation indicator \(A\) is to the uncertainty factor \(F\). It can be seen from Figure 5 that the critical depth of the wellbore structural instability after the adoption of optimized construction measures is affected by several factors to varying degrees. The critical depth of instability of the  
Figure 5: Effect of each factor on the critical depth of wellbore. (**a**) Effect of \(E\) and \(q_{c}\) on critical depths. (**b**) Effect of \(E\) and \(H_{w}\) on critical depths. (**c**) Effect of \(E\) and \(\triangle\) on critical depths. (**d**) Effect of \(q_{c}\) and \(H_{w}\) on critical depths. (**e**) Effect of \(\triangle\) and \(q_{c}\) on critical depths. (**f**) Effect of \(H_{w}\) and \(\triangle\) on critical depths.  
wellbore structure after the optimization measures was affected by some factors to different degrees. The sensitivity coefficients of critical depth to each factor are shown in Figure 6.  
Figure 6 shows that in descending order, the effect degree of each factor on the critical depth of the wellbore instability is the elastic modulus (0.333) > the height of counterweight water (0.278) > the self-weight of the wellbore (0.248) > the thickness of the wellbore (0.118). Additionally, the sensitivity coefficients of critical depth of the wellbore instability to the elastic modulus, height of counterweight water, and thickness of the wellbore are all positive, while the sensitivity coefficient of the self-weight of the wellbore is negative, indicating that the critical depth of the wellbore instability changes in the same direction as the elastic modulus, height of counterweight water, and thickness of the wellbore and changes in the opposite direction of the self-weight of the wellbore.
# Landmark Generation in Visual Place Recognition Using Multi-Scale Sliding Window for Robotics
###### Abstract  
Landmark generation is an essential component in landmark-based visual place recognition. In this paper, we present a simple yet effective method, called multi-scale sliding window (MSW), for landmark generation in order to improve the performance of place recognition. In our method, we generate landmarks that form a uniform distribution in multiple landmark scales (sizes) within an appropriate range by a process that samples an image with a sliding window. This is in contrast to conventional methods of landmark generation that typically depend on detecting objects whose size distributions are uneven and, as a result, may not be effective in achieving shift invariance and viewpoint invariance, two important properties in visual place recognition. We conducted experiments on four challenging datasets to demonstrate that the recognition performance can be significantly improved by our method in a standard landmark-based visual place recognition system. Our method is simple with a single input parameter, the scales of landmarks required, and it is efficient as it does not involve detecting objects.  
Keywords:landmark generation; landmark-based visual place recognition; multi-scale sliding window; uniform distribution +
Footnote †: journal: Applied Sciences
# A Tile-Based Multi-Core Hardware Architecture for Lossless Image Compression and Decompression
## 2 Compression and Decompression Algorithms Based on Hybrid Strategies  
An image compression and decompression algorithm based on hybrid strategies is proposed, combining multiple coding techniques to improve efficiency while maintaining lossless fidelity. Run-length coding, predictive coding, and non-coding strategies are integrated into the algorithm. Each method is selectively applied based on the local characteristics of the pixel data to optimize compression performance. The algorithm is designed on the premise that the image is divided into discrete tiles, which can be sized at \(4\times 4\), \(8\times 8\), or \(16\times 16\). An advantage of tile-based processing is that it is not affected by the overall image size. Regardless of the dimensions, the image is divided into smaller tiles, and compression and decompression are carried out as independent tasks for each tile. This approach standardizes the processing method for images of varying sizes while reducing overall computational complexity. It should be noted that the tile size cannot be changed within a single image. However, different tile sizes can be selected for different images.
## 3 Vehicle-Embedded Speech Recognition Front-End for Multi-UAV Control
### Voice Trigger for Multiple UAVs Control  
Generally, a trigger module operates constantly in smart devices with voice interface functions [30]. This module is used to ignore normal conversation speech or background noises while passively listening to sounds. Once it detects a user-spoken trigger word, it sends a message to a remote cloud server to activate a speech recognition engine. The user's spoken data following the word are sent to the server and recognized by the server's recognition engine. The trigger word (also referred to as a wake-word) is predefined.  
After recognizing the user's speech, the server formulates an appropriate response and sends the response message to the device. Then, the device forms a response to the user via a synthesized voice. This process allows the user to experience direct communication with the device. A standard voice interface operation using a voice triggering function is provided in Figure A3.  
If voice triggering is not provided in voice-driven UAV control systems, users should take physical actions, e.g., pressing a button or touching a display, to activate the speech recognition engine. This triggering process provides users with direct communication with the UAV, thereby increasing convenience and efficiency. In particular, triggering plays a major role in establishing a connection with a target UAV in multi-UAV environments.
## 3 Experiments
### Evaluation Metrics  
To evaluate the classification performance of the proposed model, we use \(ASCR\) as an evaluation metric, which is shown in Equation (17). The passenger flow at the same category of stations is also visualized to evaluate the performance, too.  
The three common evaluation metrics, including mean square error (\(RMSE\)), mean absolute error (\(MAE\)), and weighted mean absolute percentage error (\(WMAPE\)), are used for evaluating the prediction performance. They are defined in Equations (30) and (32). The smaller the metrics are, the better the results.  
Mean squared error (\(MSE\)) is used as the loss function in Equation (33).  
\[RMSE=\sqrt{\frac{1}{S}\sum_{i=1}^{S}(y_{i}-\hat{y}_{i})^{2}} \tag{30}\]  
\[MAE=\frac{1}{S}\sum_{i=1}^{S}\lvert y_{i}-\hat{y}_{i}\rvert \tag{31}\]  
\[WMAPE(\%)=\sum_{i=1}^{S}\left(\frac{y_{i}}{\sum_{i=1}^{S}y_{i}}\bigg{|}\frac {y_{i}-\hat{y}_{i}}{y_{i}}\bigg{|}\right) \tag{32}\]  
\[MSE=\frac{1}{S}\sum_{i=1}^{S}\left(y_{i}-\hat{y}_{i}\right)^{2} \tag{33}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Transformer Encoder** & **K-Means** \\ \hline d\_model & 2 \(\times\) DS, 2 \(\times\)_days_ & \\ Number of heads & 2 & \\ K & & 2–10 \\ Optimizer & Adam & \\ Learning rate & 0.00004 & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters used in the classification block.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameters** & **Inflow** & **Outflow** & \
\begin{tabular}{c} **Physical** \\ **Topology** \\ \end{tabular} &
\begin{tabular}{c} **Attention** \\ **LSTM** \\ \end{tabular} \\ \hline Kernel Size & 3 \(\times\) 3 & 3 \(\times\) 3 & 3 \(\times\) 3 & \\ Residual Block 1 & 32 filters & 32 filters & 32 filters & \\ Residual Block 2 & 64 filters & 64 filters & 64 filters & \\ Batch Size & 64 & 64 & 64 & 64 \\ Activation Function & Relu & Relu & Relu & Linear \\ Number of Neurons in FC 1 & \(S_{i}\) & \(S_{i}\) & \(S_{i}\) & \\ Number of Neurons in FC 2 & & & & \(S_{i}\) \\ Number of Layers & & & & 1 \\ Number of Neurons (Neu) & & & & 128 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters used in the prediction block.  
where \(y_{i}\) is the real passenger flow, and \(\hat{y}_{i}\) is the predicted passenger flow. For unclassified prediction, \(S\) is the total number of stations in the whole network. For classified prediction, \(S\) is the number of stations in the same category.  
To integrate the passenger flow prediction results for every type of station, \(RMSE\), \(MAE\), and \(WMAPE\) are redefined as Equations (34) and (36).  
\[RMSE=\sqrt{\frac{1}{S}\sum_{i=1}^{K^{\prime}}RMSE_{i}^{2}\times S_{i}} \tag{34}\]  
\[MAE=\frac{1}{S}\sum_{i=1}^{K^{\prime}}MAE_{i}\times S_{i} \tag{35}\]  
\[WMAPE=\frac{1}{\sum_{i=1}^{S}y_{i}}\sum_{i=1}^{K^{\prime}}(WMAPE_{i}\times( \sum_{i=1}^{S_{i}}y_{i})) \tag{36}\]  
where \(K^{\prime}\) is the number of categories for stations in the whole network, \(S_{i}\) is the number of the \(i\)th type of station, \(S\) is the total number of stations in the whole network, and \(y_{i}\) is the passenger number of the \(i\)th station.
# Capacity Analysis of Lattice Reduction Aided Equalizers for Massive MIMO Systems
## 5 Mathematical Analysis  
### Impact of Channel Correlation on the Capacity  
As in [45; 46], with ZF, the sub-channel capacity of a MIMO system can be represented as  
\[C_{zf}=log_{2}[1+\frac{SNR}{(H^{H}H)^{-1}}] \tag{11}\]  
Now, from [45] we have  
\[\begin{split} H^{H}H=(& R_{RX}^{1/2}GR_{TX}^{H/2})^{ H}(R_{RX}^{1/2}GR_{TX}^{H/2})\\ =& R_{RX}^{H/2}G^{H}R_{TX}^{1/2}R_{RX}^{1/2}GR_{TX}^{ H/2}\\ =& R_{RX}GG^{H}R_{TX}\end{split} \tag{12}\]  
Figure 1: PDF of Nakagami-m channel.  
Figure 2: CDF of Nakagami-m channel.  
where the correlation matrices at the \(Tx\) and \(Rx\) side are presented here by \(R_{TX}\) and \(R_{RX}\). Here, \(G\) is a i.i.d.complex Gaussian vector with zero mean and unit variance.  
Therefore \(C_{zf}\) in correlated environment can be written as  
\[C_{zfcorr}=log_{2}[1+\frac{SNR}{(R_{RX}GG^{H}R_{TX})^{-1}}] \tag{13}\] \[=log_{2}[1+SNR(R_{RX}GG^{H}R_{TX})] \tag{14}\]  
Similarly, with MMSE, the sub-channel capacity [45] can be defined as  
\[C_{mmse}=log_{2}[1+\frac{1}{(I_{N_{r}+SNR(H^{H}H)})^{-1}}] \tag{15}\]  
In high SNR condition,  
\[(I_{N_{r}+SNR(H^{H}H)})^{-1}=(SNR(H^{H}H))^{-1}[1-I_{N_{r}}(SNR(H^{H}H))^{-1}+ \mathcal{O}(SNR^{-2})] \tag{16}\]  
Therefore,  
\[C_{mmse}=log_{2}[\frac{SNR(H^{H}H)}{1-(I_{N_{r}+SNR(H^{H}H)})^{-1}+\mathcal{O} (SNR^{-2})}] \tag{17}\]  
Under the correlated channel condition, the channel capacity with MMSE receiver can be represented as,  
\[C_{mmsecorr}=log_{2}[\frac{SNR(R_{RX}GG^{H}R_{TX})}{1-(I_{N_{r}+SNR(R_{RX}GG^{ H}R_{TX})})^{-1}+\mathcal{O}(SNR^{-2})}] \tag{18}\]  
Therefore, one can compare the achievable capacity with MMSE and ZF receiver and the capacity difference [45] can be written as  
\[\begin{split}\triangle C_{corr}=log_{2}(SNR)-log_{2}[1-I_{N_{r} (SNR.R_{RX}GG^{H}R_{TX})}^{-1}+\mathcal{O}(SNR^{-2})]\\ -log_{2}[SNR+(R_{RX}GG^{H}R_{TX})^{-1}]\end{split} \tag{19}\]
# A Comprehensive Review and Benchmarking of Fairness-Aware Variants of Machine Learning Models
## 7 Experimental Procedure
### Datasets
#### 7.1.5 MBA Admission Dataset  
The MBA Admission Dataset [37] comprises 6094 instances, each representing an applicant to a Master of Business Administration program. Its primary objective is to predict admission outcomes based on a combination of academic and personal profile features. The dataset includes variables such as GMAT scores, undergraduate GPA, work experience, age, and gender, along with the binary admission outcome--0 indicating admission (900 cases) and 1 indicating non-admission (5194 cases). The sensitive attribute gender is encoded as 0 for male (2201 cases) and 1 for female (3893 cases).
# Performance Analysis in Heterogeneous Networks with Spatiotemporal Traffic and Scheduling
## 1 Introduction  
With the development of the fifth generation (5G) communication network, the increase in data traffic has increasing raised service quality requirements. The delayed performance of 5G networks is highly influenced by dynamic traffic variations. Therefore, to design systems related to packet delay or packet throughput, randomly deployed networks with queueing interactions must be accurately modelled and analyzed. To account for the actual dynamic traffic in cellular networks, stochastic geometry was combined with queueing theory to establish spatiotemporal models to capture both spatial distribution randomness and packet arrival randomness [1; 2; 3].  
[PERSON] et al. [4] bridged the gap between the work considering Poisson point process (PPP) modelling with backlogged nodes and the work on random access protocol did not consider the random spatial distributions of nodes. [PERSON] et al. [5] employed tools from queueing theory and point process theory to study the sufficient and necessary conditions for queue stability of a static Poisson network. The results were based on constant link distances and failed to capture random network topology. [PERSON] et al. [6] deduced mean packet throughput using the random scheduling (RS) strategy in small cell networks. However, the method limits the maximum number of users served per cell and ignores the user association randomness. [PERSON] et al. [7] analyzed the packet delay ina cellular heterogeneous network, considering randomly generated data traffic. They evaluated the delay performance of three scheduling schemes: RS, first-in first-out scheduling, and round-robin (RR) protocol. However, only bounds for the delay distribution were obtained and active probabilities of base stations (BSs) were fixed. The signal-to-interference-ratio (SIR) analysis and the status of the queues were not decoupled. [PERSON] et al. [8] derived the delay outage of a Poisson point processe (PPP) distributed small cell network with RS or RR being adopted. The user number per cell was fixed. Assumptions in these analyses were usually simplified. Accurate modelling is still necessary to help gain useful insights and design suitable methods.  
In the letter, a spatiotemporal model is developed in a \(K\)-tier heterogeneous network (HetNet), where the data packets' arrival of each user are initiated randomly and the BS locations in each tier are modelled as independent PPPs. The effects of bias association, scheduling schemes, and universal frequency reuse (UFR) mode are all considered to more realistically reveal the HetNet scenario. Interference interactions in the spatiotemporal HetNet are accurately captured. By adopting stochastic geometry and queueing theory, accurate expressions of mean packet throughputs of the network are derived, which can help provide insights into system designs associating with packet throughput.
## 3 Methods
### Feature Extraction for Each Image Pixel  
#### 3.2.1 Image Features of Aquaculture Areas  
Only aquaculture ponds show regular shapes and textures on the Sentinel-2A multispectral images (Figure 3), because they are usually very large compared with medium-resolution image pixels. The spectral signatures of water ponds are also clearly presented in NDWI image, and texture features are clearly presented in all optical, spectral indices and SAR images.  
Mariculture facilities do not show obvious signatures in Sentinel-2A multispectral optical images, because they are relatively small compared with medium-resolution image pixels, or some are submerged in water. However, these aquaculture types show significantly different characteristics in NDWI, NDVI and NDBI images, because normalized difference image features amplify the differences. More specifically, the textures are very clear in Sentinel-1 SAR images, because the backscatter signature of the radar is sensitive to the roughness and structure of water surface.  
Based on the above analyses, we found that NDVI, NDWI, and NDBI could better highlight the differences between aquaculture areas and other water bodies and the textures derived from index images; VV and VH images could further enhance the differences.
# Comprehensive Review of Robotics Operating System-Based Reinforcement Learning in Robotics
## 3 Summary of ROS-Based RL Applications
### Notable Works Using On-Policy
#### 3.1.9 Navigation II  
In [67], the authors present an online Control Barrier Function (CBF) for decentralized multiagent navigation (for more information about the CBF, please refer to [89]), allowing agents to adjust their position based on the changing environment settings while ensuring safety. This approach highlights the development of decentralized controllers, empowering each agent to independently make decisions based on its local perspective, ensuring safe and efficient navigation within a multiagent system. This work enhances multiagent navigation by addressing one of the main problems with traditional CBFs, i.e., the dependence on _\"fixed parameters\"_, which does not work well in complex and changing environments. This is especially vital for practical applications, as the fixed predetermined actions are often inadequate.  
The authors in [67] used GNNs to establish a CBF tuning strategy by using the PPO RL algorithm. It is challenging to explicitly model the intricate relationship between CBF parameters and the success of navigation in a dynamic, multiagent environment. The ROS is the communication middleware to facilitate the exchange of information on the states and surrounding environment of robots; sharing this information is vital for decentralized control. The ROS allows each robot to navigate based on its local perception and communication with agents. For more information on the hardware and software used, please see Table 4.  
The above-mentioned work introduces a novel method that improves the implementation of the CBF in multiagent applications. This technique mitigates the parameter estimation, enabling safer and more efficient navigation in dynamic environments. The use of GNNs for decentralized control provides scalability and resilience. Simulation and physical demonstrations of robots show the effectiveness of an online CBF in handling multiagent scenarios that traditional methods struggle to address. The work uncovers new opportunities for autonomous systems to operate in complex and unpredictable environments.
## 5 Conclusions  
We have demonstrated 100 Gb/s/\(\lambda\) PAM-4 TDM-PON downstream and upstream transmission at O-band based on EML + SOA transmitter and SOA + PIN receiver, and PR-30 power budget requirement is reached after 25 km fiber transmission. To reduce the complexity of ONUs, different DSP algorithms are utilized for downstream and upstream transmission. For downstream transmission, compared with only ONU-side 100-tap FFE, the introduced 3-symbol LUT pre-compensation at the OLT side effectively reduces the pattern effect induced by SOA booster and pre-amplifier, which contributes a 0.8 dB receiver sensitivity improvement and >3 dB dynamic range extension. For upstream transmission, without any ONU-side pre-compensation and pre-equalization, >3 dB dynamic range is improved with the help of the 2 nd-order nonlinear part of the OLT-side VNLE by contrast with linear FFE. The power budget can be further improved by using an SOA with higher saturation power as a booster or by applying DSP with higher complexity at the transceiver sides.  
Conceptualization, Z.L.; Data curation, Y.L.; Formal analysis, Z.L.; Funding acquisition, Z.L.; Investigation, Z.L.; Methodology, Y.L. and S.L.; Project administration, Z.L.; Resources, Y.S.; Software, Y.L.; Supervision, Y.S.; Validation, Y.L. and F.Y.; Visualization, Y.L. and Y.W.; Writing--original draft, Z.L.; Writing--review & editing, Z.L. and Y.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by National Key Research and Development Program of China (2021 YFB2900800), the Science and Technology Commission of Shanghai Municipality (Project No. 20511102400, 20 ZR1420900) and 111 project (D20031).  
The authors declare no conflict of interest.