esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on Congestion Control for RPL-Based Wireless Sensor Networks
## 2 Overview of RPL
### Objective Function and Routing Metrics in RPL  
RPL supports both static and dynamic metrics. RPL also supports constraint-based routing. If a link or a node does not satisfy a required constraint, it is pruned from the candidate neighbor set. RFC6551 [13] specifies a set of routing metrics and constraints suitable for RPL.  
An objective function (OF) defines how a node computes the rank and selects its preferred parent. The rank is computed using routing metrics, optimization objectives, and related functions. Each RPL instance is associated with an OF specialized for it. The RPL standard described in RFC 6550 [1] does not suggest any specific OF. Instead, there are separate specifications defining objective functions. RFC 6552 [14] defines the Objective Function Zero (OF0) designed as a default OF that allows interoperation between implementations in a wide spectrum of use cases. OF0 is designed to find the nearest DODAG root that provides connectivity, though it does not guarantee path optimization with respect to a specific metric. The OF0 selects a preferred parent and a backup feasible successor if one is available. The minimum rank with hysteresis objective function (MRHOF) described in RFC 6719 [15] selects routes that minimize a metric, while using hysteresis to reduce churn in response to small metric changes. MRHOF works with additive metrics along a route. Typical examples of metrics for MRHOF include latency metric and expected transmission count (ETX) metric, i.e., the expected number of transmissions required to send a packet over a link, including retransmissions. For example, if MRHOF is used with a latency metric, RPL tries to find a minimum latency path from the node to a DODAG root. In the absence of a metric in a DAG metric container, MRHOF is supposed to use ETX metric by default to find a minimum ETX path from the node to the root. When finding a path with the minimum rank, MRHOF switches to that minimum rank path only if it is shorter than the current path by at least a given threshold.
## 5 Simulation Study  
The performance of the suggested Bayesian HEWMA control chart under RSS schemes is evaluated through the Monte Carlo simulation method based on the ARL and SDRL measures. The different smoothing constants \(\lambda_{1}=0.10\) or \(0.25\), and \(\lambda_{2}=0.05\) is used to check the effect of the proposed Bayesian HEWMA control chart using RSS schemes under different LFs, at the specified in-control process 370. The simulation steps for the suggested Bayesian HEWMA control chart are given below:  
**Step 1: Setting in-control ARL**  
* The sampling and prior distribution are taken as normal distribution, determine the mean and standard deviation for various LFs, i.e., \(E\Big{(}\hat{\theta}_{(LF)}\Big{)}\) and \(\hat{\delta}_{LF}\).
* We choose the specified value of smoothing constants \((\lambda_{1},\lambda_{2})\), for a fixed value of \(ARL_{o}=370\).
* Generate the different ranked set sampling schemes of size \(n\) from an in-control process from a normal distribution.  
* Compute the proposed Bayesian HEWMA statistic and evaluate the process according to the suggested method.
* If the process appears to be in-control, then repeat the above three steps until the process is stated as out-of-control, and record the number of run-length for the in-control process.  
**Step 2: For out-of-control ARL**  
1. Select the ranked set sampling schemes from a normal distribution for the shifted process, i.e., \(X\sim N\left(E\left(\hat{\theta}_{LF}\right)+\sigma\frac{\delta}{\sqrt{n}}, \delta\right)\).
2. Compute the suggested Bayesian HEWMA statistic and evaluate the process according to the design.
3. Repeat the above two steps if the process is stated to be in-control, and record the run length of the in-control process.
4. Repeat steps (i-iii) 100,000 times and calculate the ARL and SDRL.
## 3 System Design
### System Overview  
Figure 1 shows a simple hierarchical cache system based on a hierarchical NRS with 3 layers. There are two crucial components in a cache system--NRS and cache-supported router. An NRS provides a basic service of name resolution and divides a network into plenty of hierarchical nested areas (HEAs) where at least one dedicated RN is deployed. Content sources (including cache-supported routers and content publisher) in network register the mapping of its NA and content name in the NRS. When users request the NDO, it firstly asks RN that belongs to the lowest HEA for the NA where the content located. The query does not stop forwarding to the upper level RN until it is satisfied in some RN. This name resolution process can be seen as a request of the NDO to travel from routers in given lower level HEAs towards routers in the upper level HEAs until the requested NDO is found, which makes an ICN cache system implicitly form a hierarchical structure. To make an ICN cache system compatible with IP, we implemented ICN by overlaying it onto IP and identifying IP (IPv4 or IPv6) addresses as content locators, i.e., NA. A 160-bit flat entity ID (EID) was employed as the content name due to its characteristic of self-verification. ICN routers need to be able to process both IP and ICN flows (including routing and caching the NDO), as well as to make caching decisions locally. Due to the substantial overhead brought from small-sized chunks [31], we set the size of the chunk transmitted in network to up to a few MBs.
## 2 System Model
Based on Equation (3), the possible rate for far user could be expressed as [18]:  
\[R_{f}=\log_{2}\left(1+\frac{\left|h_{f}\right|^{2}P_{t}\alpha_{f}}{\left|h_{f }\right|^{2}P_{t}(\alpha_{n}+\alpha_{m})+\sigma^{2}}\right) \tag{4}\]  
Likewise, the attainable bit rate for middle user can also be expressed as:  
\[R_{m}=\log_{2}\left(1+\frac{\left|h_{m}\right|^{2}P_{t}\alpha_{m}}{\left|h_{ m}\right|^{2}P_{t}(\alpha_{n})+\sigma^{2}}\right) \tag{5}\]  
Typically, the near user has good channel condition along with BS; thus, their signal \(x_{n}\) is assigned low power, and therefore, the received signal for the near user can be shown as:  
\[y_{n}=xh_{n}+z_{n} \tag{6}\]  
\[y_{n}=\sqrt{P_{t}\alpha_{n}}x_{n}h_{n}+\sqrt{P_{t}}\Big{(}\sqrt{\alpha_{m}}x_ {m}+\sqrt{\alpha_{f}}x_{f}\Big{)}h_{n}+z_{n} \tag{7}\]  
The first term in Equation (6) represents the near user expected signal, while the second term denotes the interfering term from middle and far users. On the other hand, it can be observed from Equation (6) that the interfering term is predominant due to the additional power that can be assigned to the far user. Therefore, at the near user side, SIC is performed, where immediate decoding for far user signal \(x_{f}\) is accomplished, then removed from the composite signal. Next, the middle user signal \(x_{m}\) is decoded and removed from the remaining signal. Finally, the near user achieved rate \(R_{n}\) can be expressed as [18]:  
\[R_{n}=\text{log}_{2}\Bigg{(}1+\frac{|h_{n}|^{2}P_{t}\alpha_{n}}{\sigma^{2}} \Bigg{)} \tag{7}\]
## 1 Introduction
The results indicated that appropriate delay times can effectively control blasting damage, but setting the delay time too high yields poor results. [PERSON] et al. [18] proposed a modal-based method for assessing the damage caused by explosions to reinforced concrete columns based on LS-DYNA numerical simulation experiments and field tests. [PERSON] et al. [19] studied the cumulative damage characteristics of the reserved surrounding rock after cyclic blasting in the Jinjing Tunnel using acoustic wave tests and numerical simulations. Currently, the acoustic wave testing method remains the most commonly used and efficient testing technique for studying the damage caused by blasting to rock masses [20; 21; 22; 23]. Although relatively thorough research on the optimization of pre-splitting blasting parameters and the control of rock mass damage has been performed both domestically and internationally, systematic studies on the specific impact of hole-by-hole pre-splitting blasting on the damage caused to reserved rock masses are lacking. To improve the pre-splitting blasting effect at the Niukutou open-pit mine in the alpine region of Qinghai Province and reduce the damage caused to the reserved rock mass, this study combines numerical calculations with field experiments. Using the ANSYS/LS-DYNA finite element software, the influence of different delay times on rock mass damage was investigated, and the patterns of rock mass damage caused by hole-by-hole pre-splitting blasting were explored. An optimal combination of pre-splitting blasting parameters suitable for the site was determined. Finally, the parameter combination determined by the numerical model was verified through field experiments and acoustic wave testing. These research findings can provide a theoretical basis and technical support for the optimization of pre-splitting blasting technology in alpine regions.
## 4 Numerical Results
### Spectral Efficiency
It can be seen from the Figure that the channel capacity of heterogeneous and orthogonal schemes combined with a transmission scheme is superior to orthogonal SSA and traditional SSA. This advantage is more obvious at the stage of medium to high signal-to-noise ratios. The signal space orthogonal scheme further exploits the potential of signal space, expands the Euclidean distance between constellation points, and improves the decoding reliability. Under the same conditions, a higher-order modulation scheme can be used to further improve frequency band utilization. By defining the protection area to ensure the power-off performance of the GEO system, ref. [30] calculated the protection area that the cognitive user (ground system or NGEO system) can transmit, which greatly improved the performance compared with a traditional SSA [25]. This improves the overall performance of the system. As can be seen from the Figure, the optimal selection of alignment direction can also further improve the performance. Figure 9: Performance difference between different methods for determining alignment direction.
## 4 Results
### Learning Convergence Analysis  
Within the reinforcement learning framework, convergence of the learning process occurs when the agent's reward function stabilizes and no longer undergoes significant changes over time, signifying that the agent has acquired the optimal policy.  
As shown in Figure 8, this graph illustrates the learning convergence of the MPNN-DQN-based interstellar handover algorithm proposed in this paper. It is evident that the average reward attained by the agent gradually increases as training progresses, and it tends to stabilize after approximately 1700 episodes. This stabilization indicates that the learning process has converged to the optimal handover scheme.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline UE position (Latitude, Longitude, Altitude) & (\(-62^{\circ}\), \(50^{\circ}\), 0 m) \\ Simulation time (minutes) & 30 \\ Number of total time slots & 60 \\ Number of total satellites providing coverage & 15 \\ Satellite altitude (km) & 400–600 \\ Minimum coverage elevation angle & \(10^{\circ}\) \\ Simulation starting time & \(1\) May 2023 09:30 a.m. (UTC) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of LEO satellite mobility environment parameters.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Discount factor & 0.95 \\ Learning rate & 0.001 \\ Experience replay pool size & 4000 \\ Initial exploration rate & 1 \\ Termination of exploration rate & 0.005 \\ Training batch size & 32 \\ Q-target network parameter update step size & 50 \\ (episodes) & 1600 \\ DQN iterations & Mean-Squared Error (MSE) \\ Optimizer & Stochastic Gradient Descent (SGD) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of DQN framework parameters.
# Application of Compressive Sensing in the Presence of Noise for Transient Photometric Events
## 2 CS Architecture
A sample reference image (\(x_{r}\)), a sample observed image (\(x_{o}\)), and a sample differenced image (\(x_{diff}\)) are shown in Figures 2-4, respectively. \[x_{diff}=x_{r}-x_{o} \tag{2}\]0 Sample reference image. Once we have a differenced image, we can select the group of pixels that represent a source star experiencing a microlensing event, and plot the magnification over time, to obtain a photometric light curve. In our result analysis, we perform error calculations using this photometric curve over time.
## 4 Results and Discussion
### Discussion  
The proposed method in this paper is based on a similarity analysis. The key steps are feature vector extraction and the selection of weighted distance. The number of fault cases and the quality of maintenance records influences the feature vector extraction greatly. One year of maintenance records are used in this paper, and the number of repeated faults is relatively few. The method performs better with more fault cases; it can be self-optimizing with more fault cases in the fault-template database.  
If the similarity between an online alarm list and each feature vector is small, we think that the root fault of this online alarm list is unknown. There are two reasons for this situation. First, the root fault is not in the fault-template database. Second, the available fault cases of this fault are relatively small. The coupling correspondence between the fault and its alarm lists is not well established. However, the identification process is not over. In this case, manual maintenance is needed, and the fault-template database is updated according to the maintenance results. In the later identification process, the identification accuracy is improved with new and more fault cases. This self-optimizing process is shown in Figure 4.  
Choosing a suitable distance measure also increases the overall performance of the proposed method. Other distances, aside from Euclidean distance and Hamming distance, can also be used in the similarity measure.
## 4 Results  
### GPP Estimation Using the Light Use Efficiency Model  
#### 4.1.1 Analysis of the GPP-SIF Correlation and Stability of Fitting Coefficients  
Based on the scatter plot of GPP data from the Huoditang forest area (2003-2010) and the corresponding GOSIF data for the respective latitude and longitude (as shown in Figure 2), a strong linear correlation is observed between GPP and SIF, with a correlation coefficient (R\({}^{2}\)) of 0.63.  
The fitting coefficient S was calculated using the GPP data from the Huoditang forest area (2003-2010) and the corresponding GOSIF data. The coefficient S was then plotted as a time series for each year (as shown in Figure 3). The analysis revealed that the fitting coefficient S increased from January to March, reaching its peak value around March. It then declined, remained relatively stable from June to September, increased steadily from September to November, and decreased again from November to December.  
Given the significant monthly variations in fitting coefficients across different years, the monthly average fitting coefficients from 2003 to 2010, along with the fitting coefficient values for the same months in different years, were plotted together (as shown in Figure 4). The analysis revealed that, from June to September between 2003 and 2010, the interannual variation in the fitting coefficient S between GPP and SIF was relatively small. This suggests that the photosynthetic efficiency of the forest remains more stable during the growing  
Figure 3: Monthly trends in fitting coefficients (2003–2010). Note: Lines of different colors represent the various years between 2003 and 2010, and the different points indicate the values of the fitting coefficient S for each year in different months.  
Figure 2: Relationship between SIF and GPP. Note: The black dots represent the scatter plot of SIF versus GPP, and the blue line represents the linear regression line between the two.  
season (June to September). Consequently, it is recommended to focus research efforts specifically during the vegetation growing season (June to September).
## 12 Conclusions  
In this paper, data routing goals of the existing approaches are categorized into three main types: energy conservation, fast delivery and fault-tolerance. Moreover, the most famous and recent data routing approaches based on their attitudes and features are reviewed and compared in each goal category. According to this overview, most of the proposed data routing algorithms consider the energy of the nodes as an unavoidable factor in proposing a data routing approach in WSNs (HGMR, M-SPIN). Some of them consider delays (TEEN, EAR) and a number of them aim to provide fault tolerance (ARF, DMRF). However, by increasing the application of WSNs, the functions of sensor nodes will be so highlighted and they may be requested to do more complicated task. Therefore,  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline Protocol & Classification & NO of & Mobility & Durn & Energy & Fast & Fault \\  & & Stinks & & Aggregation & (Conversation & Delivery & Tolerance \\ \hline M-SPIN [44] & Data-centric & 1 & Possible & Yes & \(\surd\) & & \\ DD [10] & Data-centric & 1 or & Limited & Yes & & & \(\surd\) \\  & & more & & & & & \\ Hybrid & Hierarchical & 1 & Possible & Yes & \(\surd\) & & \\ Clustering [60] & & & & & & & \\ EAR [48] & Data-centric & 1 or & No & No & & \(\surd\) & \\  & & more & & & & & \\ SHM [71] & Data-centric & 1 & No & Yes & & & \(\surd\) \\ LEO [47] & Data-centric & 1 & No & Yes & & \(\surd\) & \\ HGMR [62] & Location-based & 1 & No & No & \(\surd\) & & \\ TEEN [14] & Hierarchical & 1 & No & Yes & & \(\surd\) & \\ /APTEEN [64] & & & & & & & \\ DMRF [75] & Data-centric & 1 & No & No & & & \(\surd\) \\ GEAR [1] & Location-based & 1 or & Limited & No & \(\surd\) & & \\  & & more & & & & & \\ DGMA [68] & Hierarchical & 1 & Yes & No & & \(\surd\) & \\ RAG [65] & Data-centric & 1 & No & Yes & & \(\surd\) & \\ GAF [20] & Location-based & 1 & Limited & No & \(\surd\) & & \\ ARF [73] & Data-centric & 1 & No & No & & & \(\surd\) \\ EAGR [19] & Location-based & 1 & Limited & No & \(\surd\) & & \\ Fault tolerant & Hierarchical & 1 & No & Yes & & & \(\surd\) \\ Design [70] & & & & & & & \\ QoS-aware & Hierarchical & 1 & No & Yes & & \(\surd\) & \\
[67] & & & & & & & \\ GESC [52] & Hierarchical & 1 & No & Yes & \(\surd\) & & \\ Energy- & Hierarchical & 1 & No & Yes & & & \(\surd\) & \\ efficient and & & & & & & & \\ Fast [12] & & & & & & & \\ EARS [69] & Data-centric & 1 or & Limited & Yes & & & \(\surd\) \\  & & more & & & & & \\ \hline \end{tabular}
\end{table}
Table 1: Features and goals of data routing approaches.  
scalable solutions which can perform data routing by considering multi-objective QoS requirements is greatly required for WSNs.
# Empowering Healthcare: TinyML for Precise Lung Disease Classification
## 2 Related Works
Utilizing features such as chroma, RMS, Spectral centroid, Rolloff, and MFCCs, the ConvNet model demonstrated impressive performance metrics, including 99.8% accuracy, 100% sensitivity, 100% specificity, and a 99% F-score. In [33], the authors proposed RDsLINet, a novel lightweight inception network for classifying a broad spectrum of respiratory diseases through lung sound signals. The framework involves preprocessing, melspectrogram image conversion, and classification via RDsLINet. The proposed RDsLINet achieved impressive accuracy rates: 96% for seven-class, 99.5% for six-class, and 94% for healthy vs. asthma classifications. In [34], the authors proposed a novel approach to respiratory disease detection through a wearable auscultation device. They developed a Respiratory Sound Diagnosis Processor Unit (RSDPU) utilizing LSTM networks to analyze respiratory sounds in real time. The study highlights the implementation of Dynamic Normalization Mapping (DNM) to optimize quantization and reduce overfitting, crucial to maintaining model accuracy with limited computational resources. The hardware implementation of the RSDPU includes a noise corrector to enhance diagnostic reliability. The results show that the RSDPU achieved an 81.4% accuracy in abnormality diagnosis, with a minimal power consumption of 381.8 \(\mu\)W. The study demonstrates the potential of combining advanced machine learning techniques with efficient hardware design to create effective and practical healthcare solutions for continuous respiratory monitoring  
A startup called Respira Labs [35] has introduced an innovative, cost-effective wearable sensor that leverages TinyML to analyze cough sounds for signs of respiratory diseases like pneumonia. This compact device integrates a microphone and a microcontroller executing a neural network to discern specific cough characteristics such as wheezing and crackling. Designed for ease of use, it features a simple strap mechanism, operates without batteries, and communicates results via LED indicators and sound signals. Table 1 summarizes existing research on lung disease detection and classification using audio data, covering diseases such as asthma, COPD, lung fibrosis, bronchitis, and pneumonia and various pathological lung sounds. Datasets vary from publicly available ICBHI 2017 to self-collected data, indicating diversity in data sources. The extracted audio features include STFT, MFCC, and spectrograms, with MFCC being the most common. The models used range from ANN, CNN, and LSTM, to hybrid CNN-LSTM, achieving high accuracy rates and mostly deploying solutions on cloud platforms. While some studies [33; 34] have explored computation directly on edge devices, our work leverages TinyML to push the boundaries of what can be achieved on resource-limited hardware. This approach allows for efficient real-time analysis and model deployment on compact, low-power devices, making advanced diagnostics more accessible in a wide range of settings.
# Literature Review of Digital Twins Applications in Construction Workforce Safety
## 5 Sensor Technology in Construction Safety
### On-Site Individual Positioning  
Even if a hazard area can be displayed for managers, it is still difficult to determine whether people are entering the hazardous area [53]. Because occupational risk only exists when the workforce is present, monitoring the position of on-site individuals becomes particularly important. At present, two kinds of mainstream positioning method are being used. One is the sensor-based positioning methods, including wireless networks, RFID, UWB, and GPS. He and [PERSON] [54] used GPS to locate an outdoor individual and an object being hoisted by a crane to warn the individual who was about to step into the danger area under the object, avoiding falling injuries. The other kind is vision-based positioning, which is based on computer vision technology [55]. Vision-based positioning method relies on cameras to obtain images or videos. Then, computer vision algorithms are used to identify and track on-site individuals or other objects [56; 57]. The on-site position can be digitised by mapping the object in images/videos to the actual location (Figure 6). Combined with individual positioning and the aforementioned construction environment monitoring, DT has made remarkable contributions to avoiding collisions. The method has been proven to be useful for detecting collision hazards between individuals and vehicles [58] and between individuals and heavy machinery [59]. By installing cameras around equipment, equipment operators can obtain the location information of surrounding workers and avoid collisions.