esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
* Define and utilize three types of parameters to create a reward function in which the states adaptively switch from current to new states, and the agent learns from the traffic condition and plays a vital role in changing these states. * Implement the proposed ASC-RL in Python and check its applicability with various parameters such as joint Gaussian distributions, event correlations, prediction accuracy, and energy efficiency with a combined reward score. Finally, a comparative analysis was performed with the detection and transition probabilities, false alarm probabilities, and transmission success rate. **Problem Statement and Motivation:** Cognitive sensors often create massive amounts of data with redundant and repeated bit values. Sensing this redundant and useless data and broadcasting not only requires a good amount of energy, but it also affects many other performance parameters such as reconfiguration, delay, latency, and packet loss. To address these challenges, we need a new method to check and adjust the state of cognitive sensors according to the roles and functions within these networks. A reinforcement learning-based approach is useful for dynamically switching states and adjusting sensors. Here, we propose a novel reinforcement learning-based mechanism called \"Adaptive Scheduling in Cognitive IoT Sensors for Optimizing Network Performance using Reinforcement Learning\". There are three types of parameters: internal parameters (states), environmental parameters (sensing values), and history parameters (energy levels, roles, and number of switching states). The mechanism aims to reduce extensive sensing, minimize costly processing, and control frequent communication, as well as reduce network traffic and optimize network performance in terms of network energy. The rest of this paper is arranged as follows: Section 2 is the related work in RL-based approaches with cognitive IoT networks; Section 3 is the preliminaries and basics of ASC-RL; Section 4 is the details of the system model of ASC-RL; Section 5 is the simulation parameters and concerned metric values that are discussed; Section 6 is the evaluation part; Section 7 compares the proposed scheme with other schemes; and finally, the paper is concluded in Section 8.
## 3 Materials and Methods  
### Database  
This study retrospectively examined the clinical data of 304 patients diagnosed with voice disorders at the Department of Otorhinolaryngology, Nowon Eulji Medical Center, Eulji University, between March 2020 and January 2024. Ethical approval was obtained from the Institutional Review Board (IRB No. 2022-04-014), and all procedures adhered to research ethics standards. After excluding individuals who discontinued voice therapy or were lost to follow-up, a final cohort of 102 patients was included for analysis. For acoustic data collection, participants were instructed to sustain the vowel sound /a/ at a comfortable pitch and loudness, with a microphone positioned 5 cm from the mouth [25]. All recordings were conducted in a sound-treated room to minimize environmental noise and ensure consistent recording conditions. A high-quality condenser microphone was used for data acquisition, and calibration was performed prior to each recording to maintain signal consistency. Participants were seated in an upright position and asked to sustain phonation for at least 5 s to ensure signal stability for acoustic analysis. All recordings were supervised by experienced speech-language pathologists to ensure strict adherence to the protocol. The resulting voice samples were digitized at a sampling rate of 44.1 kHz and analyzed using the Multidimensional Voice Program (MDVP) software version 2.3 developed by Kay Elemetrics [26]. The acoustic data were further subjected to quality control, and recordings with excessive background noise, voice breaks, or irregular phonation were excluded from analysis.  
Table 1 provides an overview of the dataset used in this study. The dataset comprises recordings of the sustained vowel /a/ from 63 female and 35 male participants, each diagnosed with 1 or more of over 19 distinct voice pathologies. In addition to acoustic recordings, the dataset includes demographic and clinical variables such as gender, smoking status, alcohol consumption, voice usage status, coffee consumption, comorbidities, surgical history, and voice training status (including the number of training sessions). Furthermore, it contains acoustic parameters measured both before and after voice disorder therapy, including fundamental frequency (F0), jitter, shimmer, SFF, noise-to-harmonics ratio (NHR), and maximum phonation time (MPT).
# An Iterative Closest Point Method for Lidar Odometry with Fused Semantic Features
## 5 Results
### Analysis of FtF Matching and FtS Matching  
In Section 3, the LO algorithm system is noted to consist of three parts. The relationship in the second part involves point cloud registration between adjacent frames, which is now referred to as frame-to-frame (FtF) matching. The matching in the third part is termed frame-to-submap (FtS) matching. We use two threads to calculate the odometry, one performing FtF matching and the other performing FtS matching. First, a less accurate relative pose is calculated by FtF matching, and then a more accurate FtS matching uses the FtF calculated poses as initial assumptions to obtain the final LO.  
In Figure 4a-d, the trajectory represented by the green line indicates the ground truth, whereas the trajectory represented by the red line is generated during FtF matching. In contrast to Figure 4a, the FtF matching incorporating semantic features is illustrated in Figure 4b. Within the light green rectangle, it can be observed that the pose calculated through the semantic FtF matching process exhibits a higher degree of alignment with the true pose compared to Figure 4a. This, in turn, provides a more favorable initial hypothesis for the FtS matching process. A similar set of comparisons is also depicted in Figure 4c.d. The trajectory obtained through executing FtS matching represents the final trajectory of LO, as shown in Figure 4e-h.
# Crowdsourced Evaluation of Robot Programming Environments: Methodology and Application
## 1 Introduction  
The \"fourth industrial revolution\" or Industry 4.0 and its embodiment as the \"digital factory\" depicts the softening of limits between the physical, digital, and natural universes. Industry 4.0 combines advances in AI, IoT, robotics, 3D printing, genetic engineering, quantum computing, and other technologies. One of the pillars of Industry 4.0 is represented by human-robot collaboration. This form of collaboration requires extensive research, which is not focused on robotics alone but also on user-centered, simplified robot programming as a strong foundation for smooth human-machine interaction (HRI) [1].  
The field of industrial robotics has progressed in the past decade, with companies looking to embrace this technology in diversified areas as it brings a high return on investment in a short time, increases productivity and velocity, and reduces the risk of human error caused mainly by the monotony and repetitiveness of tasks. Robots, in contrast to human workers, would mean a single more considerable initial investment in the hardware, which can later be employed 24 h/day, keep the entire machinery operating even without the physical presence of human workers, master repetitive tasks quickly, and even specialize in customizable ones.  
Regarding customization, the ever rising demand for lot size 1 products is challenging this approach mostly from the software point of view, task changes within one industrial robot's timeline being rare [2]. The complexity and high costs of programming and re-programming the robot are some of the main reasons for the low flexibility in these changes [3]. This emphasizes the relevance of creating a simple yet powerful and universally applicable robotic programming environment.
# Transaction Mechanism Based on Two-Dimensional Energy and Reliability Pricing for Energy Prosumers
## 3 ERT Implementation Issues
### Prosumer Side
#### 3.2.2 Reliability Band Selection  
To select a reliability band, the reliability of the forecasted value at each operational time horizon is required. Conventional forecasting methods, such as CBL, do not provide the value nor provide it as a probabilistic average. The reliability of the forecasted value is related to the accuracy of the demand forecasting. Figure 5 shows the relationship between the gradient of forecasting and the accuracy of BPA's wind generation [26; 27]. Prosumers can estimate the reliability as an example method,  
\[\hat{\gamma}_{t}=\kappa|\hat{e}_{t}-\hat{e}_{t-1}|, \tag{8}\]  
and, substituting (8) into (4), they select the reliability band.  
Similar to the internal control for demand forecasting, the reliability is also enhanced using additional control afforded by energy storage. The selection of a high-reliability band with additional control enhances potential benefits. However, this increases both the operational burden of prosumers and their costs from additional equipment (such as energy storage).
# Path Segmentation-Based Hybrid Caching in Information-Centric Networks
## 4 Performances Evaluations
### Simulation Result
#### 4.3.1 Impact of Skewness (\(\alpha\))  
We change the size of skewness (\(\alpha\)) to observe the impact on cache hit ratio and average access latency in three network topologies. In this part of the experiment, the cache size ratio is set to 0.04. The results are shown in Figure 4.  
A larger skewness (\(\alpha\)) means more requests from users for popular content. As shown in Figure 4a-c, the cache hit ratio increases with skewness (\(\alpha\)). The cache hit ratio is related to network resource utilization [8]. In the three network topologies, LCE causes cache redundancy, and the hit ratio of LCE is the lowest. CL4M caches content on nodes with the greatest betweenness centrality on the data return path, which results in an uneven distribution of content, so the cache hit ratio of CL4M is also relatively low. LCD caches content in the next-hop node of the hit node, and ProbCache caches content probabilistically according to data transmission distance and caching space available on the return path. The hit ratios of these two strategies are relatively close. PSBC has the highest cache hit ratio because popularity is the main factor for PSBC's caching decision, and the content migration mechanism improves the utilization of network resources. For example, when skewness (\(\alpha\)) is 0.8, PSBC's cache hit ratio in GEANT topology is 48%, 40%, 18%, and 22%  
Figure 4: Cache performance with different skewness values (\(\alpha\)).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Caching Strategy** & **Limitations** \\ \hline LCE & Causing cache redundancy and \\ underutilization of cache space \\ \hline LCD & Causing excessive eviction operations and \\ maximum utilization of bandwidth \\ \hline ProbCache & Differences in content popularity are \\ not considered \\ \hline CL4M & Causing load imbalance between nodes and \\ underutilization of cache space \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of limitations of the contrastive caching strategies.  
higher than LCE, CLAM, PROB_CACHE, and LCD, respectively. As shown in Figure 4d-f, the average access latency decreases as skewness (\(\alpha\)) increases. In PSBC, nodes close to users focus on caching popular content. We make a temporary area division for each data return path, which takes different users' access locations into account. The results show that the average access latency of PSBC is lower than the other four caching strategies under different skewness values (\(\alpha\)).
###### Abstract  
In view of the common U-shaped apron structure of large- and medium-sized airports at home and abroad, this study considered the optimization design and performance evaluation of the U-shaped apron operation procedure. First, by analyzing the physical structure characteristics and traffic operation characteristics of the U-shaped area, exclusive, partition-shared, and global-shared operation procedures of the U-shaped area were designed, and differentiated apron-operation rules and traffic models were constructed for different types of operation procedures. Then, from the perspectives of safety, efficiency, and environmental protection, a multi-dimensional evaluation index system of U-shaped area operation performance is established, and a classification measurement and comprehensive evaluation method based on critique is proposed. Finally, a traffic simulation model was established based on airport network topology modeling. We used Monte Carlo methods for the simulation in Python 3.6, and the experimental results show that, in the scenario of high-density traffic operation, compared with exclusive and partition-shared procedures, the implementation effect of the global shared procedure is very significant, and the apron operation capacity increased by 14.8% and 5.0%, respectively. The probability of aircraft conflict decreased by 32.2% and 11.8%, respectively, and the time of single conflict relief decreased by 16.1 s and 10.6 s, respectively. The average resource utilization in each U-shaped area increased by 66% and 25%, respectively, while the average daily carbon emissions of a single aircraft were reduced by 16.7 kg and 11.0 kg and the average daily fuel consumption of a single aircraft were reduced by 3.6 kg and 2.4 kg, respectively. The proposed method is scientific and effective and can provide theoretical and methodological support for optimizing the configuration of the scene operation mode of complex airports and for improving flight operation efficiency.  
air transportation; airport operation model; airport simulation; operation procedure design; performance evaluation; U-shaped area +
Footnote †: journal:  
0000-0001-1000-1000-1000-1000-1000-1000-1000-1000-1000-1000-1000-1000-1000-1000-1000-1000-10  
multi-level business entities, serving \"passengers-cargo\". In the apron areas of large airports, the conflict between the highly intensive arrival and departure traffic and the relatively limited apron capacity is becoming more pronounced, resulting in low levels of safety and efficiency in aircraft operations. From the perspective of airport ground service coordination, inefficient and disruptive aircraft operations further constrain the efficiency of the coordinated dispatch of operators and vehicles. In this context, how to adopt scientific means to optimize the design of apron operation procedures, scientifically evaluate the performance of apron operations, and enhance the safety, efficiency, and greenness of apron operations as far as possible has become a key challenge that needs to be addressed.
# A Survey on Recent Advanced Research of CPS Security
## 3 Reviews
### Trusted Computing
[PERSON] and [PERSON] [148] proposed a novel consensus algorithm, RATCHETA, which increases the maximum tolerable faulty nodes to 50% and lowers the message complexity. RATCHETA adapts a hybrid fault model, which assumes a more trustworthy subsystem among the less trusted parts, to prevent equivocation, such as sending inconsistent messages to different recipients. Moreover, it also guarantees an upper bound of the memory usage and message size and uses UDP multicast to avoid packet loss detection and retransmission. The authors implemented RATCHETA with its trusted subsystem built on top of ARM TrustZone, and the experimental results show that with 20% message omissions, it costs less than one second on average to reach a consensus on a 10-node group. Contego-TEE [27] is a framework that prevents control spoofing attacks on a real-time embedded platform. It uses a trusted computing environment to guarantee the validity of protection mechanisms even when the host OS is corrupted. It also leverages the invariant real-time nature and domain-specific features to detect malicious control signals. Tested under a control spoof and DoS attack, the robotic vehicle equipped with Contego-TEE still worked regularly with negligible extra overhead in execution time. PoTN [74] is a lightweight blockchain consensus protocol with proof-of-trust negotiations to identify the compromised fixed miners. With negotiation rules, a trusted random selection algorithm is introduced to select proposers and validators in a round of block creation while avoiding more communication overload of consensus protocols. As the proposers know nothing about each other, collusion to fake blocks among the proposers can be avoided. After simulating the peer-to-peer consensus processes, the results show that PoTN performs better in accuracy, network overload, and efficiency than traditional consensus protocols, Tendermint [156] and PoR [157]. [PERSON] et al. [149] proposed and studied a new hardware method, called Atlas, to guarantee security and isolation even when the operating system of an embedded system is compromised. Atlas relies on its zero-software trusted computing base to prevent any data or code leaks and provides confidential shared memory as a secure communication channel without a dynamic key exchange. They implemented Atlas based on the LEON3 softcore processor, and the FPGA-based evaluation showed that their method leads to minimal cycle overhead at the cost of a reduced maximum frequency.
# Fault-Tolerant Topology of Agricultural Wireless Sensor Networks Based on a Double Price Function
## 4 Hybrid WSN Topology  
Clustering has proven to be an effective energy-saving method. Its core is to divide nodes into several clusters, and each cluster is managed by one or more CHs. The member nodes only need to collect data. The CHs receive and fuse the data transmitted by the member nodes, and then transmit the processed data to the base station in a single hop or multi-hop [23]. As shown in Figure 1, we designed a hybrid network topology to reduce energy consumption and ensure network connectivity. Through network clustering, the member nodes can communicate directly with the CHs. The amount of data in the CHs is large and important. Therefore, a 2-connected fault-tolerant topology between clusters was constructed. The topology was initialized based on the potential game, and then the redundant links were eliminated by cut vertex detection.  
In this paper, clustering is based on the Heed clustering algorithm [33]. Considering the influence factors such as residual energy, the selection probability \(CH_{i}^{prob}\) of node \(i\) is as follows:  
\[CH_{i}^{prob}=C^{prob}\times(\varepsilon\times des_{i}+\sigma\times\frac{1}{d_ {i}}\times d_{i}^{\min}+\omega\times\frac{Er_{i}}{E^{\max}}) \tag{5}\]  
where, \(\varepsilon\), \(\sigma\) and \(\omega\) represents the weight factor. \(d_{i}\) is the distance between node \(i\) itself and sink, and \(d_{i}^{\min}\) is the minimum distance between the sink and the node in the set composed  
Figure 1: Hybrid topology diagram.  
of node \(i\) and neighbor nodes. \(des_{i}\) stands for node density. \(d_{i}\) and \(des_{i}\) is calculated as follows:  
\[d_{i}=\sqrt{\left(x_{i}-x_{s}\right)^{2}+\left(y_{i}-y_{s}\right)^{2}} \tag{6}\]  
\[des_{i}=\frac{\left|\left|Ne_{i}\right|\right.}{N} \tag{7}\]  
(\(x_{s}\), \(y_{s}\)) and (\(x_{i}\), \(y_{i}\)) represent the positions of the sink node and node \(i\) respectively.  
Equation (5) means that the greater the residual energy, the greater the node density, the closer to the sink node, and the closer the neighbor node is to the sink, the greater the probability of becoming the CH. After clustering, each member node adjusts the transmit power according to its distance from the CH.
# Deep Learning CNN-GRU Method for GNSS Deformation Monitoring Prediction
## 2 Method
### Prediction and Evaluation  
The output of GRU is connected to the fully connected layer of CNN to accomplish specific tasks. The fully connected layer integrates features extracted from convolutional and pooling layers and maps them to the GRU model. In this layer, features extracted by convolution and pooling layers are flattened and connected to a fully connected layer, which ultimately outputs the prediction results. The mathematical representation of the fully connected layer is as follows  
\[a^{l}=\sigma\Big{(}z^{l}\Big{)}=\sigma\Big{(}K^{l}a^{l-1}+b^{l}\Big{)} \tag{7}\]  
where \(l\) is the number of layers, \(a^{l}\) is the output layer, \(a^{l-1}\) is the output of the previous layer, \(K\) is the convolutional kernel, \(b\) is the bias, and \(\sigma(\ )\) is the activation function. The fully connected layer integrates features from the GRU model, which has better performance in the prediction of GNSS time series.  
The entire CNN-GRU model is trained using a labeled dataset and optimized via a backpropagation algorithm to better fit the training data and generalize to a new dataset. The trained model is evaluated using a validation set or test set to assess its performance on unseen data. The proposed method sets the Mean Squared Error (MSE) as the evaluation, for which the formula is as follows:  
\[MSE=\frac{1}{m}{\sum_{i=1}^{m}\left(y_{i}^{pred}-y_{i}^{test}\right)^{2}} \tag{8}\]  
where \(m\) is the length of the GNSS time series, \(y_{i}^{pred}\) denotes the \(i\)th element of the prediction, and \(y_{i}^{test}\) denotes the corresponding observation.  
The CNN-GRU model offers a powerful combination of multi-level feature extraction capabilities from CNN and the ability to capture long-term dependencies in sequential data through GRU. By integrating CNN for spatial feature extraction and GRU for modeling, the proposed method achieves strong adaptability, parameter-sharing benefits, and end-to-end training efficiency, making it highly effective for GNSS time series prediction.
# ELID Dressing Behaviors of Non-Abrasive Iron-Based Grinding Wheels
###### Abstract  
Non-abrasive iron-based grinding diamond wheels, lacking abrasive particles, negate the concern of detached passivated abrasives scratching the polished surface. During the Electrolytic In-Process Dressing (ELID) polishing process, \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\) particles form on the oxide film surface of the iron-based grinding diamond wheel devoid of abrasives. These particles assume the role of the grinding diamond wheel for polishing. Therefore, the ELID electrolytic performance and the oxide film formation performance of the non-abrasive iron-based grinding diamond wheel significantly influence the formation of \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\) particles and the subsequent ELID polishing performance. Our studies investigating the ELID dressing behavior of the non-abrasive iron-based grinding diamond wheel have analyzed the influence mechanism of the electric field, flow field, and pulse energy on its ELID dressing. Moreover, the roundness of the ELID dressing grinding diamond wheel, oxide film properties, and \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\) particle content were measured, and plate glass was polished using the ELID non-abrasive iron-based grinding diamond wheel. The results illustrate that the non-abrasive iron-based grinding diamond wheel exhibits excellent ELID dressing performance. The diamond wheel's roundness is less than 0.001 mm, the oxide film uniformly covers the surface, the content of \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\) particles is evenly distributed, and the surface accuracy of the polished plate glass is approximately 0.5 nm. These findings provide a compelling case for the continued use and development of grainless iron-based grinding diamond wheels in precision grinding operations.  
non-abrasive iron-based grinding diamond wheel; ELID dressing; finishing; oxide film; \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\)