esa-sceva/satcom-chunk-collection huggingface dataset:
# Traffic Signal Optimization for Multiple Intersections Based on Reinforcement Learning
## 4 Simulation  
The performance of the proposed model, the reinforcement learning-based comparison model excluding constraints, and the fixed-time model, PASSER II, were analyzed for two scenarios. The training of the proposed model and the comparison model was carried out by generating random traffic under the same conditions. When the accumulated waiting time or reward no longer decreased and converged, it was judged that learning was complete. Table 1 shows the number of repeated episodes until learning was complete. The simulation time of scenario 2 was based on the period of the acquired traffic data. For the proposed model to respond flexibly to realtime traffic flow, the time interval \(\Delta t\) was set to 3 s. Agents meaning signal controllers are equal to the number of intersections. In this simulation, it was assumed that the signal controller could obtain the traffic situation in realtime using the vehicle detectors.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Scenario 1** & **Scenario 2** \\ \hline Number of episodes & 100 & 160 \\ Simulation time \(T\) & 3600 & 18,000 \\ of one episode (second) & 3 & 3 \\ Time interval \(\Delta t\) (second) & 0.0001 & 0.0001 \\ Number of intersections & 2 & 6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters used in the model.  
Figure 2: Example of traffic signal control at a single intersection.
## 3 Methodology
### Model Evaluation Metrics  
The effectiveness of the models was assessed by computing goodness-of-fit metrics, such as accuracy, precision, and ROC curves. Table 1 shows how well negative and positive samples were correctly identified, with \(TN\) and \(TP\) values indicating true negatives and true positives, respectively, and \(FP\) and \(FN\) indicating false positives and false negatives, respectively. By calculating accuracy, recall, precision, \(F1\) score, specificity, and false positive rate (for their full specifications, see Equations (2)-(7)), a comprehensive evaluation of the model's ability to distinguish between different classes was obtained [37].  
\[Accuracy=\frac{TN+TP}{TN+FP+TP+FN}, \tag{2}\]  
\[Recall\ (True\ Positive\ Rate(TPR))=\frac{TP}{TP+FN}, \tag{3}\]  
\[Precision=\frac{TP}{TP+FP}, \tag{4}\]  
\[F1-Score=\frac{2\times(Recall*Precision)}{Recall+Precision}, \tag{5}\]  
\[Specificity=\frac{TN}{TN+FP}, \tag{6}\]  
\[False\ Positive\ Rate(FPR)=\frac{FP}{TN+FP}, \tag{7}\]  
A receiver operating characteristic (ROC) curve visually represents the performance of a binary classifier by illustrating its ability to distinguish between two classes considering different settings. It plots the recall (true positive rate) against the false positive rate (FPR). A better-performing classifier tends to have its ROC curve closer to the upper left side, while a curve approaching the diagonal signifies lower test accuracy. The default cutoff for classifiers is typically set at 0.5 to maintain a balance between true positive and false positive rates. Another performance metric, area under the curve (AUC), ranges from 0.5 to 1.  
In this study, we employed Python 3.12.0 as the programming language and leveraged the \"Sklearn\" library to process and build the decision tree model. The evaluation metrics were calculated using the \"Sklearn\" library, and the \"Imblearn\" library was utilized for implementing the random undersampling method (RUMC).
# Efficient and Secure EMR Storage and Sharing Scheme Based on Hyperledger Fabric and IPFS
## 3 Framework Components
### InterPlanetary File System  
The InterPlanetary File System(IPFS) is an open-source distributed file storage and content distribution protocol [21]. Designed to create a more robust, secure, and efficient environment for data sharing and transmission, the IPFS is particularly well suited for applications requiring long-term preservation, high redundancy, and resistance to censorship. When accessing medical data, the IPFS does not rely on server addresses but locates data based on its content hash, ensuring the uniqueness of the data and allowing for the replication and storage of identical content across multiple nodes in the network. When users access content stored on the IPFS, they are actually downloading data from the nearest or available nodes, rather than fetching it from a single server. This approach enhances the reliability and efficiency of the system.
# Intelligent Caching with Graph Neural Network-Based Deep Reinforcement Learning on SDN-Based ICN
## 4 Proposed Methodology
### State Space  
The network state \(\mathcal{S}_{t_{l}}=\{s_{t_{l}}^{n_{1}},\ldots,s_{t_{l}}^{n_{N}}\}\) captures the state of each network node at time step \(t_{l}\). Each node's state feature vector \(s_{t_{l}}^{n_{k}}\) at time step \(t_{l}\) is represented by \(s_{t_{l}}^{n_{k}}\in\mathbb{R}^{C\times 3}\), where \(C\) is the total number of items in the network.  
The state \(s_{t_{l}}^{n_{k}}\) of a network node \(n_{k}\) at time step \(t_{l}\) consists of three components:  
* 1 st component: The number of requests for each content item \(c_{i}\) that have traversed the node during the previous time interval (\(t_{l-1}\) to \(t_{l}\)). This count is stored only for the requested content in receiver nodes, cached content in router nodes, and published content in source nodes.
* 2 nd component: The cache storage of the node, represented by a binary variable for each content item \(c_{i}\). A value of 1 indicates that the node caches the content during the previous time interval, while a value of 0 is used otherwise.
* 3 rd component: The content publication of the node is also represented by a binary variable for each content item \(c_{i}\). A value of 1 indicates that the node has published the content during the previous time interval, while a value of 0 is used otherwise.
## 4 Experiments
### Experimental Setup
#### 4.1.2 Implementation Details and Parameter Settings  
Our model has been implemented using PyTorch [34]. We trained our model for 100 epochs with a batch size of 14. In the training process, each epoch takes 1 hour when using a single GeForce GTX TITAN X GPU. The resolution of the input/output images and disparity maps is \(768\times 384\).  
The output disparities are the values passed through the sigmoid activation function. These are bound between 0 and \(d_{limit}\) using sigmoid nonlinearity, where \(d_{limit}=0.15\times\) image width. We use a ResNet50-based auto-encoder for depth prediction. Our model outputs three disparity maps of different sizes in the decoder. The first predicted disparity map is upscaled and concatenated with a larger one in the channel direction, which is repeated once more in the following step. We compute the suggested losses for each of the three disparity maps. An Adam optimizer [35] is used to optimize our model with \(\beta_{1}=0.5\) and \(\beta_{2}=0.999\). The initial learning rate is \(1\times 10^{-4}\). It decreases by one half from the 15 th to the 29 th epoch, by one half again from the 30 th to the 39 th epoch, and by one fifth from the 40 th epoch to the end.  
We also performed several data-augmentation techniques to prevent over-fitting and enrich the training data: we randomly performed (1) horizontal flips, (2) gamma, (3) brightness, and (4) color transformation with a 50 percent probability and \(\pm 0.15\) range value. We set \(\alpha\) of Equation (7) to 0.5. In addition, we set the weight of the image-reconstruction loss to 1, the left-right consistency loss to 1 and the smoothness loss to 0.05 for the total loss. Based on the general principle of hyperparameter setting, we haverepeatedly evaluated the network accuracy with randomly sampled validation data to set the optimal values.
## 3 Results and Discussion
### Volatiles Composition and Sensory Evaluation  
The composition of headspace volatile compounds that were identified by SPME GC/MS in the non-fermented (NF) and fermented pomergnate (F) juice at 0 and 24 h of fermentation as well as at the 4 th week of storage is presented in Table 3. Statistical analysis was conducted between normalized peak area % of each volatile of NF and F pomergnate juice. Alcohols, aldehydes, ketones and esters were the predominant compounds identified. In particular, 13 alcohols, 11 aldehydes, 11 ketones, 10 esters, 7 terpenids and furfural were identified in the fermented pomergnate juice. All these compounds have been previously identified in pomergnate juices/fruit/seeds, fermented pomergnate juice by lactic acid bacteria and other fruits (apples, berries, plums, citrus, exotic fruit, etc.) or fruit beverages [51; 52; 53; 54; 55; 56; 57]. Fermented pomergnate juice (F) seems to contain more desirable compounds compared to non-fermented pomergnate juice (NF). Specifically, F contained more and higher amounts of alcohols, ketones and esters and less amounts of aldehydes after 24 h of fermentation and at the 4 th week of storage. This outcome is very significant since it is well established in the literature that: (i) alcohols contribute positively in the flavor profile due to floral attributes such as 2-ethyl-1-hexanol [51], (ii) aldehydes are non-desirable compounds in pomergnate juices [58; 51], (iii) ketones can deliver several positive sensory/aroma attributes [59] and (iv) esters play an important role in contributing to fruity notes of fruit juices [60]. Likewise, it is obvious that lactic acid fermentation affected positively the flavor of pomergnate juice. Finally, various common terpen originating from pomergnate juice [56; 35] were found in both non-fermented and fermented pomergnate juice. In particular, p-cymene, D-limonene, eucalypt, linalool, camphor, terinen-4-ol and \(\alpha\)-terpineol were identified. Figure 2: Antioxidant activity of fermented with _Lactobacillus plantarum_ ATCC 14917 and non-fermented pomergnate juice and at first 24 h at 30 \({}^{\circ}\)C and during storage at 4 \({}^{\circ}\)C for 4 weeks. TE, Trolox equivalent. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \multirow{2}{*}{**Compound**} & \multirow{2}{*}{**H1\({}^{\text{1}}\)**} & \multirow{2}{*}{**0 h**} & \multicolumn{4}{c}{**Normalized Peak Area \%**} & \multirow{2}{*}{**4 th week**} & \multirow{2}{*}{**Identification \({}^{\text{2}}\)**}
# Integrated Optical Phased Arrays for Beam Forming and Steering
## 2 Principles
### Transmitters and Receivers  
OPA transmitters are capable of beam forming and steering. OPA transmitters consisting of more than a thousand radiating elements [10; 16], and showing narrow beamwidths and high resolution [12; 65] have been demonstrated. Moreover, blue [41] and red light [65] OPA transmitters in the visible range have also found potential applications in biological sensing [55; 56].  
Based on the same physical principle and the reciprocity of electromagnetic wave propagation, a transmitter can be theoretically used as its counterpart, i.e., a receiver. Using control circuits, the reconfigurable receiver arbitrarily collects light in the FOV. The straightforward realization of an OPA receiver is yet obstructed due to the low signal-to-noise ratio. It is ineffective to directly use a transmitter as a receiver. An optoelectronic mixer can be used to down-convert optical signals to radio frequencies, with phase and amplitude information maintained. The weak received signals can be detected by highly sensitive detectors [43; 69]. Based on the dispersion of gratings, the output diffraction angle of a transmitter can be changed by wavelength. However, this mechanism does not work for receiving optical signals [48].  
The first presented integrated Si OPA receiver with an aperture size of 96 \(\times\) 50 \(\upmu\)m\({}^{2}\) contains 32 receiving elements [69]. This 1D OPA with heterodyne detection can selectively collect light from the desired angle and rejects others. The OPA receiver provides a fully programmable angular selectivity with an FOV of 30\({}^{\circ}\) and a beamwidth of 0.74\({}^{\circ}\)[69]. An optoelectronic mixer is used to enhance the detection sensitivity and robustness against noises and interference. A simplified 2 \(\times\) 2 receiver architecture is presented in Figure 3e. The first 2D OPA receiver, which consists of 8 \(\times\) 8 receiving elements, can selectively receive light from 64 different angles in an FOV of 8\({}^{\circ}\)\(\times\) 8\({}^{\circ}\)[43].
# Copper Alloys Performance in High-Pressure and Low-Velocity Conditions Using a Custom Tribometer
## 2 Materials and Methods
### Tribological Performance Study of Two Commercial Copper Alloys Methodology
#### 2.3.3 Subsurface Strain during Dry Sliding Study Methodology  
When the normal load is high, and the sliding velocity is low, severe mechanical damage is expected due to higher mechanical stresses, with a predominantly plastic wear mechanism. Therefore, it is crucial to assess the subsurface strain (\(\varepsilon\)) during dry sliding [5; 42].  
Frictional forces cause strains beneath the surface of metals that can be analyzed through metallographic examination of longitudinally prepared samples. The examination focuses on the tribologically transformed zone (TTZ), which consists of three distinct regions. The first zone is the unaltered bulk material, the second zone has undergone plastic strain due to shear, and the third zone contains metallic oxides with refined sub-micrometric grains and additional chemical elements, according to Figure 5a [43; 44; 45; 46; 47].  
To measure subsurface shear strain (\(\varepsilon(Z)\)) in TTZ, a shear angle (\(\theta\)) can be measured at different depths (Z) from the worn surface by tracing these lines separately, as explained in Figure 5b [41; 44; 45; 46].  
Equation (13) provides a mathematical interpretation of \(\varepsilon(Z)\). A geometrical approach determines this value by calculating the average linear intercepts of the deformed and original grain. This assumes a spherical metal grain model deformed due to shear stress. The cross-section that passes through the sphere's center and is perpendicular to the shear stress plane is considered [43; 44; 46].  
\[\varepsilon(Z)=\frac{\sqrt{3}}{3}\cdot\text{tan}[\theta(Z)] \tag{13}\]
# Advanced Sensor Integration and AI Architectures for Next-Generation Traffic Navigation
## 4 Discussion
By calculating the global indicator _GRQIK_, this innovative approach offers a holistic perspective on urban traffic optimization. This makes this paper stand out due to its high degree of innovation. To support this claim, a Web of Science search was conducted using the query TS = ((\"Traffic Optimization\" OR \"Route Optimization\" OR \"Urban Traffic\") AND (\"IoT Sensors\" OR \"Internet of Things\" OR \"Sensors\") AND (\"Road Quality\" AND \"Accident Detection\" AND \"Road Safety\")), which yielded no results. The implications of this work extend to urban mobility and intelligent transportation systems. Our approach provides a scalable solution to mitigate congestion, improve travel safety, and reduce environmental impact through optimized traffic flow. The system also demonstrates the feasibility of combining edge computing and cloud-based AI to address complex real-world navigation challenges. Furthermore, the TOA's modular design allows for integrating additional parameters, such as weather conditions or user preferences, thus broadening its applicability. Despite its promising results, the system has some limitations. First, relying on the TomTom Routing API and Azure services introduces potential dependency issues, particularly regarding data availability and service disruptions [69]. Second, while the _GRQlk_ captures multiple dimensions of route quality, some aspects, such as pedestrian traffic or real-time construction updates, are not accounted for. Finally, deploying IoT sensors on a large scale may pose logistical and financial challenges, particularly in resource-constrained regions. Future research should focus on extending the system's capabilities by incorporating additional data sources, such as real-time weather updates and user-generated feedback. Further _GRQlk_ development could include adaptive parameter weighting based on user preferences or contextual factors. Expanding the dataset for training and testing Azure's Content Safety API will improve its accuracy across various scenarios. By addressing these limitations and recommendations, the proposed system has the potential to set a new benchmark in intelligent traffic navigation and urban mobility solutions.
# Small-Scale Zero-Shot Collision Localization for Robots Using RL-CNN
## 4 Proposed Two-Stage Collision Localization Method  
In this section, our proposed method, a two-stage convolutional neural network, is introduced. In our scheme, the structure of the two-stage model is in binary tree form, and the results of the root stage will decide which leaf stage should be activated. Therefore, our method is called RL-CNN. Such a structure is designed to isolate regions where similar collision behavior occurs in order to improve localization accuracy. In the root stage, the task is to identify whether the collision happens in the upper region or the lower region. In the leaf stage, the model predicts in which fine-divided region the zero-shot collision point is located. As the whole area is partitioned into eight finely divided regions, the task in each leaf stage is essentially to solve a four-classification problem with zero-shot samples.
###### Abstract
Motivated by these issues, we propose a novel approach for achieving high-speed optical chaotic data selection logic operations with error detection and correction capabilities. In our approach, based on EO modulation theory, we achieve complete mutual inhibition between x-PC and y-PC. By employing an outstanding mean comparison demodulation mechanism, we enable the optical chaotic data selection logic operations to achieve a maximum speed of up to 114 Gb/s. Additionally, leveraging the generalized chaos synchronization theory, the system is capable of accurately detecting and correcting errors in chaotic logic outputs, with a noise strength limit as high as 185 dBw, exceeding the noise strength limit reported in reference [28] by approximately 100 dBw. These research results have great applications and reference values for the construction of high-speed optical chaos computing architecture and the development of optical chaotic network communication systems.
## 3 Analysis of Structural Dynamic Response under Pedestrian Load Excitation
### Structural Dynamic Characteristics Analysis  
The structure eigenvalue analysis of the bridge was conducted, and the results are presented in Table 1. The structural vibration diagrams are shown in Figure 2.  
\begin{table}
\begin{tabular}{c c c} \hline
**Vibration Type** & **Frequency (Hz)** & **Description** \\ \hline
1 & 1.150896 & First-order lateral bending of bridge deck and arch ribs \\
2 & 1.427685 & First-order vertical bend of bridge deck + vertical bend of arch rib \\
3 & 1.446180 & Antisymmetric vertical bend of bridge deck + vertical bend of arch rib \\
4 & 1.854052 & Arch rib lateral antisymmetric side bend \\
5 & 2.499991 & Bridge deck and arch rib vertical bend \\
6 & 3.392810 & Bridge deck with arch rib second-order lateral bend \\
7 & 3.521042 & Bridge deck second-order lateral bend + arch rib third-order lateral bend \\
8 & 3.922015 & Bridge deck second-order vertical bend + arch rib second-order vertical bend \\
9 & 3.981197 & Bridge deck third-order vertical bend + arch rib second-order vertical bend \\
10 & 4.763216 & Bridge deck third-order vertical bend + arch rib third-order vertical bend \\ \hline \end{tabular}
\end{table}
Table 1: The first 10 order of frequency and related description of the case arch bridge.  
Figure 1: FE model of the case bridge.  
According to the self-vibration modal analysis, it can be expressed that the bridge deck system and the arch ribs appear to vibrate synchronously, and the vibration of the arch ribs is coupled with the vibration of the bridge deck. In addition, the results shown in Table 1 and Figure 2 indicated that the 1 st order frequency of the lateral vibration was 1.1509 Hz, and the vibration pattern was characterized by the lateral bending of the bridge deck and arch ribs. The 1 st order frequency of the vertical vibration was 1.4277 Hz, and the vibration pattern was characterized by the vertical bending of the bridge deck. What is more, the 3 rd and 5 th order modes are also vertical vibration modes with frequencies of 1.4462 Hz and 2.4999 Hz, respectively, which are within the range of the pedestrian step frequency, resulting in the possibility of resonance.
# From Patient to Musician: A Multi-Sensory Virtual Reality Rehabilitation Tool for Spatial Neglect
## 2 Background
### Music Therapy and USN  
Music therapy has shown great potential as a therapy method for multiple syndromes [45; 46; 47]. The act of playing an instrument provides a patient with the opportunity to train multiple sensory modalities simultaneously, such as hearing, vision, touch, motor and cognitive skills [48; 49]. Moreover, music-making taps into the emotion and reward system of the human brain, making a music therapy intervention intrinsically rewarding and engaging [50].  
Neurologic Music Therapy (NMT) is a neuroscientifically motivated model of music practice and consists of 20 research-based music therapy techniques. One of these techniques is MNT, a music-making spatial neglect therapy. In this model, MNT is described as a technique that \"includes active performance exercises on musical instruments that are structured in time, tempo and rhythm, and is in appropriate spatial configurations, to focus the attention to a neglected or unattended visual field\" [11].  
The use of music in spatial neglect therapy has additional benefits, such as the [PERSON] effect. This effect states that one has better spatial-temporal reasoning when listening to a certain [PERSON] sonata [51], indicating that music and space are closely linked. It has been shown that, for people with spatial neglect, listening to classical music, or pleasant music in general, increases their spatial awareness and boosts arousal [52; 53]. Second, people have a tendency to spatially represent ordinal sequences, such as numbers or tones. Even non-musicians have a so-called \"piano in the head\", where notes are arranged from left to right, low to high, on a horizontal axis. This neurological trick can thus be applied in a spatial neglect intervention method [54; 55].  
For these aforementioned reasons, musical practice is considered a promising tool for the rehabilitation of USN [49]. However, to date, only a few studies exist on the use of MNT [17; 56; 57; 58]. These studies use a horizontally aligned instrument, such as a piano, with the objective to increase the active exploration towards the neglected side of the patient. Overall, results were positive, observing significant improvements in the visual exploration of patients, with both short- and long-term effects. One case studied the influence of the congruence of the sound feedback design on the performance of the patient, by comparing multiple sound feedback options, such as congruent sound feedback, i.e., the pitches of the notes are horizontally aligned similar to the keys on a piano, random sound feedback and no feedback, i.e., silence [17]. The researchers observed that the spatial exploration of patients with left-sided neglect was superior in the case of congruent sound feedback and that the use of a musical scale may trigger the preserved auditory and spatial multi-sensory representation of successive sounds.