esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Pathophysiology of Psychosis  
The pathophysiology of psychosis remains an enigma, so studies have described a biologically plausible mechanism that makes it possible to detect the increased risk of people developing a psychotic disorder when exposed to cannabis, due to the presence of THC, which is responsible for psychotomimetic effects [21; 30].  
As has been described, the AKT1 gene inactivates the enzyme glycogen synthase kinase 3 (GSK-3) by phosphorylation, which acts as a serine/threonine kinase that participates in signaling downstream of the dopamine D2 receptor (DRD2) [18]. In this way, cannabinoids activate the AKT1/GSK pathway by acting on CB-1 and CB-2 receptors thanks to the endocannabinoid system [18; 39; 40]. Likewise, CB-1 receptors are coupled to G proteins and are abundantly distributed in the limbic system (hippocampus and amygdala), cerebellum, prefrontal cortex (PFC) and basal ganglia, thus mediating the release of glutamate and gamma-aminobutyric acid (GABA) in the PFC, while in the cortical area, there is a subtype of GABAergic interneurons and CB-2 receptors, the latter being similar to CB-1 receptors, despite the fact that they are present in smaller quantities in the central nervous system (CNS) [27; 31; 36; 39].  
Regarding THC, the data reveal that it induces a decrease in the functionality of the AKT1 gene, producing an alteration in the inhibitory and excitatory balance that reaches the dopaminergic cells, which causes a greater response to the stimulation of DRD2 and a greater release of dopamine in the striatum, compared to that released in response to natural stimuli [40]. Therefore, studies indicate that dopamine release may be affected by the biological vulnerability of the psychotic disorder, showing that cannabis use leads to a decrease in the synthesis of dopamine. In healthy people, an increase in dopamine is found in those who had first-degree relatives with a psychotic disorder [28; 31; 41]. In the same way, the findings show that this greater release in the striatum can produce alterations in cognitive functions such as attention and working memory [42].  
Similarly, studies show that CB-1 and DRD2 receptors are jointly expressed in several brain regions and are involved in signal transduction [31]. In addition, they show that CB-1 receptors are absent in dopamine neurons, but they are expressed in glutamate and GABAergic neurons that are associated with dopaminergic neurons [31]. This leads to the results showing that dopaminergic neurotransmission by cannabinoid agonists is achieved involving indirect mechanisms through GABAergic signaling and glutamatergic [31; 34; 43].  
The mechanisms of action that indirectly increase dopamine levels in the striatum are shown below [34].
# Reference Broadcast-Based Secure Time Synchronization for Industrial Wireless Sensor Networks
###### Abstract  
Security is an important factor that cannot be neglected in the design of time synchronization algorithms since industrial wireless sensor networks are prone to attacks against physical nodes and communication links. The Sybil attack is an intelligent attack with a high destructive capacity in pretending multiple identities and broadcasting illegitimate messages to destroy the network operation. Existing secure time synchronization algorithms mostly focus on distributed protocols; however, they pay less attention to Sybil attacks and centralized network time synchronization. In this paper, we propose a novel reference broadcast-based secure time synchronization (RSTS) for industrial wireless sensor networks with a time source against Sybil attacks. Different from previous protocols, in converging the network structure and the clock status, RSTS employs a public neighbor forwarding mechanism based on reference broadcast to filter the illegal time information automatically. Instead of establishing a table with timestamps of packet transmission and receipt, the least square linear regression is utilized to estimate the compensation relative to the source node with the recorded time and calculated time difference in receiving packets. The simulation results demonstrate that RSTS is resilient to Sybil attacks as well as message manipulation attacks in comparison with existing algorithms.  
time synchronization; sybil attacks; security; industrial wireless sensor networks; message manipulation attacks +
Footnote †: journal: Applied  
00 2023 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 2 Materials and Methods
### Prediction Models Workflow
#### 2.2.1 Multimodal Signal Processing  
The multimodal signals were captured from three devices integrated into the collaborative system. We used the signals from (i) task-dependent controllers, (ii) a microphone headset, and a (iii) Tobii EyeX eye tracker that were set up for each participant to extract seven binary features used to recognize the behavior of the participants in collaborative interactions. As an initial approach to analyze the signals, we chose to represent the features as binary as it allows for simplified analysis with still dependable results [4, 50]. The diagram in Figure 4 shows that we derived one feature, _Speech Presence_, from the microphone headset as a measure of communication between the participants. Then, from the eye tracker, we extracted the _Gaze Presence_ feature and _Gaze on Object_ feature to measure participant's focus in the task. Finally, we extracted four features from the controllers based on the presence of the controller, represented by the _Controller Presence_ and _Controller Manipulation_, and the distance of the virtual object from a target location as a measure of task progression, as _Object Move Closer_ and _Object Move Away_. The feature values were either 1 or 0 representing the presence or absence of the feature. We describe the selection of the feature values in more detail in Table 2.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Device** & **Binary Feature** & **Feature Description** \\ \hline Microphone & & Feature is set to “1” when participant is speaking and “0” otherwise. \\ headset & Speech Presence & & Feature is set to “1” when participant’s gaze detected on screen and “0” otherwise. \\ \hline Tobii EyeX & Gaze Presence & & Feature is set to “1” when gaze is on a virtual object or within the defined “focus area” as depicted in Figure 5. \\ \hline Task-dependent controller & & Feature is set to “1” when an input is detected from the controller (keyboard button, mouse clicks, haptic presses) and “0” otherwise. \\ (keyboard, haptic, or game controller) & & Feature is set to “1” when controller is actively moving an object, and “0” otherwise. \\ \hline \multirow{4}{*}{Object Move Closer} & & Feature is set to “1” when the distance of the object from the target location is decreasing, and “0” otherwise. \\ \cline{1-1}  & & Feature is set to “1” when the distance of the object from the target location is increasing, and “0” otherwise. \\ \hline \hline \end{tabular}  
* When both _Object Move Closer_ and _Object Move Away_ are set to “0” at the same time, it means that the object is stationary.  
\end{table}
Table 2: Description of binary features determination from input devices.  
Figure 3: Workflow for the design and evaluation of the prediction models.
# SASLedger: A Secured, Accelerated Scalable Storage Solution for Distributed Ledger Systems
## 5 Implementation Based on Hyperledger Fabric
### Peer Node
#### 5.1.1 Modification of Generating Snapshot  
Before a block is transmitted to the remote DB server, the commitment of the block is generated and the indexDB of the peer is updated, so that the peer can retrieve transactions and blocks from the remote DB server after transmitting the blocks. A comparison of contents in indexDB before and after the update is provided (Table 1).  
In HLF implementation, a peer who joins a channel through a snapshot can obtain the latest world states when the snapshot is generated; but it cannot access the contents of blocks that have been committed before the snapshot is generated since the local databases of the peers that generate the snapshot only contain information of those blocks that are stored locally. However, in our implementation, since the indexDB is updated for the blocks that are going to be transmitted to the remote DB server, those blocks can be accessed by new peers if they join the channel through copying the information from the snapshot to their local databases. To support this function fully, the snapshot contents exported from different local databases are extended: besides the contents exported from stateDB and configHistoryDB which are also included in the snapshot of HLF, the snapshot of SASLedger also contains the history records of states that are exported from the historyDB and all the <Key, Value> records in the indexDB after it is updated.
## 5 Results and Findings
### Thematic Clusters
Key terms such as radiography, panoramic radiography, and computerized tomography reflect the essential role of AI in processing various types of dental images. Image enhancement and image segmentation play a crucial role in improving the quality and accuracy of dental diagnostics, as segmentation allows for the precise identification of dental structures, which is critical for treatment planning. This cluster also reflects the integration of AI in various practical applications such as computer-aided diagnosis, computer-assisted diagnosis, and automation. This trend points toward an increasing reliance on AI-driven tools to assist dental professionals in automating routine tasks, such as the identification of pathologies or the enhancement of image clarity for better clinical decision making. As dental AI applications mature, these tools are expected to become even more integrated into everyday clinical practice, further improving patient outcomes and streamlining workflows. The data suggest that while AI and deep learning are the foundations of current research, emerging trends such as large-scale models and digital twins are set to play a significant role in the future. Large-scale models are increasingly being developed to handle massive and diverse datasets, offering enhanced generalization across populations and improving diagnostic accuracy. The focus on terms like artificial intelligence, deep learning, and classification suggests that these models will become essential for processing the vast amounts of data generated by advanced dental imaging technologies like 3D imaging and cone-beam computed tomography (CBCT). Meanwhile, digital twins represent a more patient-specific approach, where a dynamic, real-time model of a patient's oral health can be created using AI. This concept is not yet explicitly reflected in the current data but can be inferred from the strong focus on AI's role in personalized care and predictive diagnostics, as seen in terms like diagnostic imaging and diagnostic test accuracy. The data from VOSviewer highlight the dominance of deep learning and CNNs in AI-based dental image analysis, particularly in tasks such as image segmentation and classification. The strong clustering around AI and machine learning further demonstrates the field's focus on diagnostic accuracy and the growing importance of performance metrics. Additionally, emerging trends such as large-scale models and digital twins are poised to further revolutionize dental imaging by enabling more personalized and predictive care. As research in this field progresses, these advancements will likely shape the future of AI applications in dentistry, improving both clinical outcomes and operational efficiency.
## 2 Functioning Principles and General Concepts for Transduction
### OECTs for Wearable Electronics Applications  
To complete this section describing the context of OECTs and OECTs-based sensors working principles, a few comments on the applications of such type of transistors in the field of wearable electronics are necessary.  
Wearable electronics is the branch of electronics dealing with electronics devices that can be worn on the body, either as accessories attached to a textile substrate or directly incorporated inside the fabric (fiber-based devices). Wearable electronic devices have been gaining considerable attention especially in the field of healthcare monitoring as, being in direct contact with the skin, they can provide important information on the physical and chemical signals emitted by the human body [59].  
Within this context, OECTs-based sensors seem particularly suitable for the fabrication of wearable devices, essentially for three main reasons [60]. First, as described in earlier sections, OECTs are biased using very low voltages (<1 V), allowing the utilization of portable, low-power sources. Secondly, when transistors are used as sensors the transduction and amplification phenomena are coupled in a single device, which thus reduces the number of electronic components needed for signal acquisition and treatment. Lastly, but probably most importantly, OECTs do not require a three-electrode setup (more specifically, they do not require a reference and counter electrodes, universally needed for other types of electrochemical sensors) which greatly simplifies the readout electronic circuit.  
Details on the fabrication and sensing performances of OECTs and OECTs-based sensors on fibrous substrates are given in Sections 3.3.4 and 3.3.5.
# Rare Earth Magnet Recycling and Materialization for a Circular Economy--A Korean Perspective
## 7 International Standards  
The International Organization for Standards (ISO) established a technical committee (ISO/TC 298) in 2015 for rare earth elements, with the initial scope defined as \"standardization in the field of rare earth mining, concentration, extraction, separation, and conversion to useful rare earth compounds/materials (including oxides, salts, metals, master alloys, etc.), which are key inputs to manufacturing, and further production processes in a safe and environmentally sustainable manner\" [55]. This technical committee consists of 34 countries including Korea, has five working groups (WGs) to date, and aims to establish universal standards for various industrial activities and standards for environmentally friendly technology. Korea is highly active in working group 2 (WG2; recycling), while the expert committee led by the Korean Agency for Technology and Standards (KATS) and the Korea Institute for Rare Metals (KIRAM) was established to keep up with global trends. Korean delegates proposed three documents starting from 2017 to aid recycling in view of Korea's ReMaT process.  
The documents are as follows:  
ISO 22450:2020: Recycling of Rare Earth Elements--Requirements for Providing Information on Industrial Waste and End-of-Life Products;  
ISO/FDIS 22453: Exchange of Information on Rare Earth Elements in Industrial Wastes and End-of-Life Products;  
ISO/PRF TS 22,451: Recycling of Rare Earth Elements--Methods for the Measurement of Rare Earth Elements in Industrial Waste and End-of-Life Products.  
These three documents comprehensively cover the downstream value chain of rare earth magnets. ISO 22,540 deals with the information that should be provided to the recycler by the waste handler, while 22,451 provides guidelines for conducting the quantitative analysis necessary for collecting this required information and 22,453 specifies the method of information exchange by which the desired information can reach the recycler in a systematic manner.  
ISO 22450:2020 has been published, while ISO TS 22,453 and ISO FDIS 22,453 are one stage away from being published. These standards will streamline the recycling process and promote recycling by aiding the recycling industry, so that recycling can be carried out in a standardized and sustainable manner. This will allow for industrial waste and end-of-life products to efficiently reach recycling facilities from where Korean recyclers can execute the ReMaT strategy.
# Achieving Low Latency Communications in Smart Industrial Networks with Programmable Data Planes
## 5 Implementation and Performance Evaluation
### Experiments and Measurements  
This section explains the experiments that have been designed and performed in order to validate the solution presented in this paper. The experiments have been executed in two different scenarios of IIoT communication presented bellow and in Figure 8:  
* Scenario A: This scenario comprehends the transmission of a message from the IIoT device to the SmartNIC, then to the cloud broker, and then the same path for the response.
* Scenario B: This scenario comprehends the transmission of a message from the IIoT device to the SmartNIC, then to the edge broker, and then the same path for the response.  
The operation of the experiments is the following. Messages carrying temperature measurements are sent from the MQTT publisher to the cloud MQTT broker using the Mosquito Linux package, which is an MQTT client/broker implementation. Those messages are analyzed upon arrival at the P4 SmartNIC, which decides where to send them. In the first scenario, where they are sent towards the cloud broker, this server processes them using a Python-based application to decide if the value exceeds a threshold. If that is the case, a response is sent to the publisher. However, in the second scenario where MQTT messages are sent to the edge broker, this server receives them with a Python application based on Scapy library, which automatically generates the response to the publisher. Response packets in both cases could be any kind of message, such as action triggering messages for industrial machines. For the sake of simplicity, we have decided to answer with MQTT alarm messages too, by subscribing the same IIoT device that publishes the measurements to the \"alarm\" topic in those brokers.  
Key performance indicators (KPIs) have been defined for the performance evaluation of the solution through several experiments, basing the election on the environment of the solution and its objectives. Experiments have been repeated 1000 times to obtain statistically meaningful results and not to be linked to temporary or occasional conditions.
## 2 Tracked Vehicle \"Dyjob\" with Internally Movable COG  
### Prototype Tracked Vehicle \"Dyjob\"  
Figure 2 shows our prototype vehicle \"_Dyjob_\". The COG position of _Dyjob_ can be longitudinally and internally moved by sliding a movable mass forward and backward. Table 1 shows _Dyjob_'s physical features and devices. The movable mass sticks out a little from the vehicle because _Dyjob_ is only a prototype. However, the small upper part hardly affects _Dyjob_'s performance and can in the future be contained in the main body by using a small on-board computer and compact motor drivers. The movable mass contains heavy-geared motors as well as electric devices and batteries. The high weight ratio of the movable mass to the whole body enables the COG to be shifted within a relatively long distance. _Dyjob_ uses 60 W DC motors with reduction ratios of 36:1 and 25:1 to drive a rubber track on each side and the movable mass, respectively. Each motor is equipped with an encoder to detect the rotation angle. _Dyjob_'s operator uses a handheld device (Figure 3) to move _Dyjob_ forward and backward, to change its direction by driving the tracks oppositely, and to move the movable mass forward and backward.  
Figure 1: An unmanned ground vehicle with high (A–A) and low (B–B) positions of center of gravity moving on a step.  
Figure 2: A prototype tracked vehicle “_Dyjob_”.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Physical Feature and Device** & **Specification** \\ \hline Length & 600 mm \\ \hline Width & 360 mm \\ \hline Height & 180 mm excluding the protruding part of the movable mass \\ \hline Whole weight & 8.5 kg \\ \hline Weight of the movable mass & 3.5 kg \\ \hline Coefficient of friction between the rubber track & 0.82 \\ and an indoor hard ground & 1.13 \\ \hline Coefficient of friction between the rubber track & 1.12 \\ and a block as an obstacle & \\ \hline Coefficient of friction between the rubber track & 1.12 \\ and a slope & \\ \hline Moving range of the movable mass & \(\pm\)0.142 m from the center of _Dyjob_ \\ \hline Moment of inertia about the axis O in Figure 2 & 0.275 kgm\({}^{2}\) \\ when the movable mass is placed in the center & 0.345 kgm\({}^{2}\) \\ \hline Moment of inertia about the axis O in Figure 2 & \\ when the movable mass is placed most & 0.345 kgm\({}^{2}\) \\ forward/backward & \\ \hline Actuator for each track & 60 W DC motor (RE30 by Maxon) with \\ \hline Actuator for the movable mass & 60 W DC motor (RE30 by Maxon) with \\ \hline Motor driver & 1 Axis DC Power Module by Hibot Co. \\ \hline Encoder to detect the rotation angle of the track & MR Type L by Maxon \\ pulley and the movable mass’s position & \\ \hline Inclinometer to detect the body tilt & SCA61T-FA1H1G by VTI Technologies Co. \\ \hline On-board computer & HRP-3P-CN and MCN by General \\ Robotics Inc. \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Dyjob_’s specification.  
Figure 3: A handheld device to operate “_Dyjob_”.
# Real-Time Control System for Model Railway Based on SIMIS W Interlocking System
###### Abstract  
Modern railway signalling systems are critical for ensuring the safe and efficient operation of railway networks. Educational institutions play a vital role in training students to design and implement these systems, replicating practical scenarios to bridge the gap between theory and application. Advanced electronic interlocking systems, such as SIMIS W, are widely used to manage complex signalling requirements in real-world railway environments. Existing model railway systems lack the capability to simulate advanced interlocking systems with real-time performance. This paper presents a model railway signalling system based on the SIMIS W interlocking system. The setup integrates ILTIS-N dispatcher workstations with a SIMATIC S7-1200 PLC, controlling signals, switches, and track circuits. A REST API ensures seamless data exchange between ILTIS-N and the PLC, achieving efficient system responsiveness. The system effectively replicates real-world signalling conditions with compatibility with existing protocols. Performance testing demonstrated a response time of approximately 1.2 s, achieving the design objective of a maximum 1.5 s. This ensures rapid and accurate signalling under real-time constraints. The developed system provides a practical educational platform for students to gain experience with advanced interlocking systems and serves as a basis for further research into scalable railway signalling solutions.  
model railway; Python; REST API; real time; signalling system; SIMIS W
# Ambiguity-Resolved Model Tests for Carrier-Phase GNSS
## 2 The Mixed-Integer GNSS Model
### The AF and AK Residual Vectors
The last two relations of the Lemma establish the link of the AF-AK _change_ in the normed residuals, on the one hand, and the difference between the estimated ambiguities and their assumed values, under \(\mathcal{H}_{0}\) and \(\mathcal{H}_{a}\), on the other hand. The AF-AK change in normed residuals is, therefore, a measure for the truthfulness of the assumed ambiguity values.
# Integrating Visual Cryptography for Efficient and Secure Image Sharing on Social Networks
## 3 Proposed Method
### Prespeneration of Encrypted Images
However, MSSIM cannot directly interpret hue similarity because the luminance component \(l\) is influenced by the contrast \(c\) and structure \(s\) terms. To mitigate this, a monochrome similarity term \(G\) is introduced. \(G(I,I_{h})=\frac{1}{M}\sum_{i=1}^{M}(g(I)-g(I_{h}))^{2}\) measures tone preservation, with a valid range of \([0,1]\), by calculating the mean standard error between the Gaussian-blurred grayscale input \(g(I)\) and the Gaussian-blurred halftone image \((I_{h})\).