esa-sceva/satcom-chunk-collection huggingface dataset:
# Short-Text Semantic Similarity (STSS): Techniques, Challenges and Future Perspectives
## 9 Conclusions  
The current study reviewed noteworthy publications on short-text semantic similarity. We discussed some open challenges and drawbacks related to the structure of sentences. We also identified deep learning techniques that work under the domain of short text similarity, short text classification, and short question-answering systems. The study further specified the three most used models for learning the context and background information of sentences. The performance metrics employed by deep learning algorithms were also studied. We also reviewed state-of-the-art datasets that are particularly effective and readily available for short text similarity. These datasets have been tested by a variety of tools. For future recommendations, we discussed the challenges and provided suggested improvements for future research directions.  
**Author Contributions:** Abstract contemplation, Z.H.A., Y.K.H. and H.B.; investigation, Z.H.A. and K.D.; methodology, Z.H.A., Y.K.H. and K.D.; formal analysis, H.B.; data curation, Z.H.A., G.M.S. and H.B.; interpretation of data, Z.H.A., Y.K.H. and H.B.; review, writing and editing, Z.H.A., Y.K.H. and K.D.; validation, H.B. and K.D.; supervision, Y.K.H.; project administration, Z.H.A. and Y.K.H.; writing--editing, original draft, Z.H.A. and Y.K.H.; funding procurement, Y.K.H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** Yayasan UTP Pre-commercialization grant (YUTP-PRG) 015 PBC-005.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** Appreciation goes to the Yayasan UTP Pre-commercialization grant (YUTP-PRG) 015 PBC-005 and the Computer and Information Science Department of Universiti Teknologi PETRONAS for supporting this work.  
**Conflicts of Interest:** Authors have no conflict of interest.
# Artificial Intelligence Applications in Smart Healthcare: A Survey
## 4 Applications
### Behavioral Health Support  
AI effectively diagnoses mental illnesses using methods inaccessible to human therapists by accessing, integrating, and analyzing patient data from diverse sources [83]. Primary technologies include ML and DL for accurate diagnosis and outcome prediction in mental health, computer vision for analyzing imaging data and interpreting non-verbal cues such as facial expressions and gestures, and NLP for speech recognition, text analysis, and understanding clinical documentation. Behavioral health support data are sourced from sensors for monitoring, social media platforms, and multimodal devices like smartphones, wearables, and physiological sensors. This provides real-world, continuous data on symptoms, treatment responses, behaviors, thoughts, and emotions [84].  
To elaborate further on AI's application in behavioral health support, several specific examples include emotion recognition and sentiment analysis, sleep monitoring and intervention, and digital therapy and intervention. AI can identify users' emotional states by analyzing text content in social media posts and messages, predicting tendencies towards depression or anxiety through tweets, and confirming emotional states via facial expression recognition in video calls or photos [85]. Using wearable devices like smartwatches, AI monitors sleep patterns, detects insomnia or other sleep disorders, and provides personalized recommendations, such as adjusting sleep schedules or performing relaxation exercises to improve sleep quality [86]. Additionally, AI-driven applications offer digital interventions like cognitive behavioral therapy (CBT) to help manage anxiety, depression, and other mental health issues. For instance, some applications use chatbots to interact with users, providing support and advice, simulating the role of a human therapist [87].  
However, compared to human psychotherapists, AI can better monitor and predict human mental health but lacks understanding of human emotions and the ability to provide humanized care and support.
# Routing Attacks Detection in 6 LoWPAN-Based Internet of Things
## 2 Background
### Routing Protocol for Low-Power and Lossy Networks (RPL)
#### 2.1.4 Blackhole Attack  
A Blackhole Attack is a security threat that disrupts the routing process by selectively forwarding or dropping network packets. The attacker pretends to have the shortest path to the destination node, causing legitimate nodes to forward packets to the attacker instead of the actual destination. Once the attacker has control over the packets, they can be selectively dropped or forwarded to other nodes, resulting in severe damage to the network. The consequences of a Blackhole Attack can be significant, including communication disruption, data loss, and network failures. In RPL, which is commonly used in IoT networks, a blackhole attack can be particularly damaging since many IoT devices have limited resources and rely on reliable and efficient communication to function properly [12].
## 3 Materials and Methods
### Setup and Strategy  
The proposed approach was implemented in a Python 3.9 environment running under the Ubuntu 20.04 operating system. The testing machine was equipped with an Intel Core i7-9700K @ 3.60 GHz processor, 16 GB RAM, and 24 GB NVIDIA GeForce RTX 4090 graphics processing unit, running CUDA 12.1.  
To train and deploy the proposed pipeline, the PyTorch 2.1.2 module--on top of which the YOLOv8 model is defined--is the only software requirement. Alternatively, the Ultralytics library installs all the required modules. Each parameter of the YOLO model can be customized. RGB images with a \(640\times 640\) pixel resolution are used for enhancing the model capability to capture small details that could be relevant for the accurate classification. The pre-trained weights are used to leverage the ability of the model to extract relevant features. Table 1 lists all the parameters that have been used to train both designed models.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**YOLOv8 Parameters**} & \multirow{2}{*}{**Classification Model**} & **Detection and** \\  & & **Classification Model** \\ \hline Image size & 640 & 640 \\ Batch size & 32 & 24 \\ Epochs & 1000 & 1000 \\ Pre-trained & True & True \\ Optimizer & SGD & SGD \\ Early stopping & Disabled & Disabled \\ Learning rate & 0.01 & 0.01 \\ Momentum & 0.937 & 0.937 \\ Weight decay & 0.0005 & 0.0005 \\ Augment & False & False \\ Mosaic & False & False \\ Translate & 0.0 & 0.0 \\ Flipud & 0.0 & 0.0 \\ Fliplr & 0.0 & 0.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Training parameters for the (_i_) classification model and (_ii_) detection and classification model.  
The original training and testing data ratio of the IP102 pest detection datasets is 80:20, with a total number of 15178 images for training and 3798 in the testing split. After the dataset balancing process, the ratio is 85:15, as the testing images are not modified while the training set size is increased. Figure 8 shows the distribution of the augmented images of the training set as a function of the specific technique implemented for their generation.
# Deep-Sea Underwater Cooperative Operation of
## 5 Work Efficiency Analysis  
The implementation of oceanic scientific expeditions provides great attention to the efficiency of offshore operations. According to our previous operating experiences with the Jiaolong HOV and Qianlong AUV, underwater operational time is the key in completing  
Figure 10: Schematic diagram of the cooperative operational command and monitoring system.  
underwater tasks. Our evaluation of the work efficiency generally refers to the time spent on completing the same task or completing a fixed task in a unit time. Therefore, it is reasonable for us to choose operational time to evaluate the operational efficiency to some degree.  
To facilitate analysis, the durations of some operating processes were normalized following previous operating experiences with the Jiaolong HOV and Qianlong AUV. In general, the diving speed of each HOV and AUV is 40 m/min, the preparation time of each submersible before diving is 1 h, and the deployment time is 0.5 h. The diving time depends on the operating depth. The dive and ascent times are approximately the same, the recovery time is approximately the same as the deployment time, and the deck transfer and system maintenance time is 1 h.  
The deployment and recovery processes of the lander/USV system are similar to those of the AUV. The underwater operation time of the HOV is 8 h, the underwater operation time of the AUV is up to 40 h, and the speed of the research vessel R/V is 12 km (22.224 km/h). The aforementioned data considers the technical data and power consumption of each submersible and research vessel R/V (including communication, positioning and navigation, detection, and sampling systems). We obtained these data based on previous diving operations. Due to the different operating depths, the dive and ascent times are different. The surface commander needs to do the corresponding time assessment and task planning in advance. The key elements, such as the operational tasks, sea conditions in the operational area, and operational scope, will affect the operational mode of the on-site decision-maker's constituency to perform the operational tasks.
# IGBT Overvoltage Protection Based on Dynamic Voltage Feedback and Active Clamping
## 5 Experimental Analysis
### Effect of Resistors on the Performance of Dynamic Voltage Feedback Circuits  
We set \(U_{Z51}\) = 100 V, \(U_{Z52}\) = 300 V, \(C_{1}\) = 100 pF, change the value of \(R_{4}\), and obtain the IGBT turn-off waveform when \(R_{4}\) = 10 \(\Omega\), as shown in Figure 16, and key parameters in Table 3 are obtained. Compared with the turning-off waveform of the IGBT when the resistance \(R_{4}\) = 100 \(\Omega\) in Table 2, it was found that as the resistance \(R_{4}\) decreases from 100 \(\Omega\) to 10 \(\Omega\), the turning-off voltage peak of the IGBT decreases from 382 V to 368 V, and the rising slope of the IGBT \(V_{ce}\) decreases during the turning-off process. According to the analysis in Chapter 3, this decrease occurs because increasing the resistance prolongs the charge and discharge time of the capacitor and reduces the current fed back to the gate.  
\begin{table}
\begin{tabular}{c c c c c} \hline \(C_{1}\) & \(R_{4}\) & \(U_{Z51}\) & \(t_{3}\)-\(t_{0}\) & \(V_{ce}\) \\ \hline
100 pF & 100 \(\Omega\) & 100 V & 550 ns & 382 V \\
500 pF & 100 \(\Omega\) & 100 V & 1080 ns & 346 V \\ \hline \end{tabular}
\end{table}
Table 2: Influence of \(C_{1}\) on the turn off characteristics.  
Figure 14: \(C_{1}\) = 100 pF turn-off waveform.  
The influence of resistance on the gate signal in the dynamic voltage feedback circuit is analyzed. Compared with \(R_{4}\) = 100 \(\Omega\) (Figure 14) and \(R_{4}\) = 10 \(\Omega\) (Figure 16), the change in \(R_{4}\) affects the change in \(V_{ge}\). The smaller the \(R_{4}\) value is, the slower the decline rate of \(V_{ge}\). In Figure 11, \(t_{3}\)-\(t_{0}\) = 550 ns; in Figure 13, \(t_{3}\)-\(t_{0}\) = 2.2 \(\upmu\)s. During the IGBT off process, with the decrease in \(R_{4}\), the feedback signal of the gate increases, which decreases d\(V_{ge}\)/dt, and finally extends the time beyond the DC bus voltage \(V_{d}\) to reduce the voltage spike. The gate needs to feed back a small voltage signal through the active clamp circuit to suppress the voltage spike.  
\begin{table}
\begin{tabular}{c c c c c} \hline \(C_{1}\) & \(R_{4}\) & \(U_{Z51}\) & \(t_{3}\)-\(t_{0}\) & \(V_{ce}\) \\ \hline
100 pF & 10 \(\Omega\) & 100 V & 2200 ns & 368 V \\ \hline \end{tabular}
\end{table}
Table 3: Influence of \(R_{4}\) on the turn off characteristics.  
Figure 15: \(C_{1}\) = 500 pF turn-off waveform.
## 7 Prospective Aspects of WebAssembly
### Need for Enhanced Security Measures  
Security is a paramount concern in the IoT landscape in light of the critical nature of many IoT applications and the potentially sensitive data they handle. While the secure-by-default nature of Wasm is one of its strongest advantages, further enhancements in this area expected as the technology matures and its use in IoT grows.  
* **Expanding Sandbox Capabilities** One of the key security features of Wasm is its sandboxing mechanism, which isolates Wasm modules from each other and from the rest of the system. This containment prevents a module from affecting other parts of the system in case it becomes compromised. Future developments could further enhance this sandboxing mechanism to provide even stronger isolation and control over module interactions, as well as to provide more granular permissions and capabilities to each module.
* **Advanced Cryptographic Features** Cryptography is a cornerstone of secure communications, and as Wasm is increasingly used for IoT applications we may see the introduction of advanced cryptographic features directly into the Wasm standard. This could include possibilities such as built-in support for encryption and decryption functions, digital signatures, secure random number generation, and other cryptographic primitives. These features would make it easier for developers to build secure IoT applications with Wasm while potentially improving performance by providing these capabilities at a low level.
* **Hardware Security Integration** Another promising direction for enhancing Wasm's security is deeper integration with hardware security features. Many modern processors offer hardware-level security features such as secure enclaves, which provide a protected area of memory where sensitive data can be processed securely. Through integration with these features, Wasm could offer an even higher level of security for IoT applications.
* **Fine-Grained Access Control** As Wasm evolves, we may see the introduction of more fine-grained access control mechanisms. These could allow for detailed control over what resources a Wasm module can access, such as specific devices, network interfaces, or files. This would provide an additional layer of security by ensuring that a module can only access the resources it needs to function and nothing more.
* **Security in a Multi-Tenant Environment** With the rise of edge computing and the proliferation of IoT devices, there may be situations in which multiple Wasm applications coexist on the same device. In such scenarios, ensuring isolation and security in a multi-tenant environment would be crucial. Future Wasm specifications could cater to such use cases, providing robust security mechanisms to ensure data privacy and system stability.
## 4 Results and Discussion
### Abrasion Resistance  
Figure 11 shows the abrasion test results for RCLMFRRHCC for concrete pavement emergency repair according to reinforcement fiber type. Mixes using reinforcement fibers showed improved abrasion resistance. This was because the reinforcement fibers provided a bridging effect to hold the concrete particles [11; 14]. There was no major difference in terms of reinforcement fiber type. Thus, abrasion resistance improved with the addition of reinforcement fibers by effectively tightening the internal structure. Furthermore, the increase in abrasion resistance with the addition of reinforcing fibers is influenced by the bonding properties of concrete and reinforcing fibers [11; 14]. Notably, natural jute and PVA fibers are 'water-friendly fibers' and thus, they adhere strongly to the concrete matrix for enhanced abrasion resistance [10; 11; 16]. The macro synthetic fiber has a crimped surface shape, which markedly improves bonding with the concrete [22; 23].  
Figure 10: Chloride ion penetration test results.
# A Testbed for GNSS-Based Positioning and Navigation Technologies in Smart Cities:
## 5 Hansel Mobile Applications
### Hansel App  
The Hansel app serves two purposes:  
* Demonstrate the capabilities of the WALS service in the context of Wi-Fi(tm + GNSS hybridisation;
* Provide the means of interaction with the CAC service: Upload and follow routes and receive feedback from the _testbed_ in the form of notifications.  
In terms of the WALS demonstration, the terminal uses the GNSS and Wi-Fi(tm) RTT ranges for processing. The processing consists in the steps shown in Figure 8, summarised as follows:  
1. Get the apriori coordinates the terminal (i.e., receiver). This apriori may come from the terminal itself (coarse cell position) or, if not provided, by an average of the positions of nearby WAP (Wi-Fi(tm Access Points) seen by the terminal. If in the latter case, the app shall fetch these positions using the WALS API;
2. Get the coordinates for each transmitter that is provided in the data packet (which contains the ranges). If the transmitter is a GNSS satellite, use the orbit provider (broadcast file, SP3 file, etc.). If the transmitter is a WAP (Wi-Fi(tm Access Point), a query is sent to the WALS service;
3. Compute the range models and partials. The range model will depend on the transmitter type (e.g., for GNSS the atmospheric delays, relativity, etc.)
4. Use the computed range and partials to build the navigation equations;
5. Solve the navigation equations and obtain the estimate of the rover position.  
In terms of CAC, the Hansel app allows the user to upload a route (or path) to the server. This route can be later followed by the same user or other users in the _testbed_. When a route is followed, the app sends the position every second and the _testbed_ checks if the position is within the boundaries of the route and notifies the user if the position is within boundaries. In addition, the route can be _timed_ which means that not only is the position checked but also if the user is in the position at a certain  
Figure 8: Hansel app is used to demonstrate the Wi-Fi(tm+ GNSS hibrisation.  
expected time. This facility can be used in future to check the velocity of the user for applications such as platooning.
# Optimal Thresholding for Multi-Window Computed Tomography (CT) to Predict Lung Cancer
## 2 Proposed Method  
As with most existing lung nodule detectors, the proposed method also consists of three steps. First, the images are preprocessed to eliminate noise and to improve the image contrast and quality so that the later stages of the algorithm can perform better. Second, the lung is segmented from the processed images, and, in the third and final step, nodules are detected using the shape characteristics.
# Barriers and Chemistry in a Bottle: Mechanisms in Today's Oxygen Barriers for Tomorrow's Materials
## 3 Passive Barriers
### Multilayer  
A multilayer barrier usually exists of 3 or more polymeric layers. The outer layers are structural layers, while the inner layer is a polymer that exhibits superior barrier properties. Films and bottles with this technology are prepared by co-extrusion, resp. co-injection, employing two extrusion barrels  
Figure 7: Three strategies to increase the barrier properties of a structural polymer (light color) by the addition of a low permeable material (dark color): Multilayer (**a**), coating (**b**) and composites (**c**). The solid arrow represents the oxygen flow from outside the packaging (left) to the inner content (right).  
on one machine, specifically designed for these applications [81; 82; 83]. While oxygen permeability for a monolayer can be calculated through Equation (3), for multilayer barriers a summation, expressed by Equation (7) needs to be employed [52]. Permeabilities for several barrier materials are listed in Table 1.  
\[\mathbf{P_{T}}=\frac{\mathbf{L_{T}}}{\frac{\mathbf{L_{T}}}{\mathbf{P_{1}}}+ \frac{\mathbf{L_{T}}}{\mathbf{P_{2}}}+\ldots+\frac{\mathbf{L_{n}}}{\mathbf{P_{ n}}}} \tag{7}\]  
\(\mathrm{P_{T}}\) is the total permeability and \(\mathrm{L_{T}}\) the total thickness of the bottle wall, whereas \(\mathrm{P_{n}}\) and \(\mathrm{L_{n}}\) are respectively the permeability and thickness of the \(n\)th layer. A common issue in multilayer applications is delamination. When the adhesion between the two polymers is unfavorable, the middle layer can become visible as it detaches from the structural layers during handling [84; 85].  
In films, this problem is solved by co-extruding tie-layers between the outer layers and the middle layer, consisting of an adhesive [90]. This is highly unfavorable in PET-bottles as this exhausts the recyclability of the polymer [85]. In other forms, a middle layer consisting of a polymer blend has been beneficial to limit the delamination effect. These blends usually exist of the targeted barrier polymer and a PET-copolymer [82; 84]. Inorganic nano-compounds have also been shown to enhance the Vanderwaals-interactions between the layers [85].  
While this multilayer solution has often been used in films, the desired specifications in bottles are often stricter as much longer shelf life is required. This passive barrier solution is not effective enough to reach the desired permeability for oxygen in bottle applications [83; 91]. Multilayer structures in bottles are therefore mostly used for CO\({}_{2}\)-barriers, water vapor barriers or in combination with active barriers. For electronic applications, both water and oxygen barriers are required. Multilayer technologies that can combine both barriers are therefore highly recommended. However, organic passive barriers can never provide sufficient protection for oxygen, so at least some of the layers should consist of inorganic materials, such as those employed as coatings, discussed below [92; 93; 94].
# Road-Side Unit Anomaly Detection
## 2 State of the Art
### C-ITS Systems  
In Europe, the C-ITS uses a rich communication protocol stack which has been defined and standardized by the ETSI standardization institute. Over the _Network layer (defined as geo-networking layer)_, the _Facilities_ layer has been designed in order to be an efficient interface with the application layer (close to the driver and the vehicle sensors). Various types of messages are provided by this layer in order to cover a set of use cases (road works warning, traffic jam detection, traffic light control, logistics management). In this study, we base our work mostly on one particular message type, which is the CAM.  
The goal of CAMs is to create a cooperative awareness among vehicles; its use is to provide dynamic information about the vehicle such as its position, speed, heading, etc. A CAM is a periodic message that is sent at a frequency from 1 Hz to 10 Hz depending on the speed of the vehicle. It can be sent using V2V or V2I communications.  
The architecture of V2V communications is presented in Figure 1, where each vehicle is supposed to have a pseudonym certificate. The messages sent by the vehicle could reach distant vehicles thanks to multi-hop forwarding.  
The general architecture of V2I communications is presented in Figure 2.  
An RSU plays the role of the infrastructure as it handles all the received messages from the vehicles and runs the road operator's computations such as traffic management and event recording. It also plays the role of a vehicle for the forwarding aspect and in some cases it disseminates events toward other RSUs within the operator's network if they are in its range.  
For the forwarding mechanisms, they are achieved with the geo-networking protocol [6] in the ITS-G5 protocol stack which has been defined by the ETSI. The protocol plays the role of the networking layer. Moreover, in our work, in addition to the CAMs, we also use the geo-networking layer of the packets to access additional information, such as a more precise localization of the concerned stations. Ref. [7] proposes a methodology to check for anomalies on the energy consumption of wireless devices.