esa-sceva/satcom-chunk-collection huggingface dataset:
# TopoSinGAN: Learning a Topology-Aware Generative Model from a Single Image
## 4 Discussion
Its treatment of hanging lines as anomalies makes it particularly suitable for applications requiring closed loops, such as agricultural field delineation or membrane cell segmentation. However, for applications where hanging lines are expected, such as in road network analysis, TopoSinGAN may not be the optimal choice. This specificity underscores the importance of context in applying any approach including TopoSinGAN, highlighting its potential limitations in certain scenarios. This limitation is due to the design of the current optimization function (Equation (10)), which is designed to minimize the number of terminal nodes in the generated mask. To extend the applicability of our method to scenarios where hanging lines are expected, a possible adaptation could modify the optimization function to minimize the difference between the number of terminal nodes in the real input mask and those in the generated mask. For example, an adaptation of the optimization function (Equation (10)) could be expressed as:  
\[\theta^{*}=\arg\min_{\theta}\,\left(L_{\text{topo}}^{\text{Real}}(\theta)-L_{ \text{topo}}^{\text{Generated}}(\theta)\right) \tag{12}\]  
This approach could allow the model to preserve a desired number of terminal nodes rather than eliminating them entirely. However, implementing and validating this adaptation is beyond the scope of the current research, but could be addressed in future work. Another limitation of TopoSinGAN relates to the thickness of the boundary mask in the input real image. Our loss function uses fixed 3 \(\times\) 3 terminal detector kernels, which are sensitive to boundary thickness, potentially leading to suboptimal performance in certain cases. Two potential adaptations could be to convert the mask layer to a centerline (skeletonized) binary mask or introduce larger versions of the kernels, such as 4 \(\times\) 4 or 5 \(\times\) 5. Developing a version of TopoSinGAN that is robust to mask thickness will also be a focus of future work.
# Man-in-the-Middle Attacks in Mobile Ad Hoc Networks (MANETs): Analysis and Evaluation
## 4 Environment of Simulation
### Simulation Scenario Setup  
To estimate the performance of MITM assaults in MANET, this paper made use of the default simulation in OMNeT++, as depicted in Figure 5. We introduced 100 nodes into the system; the message was shared by nodes. We then injected 1, 3, 5, and 7 harmful nodes into the system to investigate the influence of attackers' strategies, where the assailant either dropped or delayed the exchanged data. Subsequently, we selected a single-source node (e.g., Node[0]) to broadcast the UDP packets and a single-destination node (e.g., Node[20]) to receive them. Note that the source node broadcast the packets to all nodes within its coverage area, while the destination address was unicast. Table 1 describes many of the simulation-related parameters.  
Figure 4: Workflow of frameworks in OMNeT++.
# Signature-Based Security Analysis and Detection of IoT Threats in Advanced Message Queuing Protocol
## 4 Overview of AMQP in IoT Systems  
The Advanced Message Queuing Protocol (AMQP) was first developed in 2003 by [PERSON] at JPMorgan Chase to facilitate efficient and reliable message exchange across diverse applications and systems. Operating at the application layer of the communication stack, AMQP is designed for interoperability, enabling seamless communication between platforms developed in different programming languages.  
AMQP uses message brokers (e.g., the RabbitMQ server) to act as intermediaries, receiving messages from producers and distributing them to designated consumers. Its architecture features two primary components:  
* Producers: Entities that generate and send messages.
* Consumers: Entities that receive and process messages.
* The broker plays a central role, facilitating communication through two key mechanisms:
* Message Queues: Temporarily store messages until they are retrieved by consumers.
* Exchanges: Route messages to appropriate queues based on predefined rules.  
AMQP supports flexible communication patterns, including publish/subscribe and point-to-point, making it adaptable for a wide range of applications. Its ability to prioritize and ensure the durability of messages enhances reliability in distributed systems.  
The IoT has transformed interactions with technology by connecting devices and applications seamlessly, enabling innovations in smart homes, healthcare, and industrial automation. However, this interconnectedness introduces significant security challenges, as data transmitted across networks become vulnerable to attacks. Protocols like AMQP are particularly susceptible to threats, given their critical role in managing communication in IoT environments.  
To address these challenges, secure communication is a priority. AMQP supports Transport Layer Security (TLS) and Secure Socket Layer (SSL) to encrypt communications between devices and servers, ensuring data confidentiality and integrity. Despite these measures, the growing complexity of IoT networks necessitates more advanced and efficient intrusion detection systems (IDSs) tailored for application-layer protocols like AMQP [22].  
By providing robust messaging features and supporting secure communication, AMQP remains a vital protocol in IoT systems. However, ongoing research and innovation are needed to address its vulnerabilities and ensure its reliability in increasingly complex network environments.
# The Shared Bicycle and Its Network--Internet of Shared Bicycle (IoSB): A Review and Survey
## 2 Background
### Comparisons
#### 2.3.1 Convenience  
**Details:** The convenience involves all aspects of bicycle rental, including account application, bicycle searching, bicycle returning, fees payment, etc.  
**Ranking:** Mobike (3) = OFO (3) > HZBike (1)  
**Reasons:** First of all, the handle procedures of the cycling card for HZBike are complicated and only available in some manual service points in Hangzhou. In contrast, the qualification of OFO and Mobike can be achieved through several simple steps in mobile app. Compared with the fixed parking pile of public bicycles, the dockless mode of OFO and Mobike seems to be more casual and convenient for users to return the bicycle. Additionally, users who rent a Mobike or OFO can pay their fees using e-payment, such as Alipay or WeChat Pay that is much more convenient than a traditional bus pass or cash. Similarly important, OFO and Mobike are available in most cities in China which means users who has an account can rent an OFO or Mobike when they go to other cities. However, for a kind of public transport dominated by the government, HZBike cannot be so convenient like OFO and Mobike for some reasons.
## 3 Communication Protocols in IoMT
### Perception Layer  
The IEEE 802.15.4 standard [14] is the basis for the protocols at the perception layer. This standard is responsible for specifying the physical (PHY) and media access control (MAC) layers for many types of devices with minimal complexity, cost, and battery consumption limitations. Several IoMT-related protocols, such as 6 LowPAN, ZigBee, and ISA 100.11a [15], are compatible with the IEEE 802.15.4 standard. To gather clinical data from sensors, healthcare systems employ several perception-layer protocols such as the Radio Frequency Identification (RFID) protocol, Near Field Communication (NFC) protocol, Bluetooth/BLE (Bluetooth Low Energy), Z-Wave, and Ultra-Wideband (UWB) protocol [16].
# Native Smartphone Single- and Dual-Frequency GNSS-PPP/IMU Solution in Real-World Driving Scenarios
## 4 Results Analysis
### Kinematic Performance Assessment with Different Processing Strategies  
Following the demonstration of IMU-only performance induced outages, this study returns to different combinations of PPP-IC smartphone processing and fusion with native IMU. In assessing the benefits of including more observations, the accuracy of three different combinations of GNSS signals processing, namely the single-frequency PPP (SF-PPP), dual-frequency PPP (DF-PPP), as well as single- and dual-frequency PPP (SFDF-PPP) are evaluated based on the time series using one of the five datasets (30 November 2021--Mi 8). All datasets are summarised afterwards to broaden the conclusions. It is important to note that GPS + GLONASS + Galileo + BeiDou (GREC) four constellations are processed both in SF-PPP and SFDF-PPP combinations, but the latter also considers the GPS L5 and Galileo E5a signals, which captures the benefits of dual-frequency PPP (GE).
# Lignocellulosic Biomass as a Substrate for Oleaginous Microorganisms: A Review
## 4 Biomass Pretreatment to Decrease Recalcitrance
### Principal Pretreatments
#### 4.1.5 Organosolv Pretreatment  
Organosolv pretreatment involves the application of an organic solvent to the lignocellulosic biomass in order to separate lignin in the liquid fraction and cellulose in high concentrations in thesolid residue. Pretreatment generates excision of lignin bonds, especially of O-aryl ether bonds with carbohydrates, producing a solid phase, which consists mainly of cellulose and hemicelluloses, and causing dissolution of lignin fragments [163]. It is carried out in different organic solvents (ethanol, methanol, acetic acid, formic acid, acetone, glycerol or phenol) whose concentrations generally vary from \(<\)1% _w/w_ to over 80% _w/w_[163] and is applied with or without catalysts (sulphuric acid, magnesium chloride, or sodium hydroxide). Treatment with ethanol is the most attractive and used method due to the low cost of the solvent and the high yields of extracted lignin [163; 190]. The organosolv pretreatment of hardwood and herbaceous in aqueous solutions can generate acetic acid from the acetyl xylan groups, which in turn catalyzes the degradation of hemicelluloses, the dissolution of lignin and stimulates the autohydrolysis of cellulose [190]. Softwoods have reduced xylan content, and therefore, the alkaline catalyzed organosolv pulping has also received a lot interest [220]. The advantages of this method are that it can be applied to hard and soft woods and the solvents can be easily recovered by more than 90% causing little environmental effect [163]. The main disadvantage is the danger of ignition by concentrated solvents and the generation of various inhibitory residues that may inhibit enzymatic hydrolysis [189]. The main degradation products are G-type lignin derivatives, such as guaiaicol (C6), vanillin (C6C1), vanillic acid (C6C1) and ferulic acid (C6C3), and S-type derivatives, such as syringaldehyde (C6C1) and syringe acid (C6C1) [215].
# Improving Bundle Routing in a Space DTN by Approximating the Transmission Time of the Reliable LTP
## 4 Numerical Evaluation of the Optimal Performance
### Case 2: Heterogeneous Network  
To observe the impact of heterogeneous link features, the average contact and disruption durations were randomly chosen for each link in the range \([50,150]\) to approximate the network average of the homogeneous case. To ensure evaluation fairness, the contact and disruption times were fixed to random values at the beginning of the evaluation, and the same values were used to evaluate all four service time estimation methods. Figure 6 depicts the results for the same parameter values that were used in the homogeneous network evaluation so that the results could be contrasted. While the overall trends were very similar to those observed before, the response times tended to be a bit lower than in the first evaluation case because of the specific random sample that was used. In that set, the average contact time was 351.75 s, and the average disruption time was 95.45 s, which offered a larger capacity than the homogeneous case with 100 s for both the contact and disruption periods, despite a lower contact rate of 0.0022 compared with 0.005 in the homogeneous case.  
Figure 5: Average network response time as a function of channel BER deviation probability. When a deviation occurrs, the BER increases four times, which extends service times and impacts the flow response times. Cases with a one-way propagation delay of \(D=0.1\) (**a**) or \(D=1\) (**b**) are shown.  
Figure 6: _Cont._  
The results from both evaluation cases suggest that the current methods used for the estimation of the bundle transmission time in routing can yield large performance gaps compared with the suggested method. The next section evaluates the method with DTN protocols and a realistic test scenario.
# Automated Enemy Avoidance of Unmanned Aerial Vehicles Based on Reinforcement Learning
## 4 Problem Solving
### Formulate the Problem as the MDP  
Reinforcement learning has been widely used in sequential decision making problems which are formulated as the Markov Decision Process (MDP). Typically, an MDP comprises of four elements: the state set \(\mathcal{S}\), the action set \(\mathcal{A}\), the transition function \(\mathcal{T}\), and the reward function \(\mathcal{R}\). When an agent is in a state \(s\in\mathcal{S}\), it can choose an action \(a\in\mathcal{A}\) according to a policy \(\pi\). After executing the action \(a\), the agent will enter into the next state \(s^{\prime}\in\mathcal{S}\) according to the transition function \(\mathcal{T}\), and will receive an immediate reward \(r\) according to the reward function \(\mathcal{R}\). In this enemy avoidance problem, the environment transition function \(\mathcal{T}\) is unknown, and will be learned by interaction with the environment. To formulate the enemy avoidance problem as the MDP, the state space \(\mathcal{S}\), the action space \(\mathcal{A}\), and the reward function \(\mathcal{R}\) are defined as follows.
## 3 Results and Discussion  
Most often, researchers using external gelation use manual periodic devices to make gel beads [6; 12; 13; 14]. Manually producing beads is associated with the possibility of amanufacturing error related to the lack of repeatability during the dropwise addition of the agent to the cross-linking solution. Moreover, their possible subsequent production on an industrial scale is cumbersome and time consuming. To avoid the influence of the human factor during processing and provide the possibility of the effective, automated production of gel beads, a prototype device was designed. The device consists of a body and a screw conveyor, the regulated rotations of which allow to adjust the residence time of the medium drop in the cross-linking solution. The CaCl\({}_{2}\) flow is realized by the body of device in the counter current by means of a peristaltic pump, from an external reservoir, in order to maintain a constant concentration of Ca\({}^{2+}\) ions. The polysaccharide solution is added using a needle attached to a syringe pump that allows flow rate control. The device is scalable and can be used both on a laboratory and industrial scale.  
The low flow rate used in the needle means that the influence of the viscosity of the pectin solutions has only a slight impact on the formation of the beads. The surface tension, which for all solutions is close to the surface tension of water, is responsible for the formation of the spherical shape of the beads. For this reason, the device made it possible to obtain repeatable pectin spheres in terms of shape and size. Commercial apple pectin was selected as the reference material, and spheres were made from aqueous apple pectin solutions of three concentrations in order to enable the comparison of properties with the tested material. For the currant pectin, concentrations were selected that would allow for obtaining similar dimensions of spheres so that their properties could be compared with the reference material. For all pectin solutions obtained, the gel spheres were approximately 3.50 \(\pm\) 0.32 mm in size and they were gelatinized throughout the entire volume. The properties of the obtained gel beads were consistent with those produced using the batch method.  
Red currant pectin is characterized by an average molecular weight of 1020 kg\(\cdot\)mol\({}^{-1}\)[19], which is almost 9 times higher than blackcurrant pectin (115 kg\(\cdot\)mol\({}^{-1}\)) [18] and significantly lower than an average molar weight of a commercial apple pectin 1576 kg\(\cdot\)mol\({}^{-1}\) (Table 1) [22]. Additionally, the aqueous fractions of pectin extracted from both currants contain a lot of simple sugars, the composition of which indicates strong branching, especially in the case of blackcurrant pectin (Table 1) [18]. The chain structure strongly influences the hydrodynamic properties of the biopolymer, especially the water-binding capacity and strength of the gel formed [6; 7; 8], which is reflected in the results obtained from the water absorption and texture test.
###### Abstract  
Three-dimensionally (3D) cultured dental pulp stem cells (DPSCs) reportedly exhibit superior multi-lineage differentiation capacities and have a higher expression in regeneration-related gene categories compared to conventionally cultured DPSCs. This study aimed to evaluate the effects of various mineral trioxide aggregates (MTAs) on DPSCs cultured in 3D, assessing their cell viability and tissue mineralization properties. We examined the morphology, cell viability, alkaline phosphate (ALP) activity and qualitative alizarin red S staining assay of the DPSCs that reacted with various MTAs, which included ProRoot (PRM), Biodentine (BIO), and Well-Root PT (WRP), in two different culture plates, an ultra-low attachment plate (ULA) and a conventional monolayer plate (2D). As a control, MTA-free and IRM samples were prepared. None of the MTA groups affected the microsphere-forming characteristics of DPSCs that had been cultured in ULA. The DPSCs that were cultured in ULA showed high cell viability in all MTA groups compared to IRM. The mineralization potential was favorable in all MTA groups, with a significantly higher ALP activity among the DPSCs that were cultured in ULA. Among MTAs, the PRM group showed substantially higher ALP activity than the other MTA groups. In conclusion, our results indicate that 3D-cultured DPSCs with various MTAs showed comparable viability and mineralization capacity similar to those cultured without reacting with MTA cement.  
mineral trioxide aggregates; dental pulp; restorative dentistry; culture method +
Footnote †: journal: Applied Sciences
# Estimation of Thermal Diffusivity for Greenhouse Soil Temperature Simulation
## 3 Results and Discussion
### Greenhouse Soil Temperature Simulation
#### 3.3.1 Average Soil Temperature Simulation  
To predict soil temperatures, we assumed that at all depths, the temperature oscillates as a sinusoidal function of time around an average value that was constant for all depths. Using the results of a traditional linear regression model and the coefficient of determination of the average daily soil temperatures at depths of 0.1 m, 0.2 m, and 0.3 m determined from the average daily soil surface temperature, the average soil temperature at different depths, T\({}_{\text{ave}}\) (z), can be calculated as:  
\[T_{ave}(z)=\exp(-0.1 Bz)T_{ave}(0)+T_{year}(1-\exp(-0.4 Bz)) \tag{18}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Date**} & **Environmental** & **Amplitude** & **Phase Shift** & **Arctangent** & **Logarithmic** & **Min-Max** \\  & **Conditions** & **(\(k_{\text{AM}}\))** & **(\(k_{\text{PS}}\))** & **(\(k_{\text{AR}}\))** & **(\(k_{\text{LC}}\))** & **(\(k_{\text{AM}}\))** \\ \hline
9/1 & Cloudy sky, cooled & 9.53 & 7.74 & 9.14 & 12.71 & 10.58 \\
9/2 & Clear sky, cooled & 8.95 & 12.69 & 11.02 & 8.19 & 10.03 \\
9/3 & Clear sky, cooled & 9.17 & 12.74 & 12.95 & 7.46 & 10.28 \\
9/4 & Clear sky, cooled & 9.49 & 11.37 & 12.56 & 8.68 & 10.03 \\
9/5 & Clear sky, cooled & 9.38 & 11.36 & 11.01 & 8.35 & 9.27 \\
9/6 & Clear sky, Uncooled & 9.21 & 12.08 & 11.51 & 8.38 & 10.53 \\
9/7 & Clear sky, Uncooled & 9.58 & 14.34 & 11.85 & 7.82 & 11.05 \\ \hline  & Average & 9.33 & 11.76 & 11.44 & 8.8 & 10.25 \\ Standard Deviation & 0.23 & 2.04 & 1.25 & 1.77 & 0.56 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the apparent soil thermal diffusivity k (\(\times 10^{-7}\) m\({}^{2}\) s\({}^{-1}\)) determined from amplitude, phase-shift, arctangent, logarithmic, and min-max methods using field data collected from 1 to 7 September 2016. The greenhouse was cooled with a pad and fan system from 1 September to 5 September, and no cooling was implemented from 6 September 6 to 7 September.  
Figure 4: Linear regression and coefficient of determination of the daily average greenhouse soil temperatures of soil surface (0 m) and different depths.  
where \(T_{yar}\) is the annual mean soil temperature outdoor, \(z\) is the soil depth and \(B=\sqrt{\omega/2k}\) is the inverse of the damping depth as described in Equation (5).  
The thermal diffusivity (\(k\)) determined with the amplitude method discussed in Section 3.2 was used in Equation (18) to simulate the daily average soil temperatures at depths of 0.1 m, 0.2 m, and 0.3 m from September 8 to December 31. The annual mean soil temperature (\(T_{ycar}\)) was recorded by the OARDC weather station. Figure 5 shows the linear regression and coefficient of determination of the observed and simulated average soil temperatures at 0.1 m, 0.2 m, and 0.3 m depths.
# Recent Advances in Flexible Resistive Random Access Memory
## 2 Basic Working Principle of Flexible RRAM
### Failure Mechanism of Flexible RRAM  
Flexible RRAM is bound to be bent repeatedly during use. In the process of bending, the device is prone to cracks, delamination, thermal effect accumulation and other problems, so the device loses the bistable characteristics, resulting in the failure phenomenon.  
[PERSON] [19] found that the generation of cracks in the electrode and dielectric layers affect carrier transport during the bending process, which is the main cause of device failure. The generation of cracks makes it difficult to form conductive filaments, and the formed conductive filaments will also break under the influence of cracks (Figure 2). With the increase in bending times, the density of cracks will increase, which will destroy the formation and stability of conductive filaments and then affect the stability of devices. On the other hand, according to the research of [PERSON] [20], when the opening current is large, the conductive filaments are relatively thick, which will produce large Joule heat. At the same time, with the increase in bending times and cyclic scanning times, the Joule heat generated in the device will accumulate. The conductive filaments formed in the device will be partially dissolved due to the Joule heat effect, resulting in device failure. In addition, if the device is in the same state for a long time, it may cause the electrode material to deposit to the electrode at the other end, which will cause delamination and lead to device failure.