esa-sceva/satcom-chunk-collection huggingface dataset:
# Plant Disease Detection Using Deep Convolutional Neural Network
###### Abstract  
In this research, we proposed a novel 14-layered deep convolutional neural network (14-DCNN) to detect plant leaf diseases using leaf images. A new dataset was created using various open datasets. Data augmentation techniques were used to balance the individual class sizes of the dataset. Three image augmentation techniques were used: basic image manipulation (BIM), deep convolutional generative adversarial network (DCGAN) and neural style transfer (NST). The dataset consists of 147,500 images of 58 different healthy and diseased plant leaf classes and one-leaf class. The proposed DCNN model was trained in the multi-graphics processing units (MGPUs) environment for 1000 epochs. The random search with the coarse-to-fine searching technique was used to select the most suitable hyperparameter values to improve the training performance of the proposed DCNN model. On the 8850 test images, the proposed DCNN model achieved 99.965% overall classification accuracy, 99.799% weighted average precision, 99.796% weighted average recall, and 99.7968% weighted average F1 score. Additionally, the overall performance of the proposed DCNN model was better than the existing transfer learning approaches.  
deep convolutional neural networks; generative adversarial network; basic image manipulation; random search; hyperparameter optimization; neural style transfer +
Footnote †: journal:  
Academic Editor: [PERSON]
# Firewall Best Practices for Securing Smart Healthcare Environment: A Review
## 3 Firewalls Types for Smart Healthcare Environment
### Packet Filtering  
The simplest type of firewall is packet filtering. Packet filtering operates at the network layer of the OSI model (Level 3). By use of the Internet Protocol (IP), a set of rules are created and applied on the network connection to accept or discard the traffic. Packet filtering firewalls allow or deny traffic in network IP protocols (TCP, UDP, or ICMP), in the source and destination of IP addresses, including ports and other settings of TCP headers. This firewall is quite straightforward to configure and can help to discard all unnecessary traffic, such as malicious packets transmitted through dangerous ports and the source of a potential attack. However, since protocol rules of packet filtering firewalls are essentially based on IP addresses, this does not ensure full protection of the network. Another problem is that IP packet filters are generally stateless, which means that the packets are not able to preserve and remember any previous traffic information [47]. This is undesirable for two reasons: (a) no packet memory means we cannot manage and secure our network effectively (it is as if we want to scout an area, and the map deletes checkpoints every time we finish the exploration of that area). (b) A stateless packet can be effortlessly spoofed due to the ACK bit in the packet's header and to the source address, then becoming potential targets for an attack [48]. For these reasons, this type of firewall is effective only when applying basic network rules directed to allow specific hosts (such as business partners) and to deny insecure hosts (i.e., blacklists).
# Evaluation of Patients with Hallux Valgus Wearing a 3D-Printed Orthosis during Walking
## 1 Introduction  
Hallux valgus (HV) is one of the most common forefoot disorders in musculoskeletal practice and is characterised by progressive subluxation of the first metatarasophalangeal joint. This deformity is more common in female and elderly populations. Approximately half of these patients develop pain or disability with worsening valgus angulation [1]. Numerous treatment options have been proposed for symptomatic patients as an alternative to surgery, including shoe modification and the use of customised or prefabricated insoles or orthotic devices. Moreover, some hallux orthoses can be used to restore alignment and biomechanical function in patients with mild to moderate valgus deformity [2].  
Orthotic management fixes the great toe in a corrected position [3]. Various orthoses are currently available on the market. However, previous studies have reported inconsistent findings regarding their effects on hallux position and progression [4, 5]. Certain conservative treatments aim to prevent soft tissue contracture at rest. The use of such devices has often been advocated for nocturnal use only. Customised approaches fit individual patients' needs to ensure that the devices are used consistently. However, making customised orthoses is expensive and time consuming, and thus might place considerable financial burden on patients.  
Recently, 3D printing has rapidly emerged as a computer-aided design and manufacturing technology [6, 7, 8] and is regarded as a quick and cost-effective approach fordeveloping a range of medical devices. This technology can be used to directly fabricate various prosthetic and orthotic devices from 3D geometric data and offers the benefits of customised manufacture and dimensional accuracy to meet the functional demands of individual patients. However, it is unclear whether 3D-printed orthoses can adequately control the hallux position during dynamic activities. Thus, further investigation is required to support their use.  
Many studies have assessed the outcome of orthotic treatment, mostly on the basis of clinical evaluation [3, 4, 5]. However, relatively few studies have assessed gait deviation in cases of HV deformity. The changes in certain kinematic parameters can be somewhat restored after surgical correction. The motion analysis of a hallux segment can provide an insight into the dynamic survey of orthosis-mediated intervention [9]. Thus, this study designed a 3D-printed orthosis and evaluated the changes observed in the HV angle in patients wearing the orthosis. In addition, the effect of dynamic correction was ascertained through a kinematic analysis of the hallux range of movement during walking.
## 5 Numerical Evaluation Results
### Evaluation Setup  
We have implemented the optimization framework described in Section 4 in MATLAB to evaluate the allocation of the backhaul bitrate resources in the upstream path in LayBack. The upstream data path consists of eNB, GW, and an operator core network.
# Research on Key Technologies of Elastic Satellite Optical Network Based on Optical Service Unit
## 3 Analysis and Discussion  
### Experiment Setup  
The topology of the satellite optical network used for verification is illustrated in Figure 5. The network topology follows the Beidou System Constellation, consisting of inclined geosynchronous orbit (IGSO), medium earth orbit (MEO), and LEO satellites. For simulation convenience, a simplified network consisting of thirty-eight nodes--comprising the space segment, ground segment, and user segment--is selected [25]. Specifically, the space segment includes thirty-five satellites, i.e., three IGSOs, eleven MEOs, and twenty-one LEOs, while the ground segment and user segment consist of three ground gateway nodes and user terminals, respectively. At least two inter-satellite laser links connect neighboring satellite nodes within the same orbit and between cross-orbit satellite nodes (e.g., IGSO01 and MEO01, and MEO2 and LEO01). These inter-satellite laser links are bi-directional full-duplex links. Additionally, ground gateway stations connect to IGSO and LEO satellites via laser links (e.g., GroundStation01 to IGSO01, and GroundStation01 to LEO04), with each laser link providing a bandwidth of 10.709 Gbps. To simplify the experimental setup, SubMiniature version A (SMA) differential cables were substituted for the wireless laser terminal in the hardware-in-the-loop simulation to emulate the inter-satellite link.  
To bridge the theoretical models described above with practical implementation, we further designed a hardware-in-the-loop simulation platform. In Figure 5, nodes spacecraft identifier (SCID) 1, SCID 2, and SCID 101 are selected from the IGSO and ground station nodes as real nodes for hardware-in-the-loop simulation, and the simulation network topology and experiment setup are shown in Figure 6a and Figure 6b, respectively. The device manufacturers and models are listed in Table 5. The hardware-in-the-loop simulation software environment and hardware setups are shown in Table 6. Given that the OISL-OSU interface's line rate is approximately 10.709 Gbps, the FPGAs used for Switch SCIDs are XILINX 7V690T, which support a maximum serializer-deserializer (GTH) line rate of 12.0 Gbps.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Parameter** & **OISL-OSU** & **OISL-MPLS** \\ \hline Channel level & L1 & L2 \\ Channel isolation & Time slot isolation & Label isolation \\ Bandwidth guarantee & Hard-channel isolation & Statistical multiplexing and QoS priority \\ Bandwidth convergence & Not supported & Supported \\ Delay and jitter & Guaranteed & Not guaranteed \\ Channel monitoring and jitter & Path overhead and OAM & Path overhead \\ \hline \hline \end{tabular}
\end{table}
Table 4: Pros and cons of OISL-OSU and OISL-MPLS.
## 5 Results  
The controlled experiment, in which 42 developers participated, was executed in April 2024. Their background included all the technologies that were used in the information system that they were asked to upgrade (all of them reported to master JSF--Jakarta Server Faces, EJB--Jakarta Enterprise Beans and JPA--Java Persistence API). Their development experience, both in study- or industry-level projects was, majorly, 1-3 years (91%). Three percent of the participants reported up to 5 years of experience, while six percent reported less than 1 year (see Figure 4). The participant involvement in the experiment was voluntary. The entry criterion was successful engagement in previous studying tasks, so only the most proven 42 students (out of 56) participated.  
The participants pre-test question answers are as follows. The majority of the participants had a positive attitude towards AI-based tools in software development, as demonstrated in Figure 5.  
The great majority (82%) also reported that they had already used AI tools in software development. They listed the following tools: ChatGPT, GitHub Copilot, Claude and Superhuman. The participants were eager to use AI tools to break down user stories into tasks and estimate them--85%. Three percent did not like the idea, while twelve percent were undecided.  
All 42 participants were divided randomly into the experimental and the control groups; inside the groups, they were also distributed randomly between development teams (7 teams in each group, 3 participants in each team). One team in the experimental group decided not to continue with the experiment, so we included and analysed the empirical data from 39 participants (6 development teams in the experimental group and 7 development teams in the control group).
## 2 Materials and Methods  
### Study Sites  
The selected reservoirs are located in the Colombian Caribbean hydrographic basin, which is irrigated by the main rivers of the country and houses around 85% of its population [40; 41]. This study considers dams constructed across three rivers of this hydrographic basin: the Magdalena River, the Cauca River, and the Sinu River. The dams built across the selected rivers are located on the fall line, with an upstream bedrock reach and a downstream alluvial bed. Anthropogenic interventions in these areas have generated severe changes in land use, which have resulted in increased sediment loads [42; 43]. However, the morphological impacts of damming in this hydrographic area have not yet been studied in detail; therefore, understanding the morphological trends and the magnitude of their changes due to dam construction is a useful task in the future planning and management of water resources.  
Spatial and temporal criteria were considered to define the study sites. The spatial criterion was limited by the fact that in this work, we use free satellite images with a lowest spatial resolution of 30 m; therefore, river reaches upstream and downstream of each dam must have a minimum width of 60 m. Considering the temporal resolution and to be able to identify morphological changes, images before and after the construction of the selected dam are required, with a minimum operating time of 5 years. The dams that satisfy the selection criteria are presented in Table 1 and Figure 1. In the case of Betania and Prado Dams, both structures affect the Magdalena River and are separated by a river reach of, approximately, 178 km (see Table 1).  
At the dates of construction of the Prado, Salvajina, and Betania projects (see Table 1), environmental impact studies were not mandatory according to Colombian regulations. Therefore, existing documents [44; 45; 46] only include technical recommendations, excluding the analysis of ecological and morphological impacts on the intervened river reaches. The Urra I project was conceived after the installation of environmental law in the country [47]; however, the possible morphological effects due to dam construction were not addressed in its environmental impact study.
# The Prediction of the Performance of a Twisted Rudder
## 4 Results and Discussions
### Validation of the Flow around a Flat Rudder
#### 4.1.2 Surface Cavitation  
Figure 5 shows the predicted surface cavitation on the flat rudder with that observed in the LCT model tests at the rudder angles of \(\delta_{\rm R}=0^{\circ}\), \(-4^{\circ}\), \(-8^{\circ}\) and \(-12^{\circ}\) and a ship speed of 30 knots. Under all conditions, surface cavitation on the outer surface of the port rudder and the inner surface of the starboard side (STB) rudder is seen. The main difference between the numerical and experimental results was that in the experiment, complicated cloud-cavitation was formed downstream just after the sheet cavitation inception at the leading edge of the rudder. Since the present numerical study used the RANS equations as the governing equations of cavitating flows and the VOF method which is a grid-based Eulerian method for cavitation modeling, it was difficult to precisely capture the behavior of cloud cavitation of particle-like characteristics which vary over a short time. However, it can be noted that the current numerical results agree qualitatively with the experimental results regarding the range and amount of cavitation occurrence according to the change in rudder angle. As expected from the previous results of the pressure distributions along  
Figure 4: Pressure distributions along the chord lines on the flat rudder: (**a**) \(z\)/s = 0.45; (**b**) \(z\)/s = 0.6.  
the chord lines, the surface cavitation inception is seen around the region of z/s = 0.7 even at \(\delta_{\rm R}=0^{\circ}\). From this cavitation inception, the rudder cavitation significantly expanded in the chord and span directions as the rudder angle increased. In addition, the tip cavitation inception was detected at the leading edge at \(\delta_{\rm R}=0^{\circ}\). The current study also tried to minimize this kind of cavitation by modifying the tip geometry. This result will be shortly discussed later.
# Integrated Haptic Feedback with Augmented Reality to Improve Pinching and Fine Moving of Objects
###### Abstract  
Hand gestures are essential for interaction in augmented and virtual reality (AR/VR), allowing users to intuitively manipulate virtual objects and engage with human-machine interfaces (HMIs). Accurate gesture recognition is critical for effective task execution. However, users often encounter difficulties due to the lack of immediate and clear feedback from head-mounted displays (HMDs). Current tracking technologies cannot always guarantee reliable recognition, leaving users uncertain about whether their gestures have been successfully detected. To address this limitation, haptic feedback can play a key role by confirming gesture recognition and compensating for discrepancies between the visual perception of fingertip contact with virtual objects and the actual system recognition. The goal of this paper is to compare a simple vibrotactile ring with a full glove device and identify their possible improvements for a fundamental gesture like pinching and fine moving of objects using Microsoft HoloLens 2. Where the pinch action is considered an essential fine motor skill, augmented reality integrated with haptic feedback can be useful to notify the user of the recognition of the gestures and compensate for misaligned visual perception between the tracked fingertip with respect to virtual objects to determine better performance in terms of spatial precision. In our experiments, the participants' median distance error using bare hands over all axes was 10.3 mm (interquartile range [IQR] = 13.1 mm) in a median time of 10.0 s (IQR = 4.0 s). While both haptic devices demonstrated improvement in participants precision with respect to the bare-hands case, participants achieved with the full glove median errors of 2.4 mm (IQR = 5.2) in a median time of 8.0 s (IQR = 6.0 s), and with the haptic rings they achieved even better performance with median errors of 2.0 mm (IQR = 2.0 mm) in an even better median time of only 6.0 s (IQR= 5.0 s). Our outcomes suggest that simple devices like the described haptic rings can be better than glove-like devices, offering better performance in terms of accuracy, execution time, and wearability. The haptic glove probably compromises hand and finger tracking with the Microsoft HoloLens 2.  
augmented reality (AR); virtual reality (VR); human-machine interaction (HMI); head-mounted display (HMD); HoloLens 2 (HL2) +
Footnote †: journal: Applied sciences
# Application of Reinforcement Learning in Decision Systems: Lift Control Case Study
## 4 The Proposed Solution
### Learning Dynamics and Evaluation Metrics  
In supervised learning, monitoring a model's progress is easily achieved by evaluating its performance on training and validation datasets. However, evaluating the performance and progress of agents in reinforcement learning presents more significant challenges [20]. To address this, we tracked multiple metrics during the learning process. These included the average Huber Loss value over all updates in the episode, the average reward obtained by the agent, the average waiting time of served passengers and the number of illegal actions the agent took.  
Although the average reward for an episode is the most obvious metric to evaluate the agents' progress, it tends to be noisy due to the changing distribution of the encountered states during \(Q\) function updates. The three plots in Figure 2 illustrate the average reward (light blue line) and its smoothed trajectory (blue line) for learning variants with Replay Buffer, Prioritized Replay Buffer and Memory Palace, respectively. Despite the noise, there is a notable improvement in the initial episodes from values around \(-0.6\) to values hovering around \(-0.12\).  
Figure 2: Average reward in subsequent episodes across three Experience Replay learning variants.  
The number of illegal moves provides insight into the progress of \(Q\) values evaluation accuracy in the subset of state space where the lift is on the lowest or highest floor. As shown in Figure 3, each learning variant's number of illegal moves sharply decreased from almost 2000 in episode 1 to below 250 from episode 50 onward. The consistent nonzero values result from the \(\varepsilon\)-greedy policy during learning, promoting exploration through random action choices.  
Although average waiting time is not explicitly stated in the agent's reward, the reward construction anticipates a decrease in average waiting time as a byproduct of increasing the reward. Figure 4 confirms this expectation, with each Experience Replay variant witnessing a visible decrease in average waiting time from over 100 steps to values oscillating around 10 steps. This alignment indicates that the increase in reward function values aligns with the general goal of reducing the average passenger's waiting time.
## 2 Theoretical Background  
### Strain Effect on Guided Waves  
According to the acoustoelastic effect, the propagation speed of ultrasonic waves changes as a function of the medium stress state [5]. The speed variation for bulk waves depends not only on the stress intensity but on its direction and is different for longitudinal and shear wave modes. In addition, there may be a geometric change in the medium, due to stress, which alters the propagation path. Thus, due to the change of the wave speed and propagation path, the propagation time of an ultrasonic wave changes with strain. This principle allows for measuring the strain level.  
Plates support the propagation of guided waves. In an isotropic plate, there are two families of guided waves, namely Lamb and SH waves [39]. For each of them, there exist, in principle, infinite propagating modes: the number of propagating modes increases with the frequency. These guided waves modes are generally dispersive which means that their phase and group speeds change with frequency. The dependence on strain for guided waves is more complex than for bulk waves [8; 9; 10; 13]. Each guided wave mode presents different speed variation which also depends on the frequency. For instance, the A0 Lamb wave mode presents either positive or negative speed variation depending on the frequency for uniaxial stress applied along the propagation direction [8]. It has also been observed that close to a mode's cut-off frequency the speed variation can be very intense [9].  
When a broadband pulse is applied to an ultrasonic transducer in a plate, potentially several dispersive guided wave modes may be generated. Due to this dispersive and multimodal behavior, it may be complicated to interpret the received signal, which may be composed by the interference of these several waves. The signal is yet more complex when there are scattering from features in the plate, such as reflections from the plate's end. In this scenario, if the plate is subjected to stress, each mode presents a different time-shift, and, due to the aforementioned nature of the received signal, quantitative identification of the stress effect in such a complex signal is impractical without resorting to some signal analysis technique [27; 28; 29]. In the next subsection, a strain monitoring approach that exploits the aforementioned complex behavior is summarized.