esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comprehensive Survey on Knowledge-Defined Networking
## 8 Discussion
### Design Guidelines
#### 8.3.6 Application Plane Design  
Understanding the needs of the service to be offered is crucial when designing an application. This involves determining the algorithms, data types, protocols, languages, models, etc. needed for the service's implementation. Additionally, network administrators must ensure that all applications such as traffic engineering, network management, security, settings, business, network virtualization, etc. have been deployed in order for the network to function. Even though application testing should be carried out even after application deployment to upgrade the application and discharge improved new application versions, applications ought to be extensively evaluated before deployment to ensure that they are operating correctly and meeting the service requirements. Applications should be designed to handle failures and errors, which includes implementing redundancy and fail-over mechanisms to ensure service continuity in the event of failures, irrespective of the purpose for which the application is designed. The application layer should implement appropriate security measures such as access control, authentication, encryption, etc. by implementing security applications with security policies to protect the network from unauthorized access and attacks. The APIs of a KDN should be well designed to provide developers with easy access to network functionality and knowledge. Different APIs, including RESTful-APIs, intent-based APIs, Ad Hoc APIs, Abstract APIs, etc., can be used, depending on the application type, to transmit data, knowledge/rules, intentions, and policies between the application plane and other planes. An API should have clear documentation, be simple to use, and have effective error handling. Applications should be continuously improved by collecting regular feedback from users to improve usability and the user experience. Regular updates should be released, addressing bugs and improving performance with new features. Furthermore, application personalization can be used to cater to the specific needs and preferences of individual users using various methods, such as providing recommendations based on user behavior, allowing users to customize the interface, providing contextual information, etc. Finally, it should be tested whether dynamic updating of application policies using a rule engine in the intent/policy update plane of the application plane based on the generated knowledge/rules regarding the current network status functions as desired without violating the high-level user intents.
# The Role of AI-Based Chatbots in Public Health Emergencies: A Narrative Review
## 1 Introduction
### PARRY, Personification in Chatbots and Its Relevance in Public Health  
PARRY [27] is a key example of personification in chatbots, i.e., the ability for chatbots to tune their responses according to a given set of human traits [18]. Developed in the 1970s, PARRY was designed to simulate a person with paranoid schizophrenia [27]. PARRY's ability to generate responses that closely mirrored some symptoms of paranoia, like suspiciousness or delusional thinking, demonstrated that a computer could be programmed to replicate some complex human thought patterns and thus impersonate some human traits in conversation. These achievements were obtained through significant advancement in NLP, at the time, incorporating decision-making models and weighted responses that simulated paranoid reasoning [27]. The dialogues produced by PARRY were convincing enough to induce even trained psychiatists into believing that PARRY's quips came from human patients [28]. PARRY remains a historical example of how even simple chatbots can portray personifications of complex human features.  
In public health communication, personification is crucial for chatbots' engagement and retention. Firstly, personification can make chatbots appear more relatable and approachable, fostering user engagement. According to [PERSON] and [PERSON] [29], users are more likely to engage in sustained interactions with chatbots perceived as having human-like characteristics, such as empathy and understanding. In public health contexts, where conveying information and promoting behavior change are key goals [23], increased engagement can enhance the effectiveness of health communication. For instance, a chatbot designed to encourage vaccination might be more persuasive if users feel it genuinely understands and cares about their concerns [30]. Secondly, chatbots with personified features may help improve user retention by making interactions more enjoyable and memorable. Studies suggest that when chatbots exhibit human-like behaviors, such as humor or even personalized conversations, users are more likely to return for subsequent interactions [31]. This is especially relevant in public health, where ongoing engagement can be critical for chronic disease management, health education, and behavior modification [20; 30]. Personification in chatbots could be managed at the level of textual responses but also through other components, like avatars or animations. Importantly, the latter may also influence user engagement. For instance, [PERSON] and colleagues [32] showed that simpler textual chatbots, without complex animations or avatars, induced in users less negative affect and less intense psychophysiological reactions than simpler, text-only, chatbots. These findings indicate that not only text but also additional visual elements might influence user engagement with public health chatbots.
# Time and Energy Efficient Relay Transmission for Multi-Hop Wireless Sensor Networks
## 4 Performance Evaluation
Therefore, we consider these factors in our simulation. In this simulation, we consider two scenarios to evaluation the proposed scheme. In both scenarios, the PAN coordinator transmits a data frame to a leaf node. The target node is randomly selected among network nodes. In the proposed scheme, the PAN coordinator transmits the data frame to a neighbor coordinator in the RP and then the coordinator forwards a data frame to the leaf node in the GTS without the contention. In the two legacy protocols, the PAN coordinator transmits the data frame to a neighbor coordinator in its active period. Then, the coordinator transmits the data frame to the leaf node in its active period. In the first scenario, to configure the cluster tree, we implemented a simple tree formation algorithm based on the hop count 2, and in the second scenario, we evaluate the network performance according to the increase of the tree depth. In this simulation, we define that the delivery ratio is the ratio between the number of messages correctly received by the destination and the number of messages sent by the PAN coordinator. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Radio band & 2.4 GHz \\ Synchronization mode & Beacon-enabled \\ Carrier sense sensitivity & \(-\)85 dBm \\ PHY Thermal noise & \(-\)110 dBm \\ Channel number & 11 \\ Contention Window & 2 slots \\ IEEE 802.15.4 ACK & True \\ Number of packet retransmissions in case of failure & 3 \\ Maximum number of successive backoffs & 4 \\ Beacon Order & 11 \\ Superframe Order & 5 \\ Packet Size & 50 bytes \\ RX current consumption & 5.9 mA \\ TX current consumption & 9.1 mA \\ IDLE current consumption & 0.550 mA \\ Sleep current consumption & 0.001 mA \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Simulation Parameters. And we define that the average energy consumption is the average energy consumed by an intermediate coordinator in the network. We also define that the average latency is the average value of the latency measured from the instant at which the PAN coordinator sends a message to the instant at which the destination node correctly receives the same message. To evaluate the consumed energy, the energy model of the CC2630, which is a single chip 2.4 GHz IEEE 802.15.4 compliant RF transceiver [36] is used. The energy model in [36] defines four modes for the radio: transmitting, receiving, idle and sleep modes. The energy consumption is calculated by calculating the time spent on radio in each state multiplied by the energy consumption in that mode. Because the CPU consumption is very low compared to energy consumption by the radio [31], we do not consider the CPU consumption in this simulation. The common simulation parameters are summarized in Table 2.
## 3 Results
### Macro Behavior Analysis
#### 3.1.3 Volumetric Strain and Dilatancy  
Figure 7 shows the volumetric strain curves of thirteen different gravel particle aggregates at 200 kPa. It can be observed that under shear loading, the particle assembly initially undergoes contraction, subsequently transitions to dilation, and ultimately stability. The volume shrinkage in the initial phase was not significantly affected by the particle shape. The smaller the EI and FI, the larger the axial strain corresponding to the critical state, and the greater the final volume expansion. This indicates that the shear and swelling of the specimen is more flat or more elongated. Among them, the changes in EI are consistent with the findings of [PERSON] [32; 33] and [PERSON] [31].  
The maximum dilatancy angle is calculated as:  
\[\psi_{max}=\arcsin\{\frac{-(d\varepsilon_{\mathrm{\varphi}}/d\varepsilon_{1}) _{max}}{2-(d\varepsilon_{\mathrm{\varphi}}/d\varepsilon_{1})_{max}}\} \tag{4}\]  
where \(d\varepsilon_{\mathrm{\varphi}}\) is the increase in volumetric strain; \(d\varepsilon_{1}\) is the increase in axial strain.  
Figure 8 shows the relationship between the maximum shear dilatancy angle with lateral pressure and EI and FI. Figure 8 shows that the shear of the specimen is more affected by the shape of the particles than the lateral stress. As EI, FI, and EI/FI increase, the maximum dilatancy angle of the particle aggregate first increases and then decreases. The EI part has the same shear expansion law as [PERSON] et al.'s [29] specimens. Figure 9 shows the change in particle arrangement during the shear process--see Figure 9a,b. The left side shows the arrangement of the specimens with different particle shapes before the start of shear after consolidation, and the right side shows the arrangement of the specimens at 60% axial strain. The particle shape in Figure 9a is EI = FI = 1, and the particle shape in Figure 9b is EI = FI = 0.6. A comparison of Figure 9a,b reveals that triaxial compression affects the particle arrangement, with a more noticeable impact on the non-ideal spherical particles. It is evident that when EI and FI are not equal to 1, shear causes the particles to shift from a random arrangement to a preferential horizontal orientation. Specifically, when EI = FI \(\
eq\) 1, shear-induced displacement causes the initially random particle orientation to shift toward horizontal alignment, triggering volumetric dilatancy due to particle rearrangement. In contrast, when EI = FI = 1, the idealized spherical  
Figure 7: Volumetric strain–axial strain curves of specimens with different particle shapes (200 kPa): (**a**) EI; (**b**) FI; and (**c**) EI/FI.  
particle assembly tends to compact by filling interparticle voids under loading, leading to volumetric contraction. As illustrated in Figures 8 and 9, higher values of EI and FI promote void-filling mechanisms, whereas lower values amplify shear-induced dilatancy dominated by particle reorientation and slippage.
## 4 Discussion
Ethnicities manifest differently in retinal image due to changes in the pigment of the fundus. Therefore, the proposed model performed differently on different datasets. By using the same dataset, it cannot be sure that the obtained best performing models can be used to a different population and achieve the same outcomes without retraining. Another issue is that the datasets used here contained only high-quality images. In real screening setting, it is expected to encounter low quality images and images with affects and noises. Therefore, a representative screening dataset with co- morphologies, diverse ethnicities, ages and genders and low-quality images with acquisition artifacts, is in urgent need. Moreover, we can observe from our results that CNN models performed differently on the underlying data across the datasets. It may occur due to two different patterns; the dataset size and the imbalance data between glaucomatous and non-glaucomatous images. Although REFUGE, ORIGA and ACRIMA datasets are large enough for generalization of deep learning methods, RIMONE and DRISTI-GSI are quite small. Therefore, the models trained on these small datasets maybe over fitted. Therefore, we intend to enlarge these datasets to validate the robustness of the evaluated methods and even to build a customized deep learning method. Enlarging the datasets can be done by using image augmentation methods or using generative adversarial networks (GAN). Regarding imbalance data, except ACIRMA dataset, the rest datasets are heavily class-imbalanced where the number of glaucomatous are tiny and the number of non-glaucomatous are abundant. The class-imbalanced data can cause the classification bias in the deep learning algorithms which likely to favor to major class. This problem can be addressed by balancing the datasets in two different ways: adding more images to minor groups or using data sampling methods, and it remains as the future works. Furthermore, in this study, we used only deep learning methods to make the classification decision between glaucomatous and non-glaucomatous images. The main measurements used in previous studies such as CDR and measurement of OD and OC are excluded. Incorporating deep learning methods with these OD and OC related measurements may improve the classification accuracy of glaucomatous images. To measure CDR, we need to segment both OD and OC region. As our current segmentation method is designed for OD segmentation only, we need to redesign the proposed segmentation method to segment both OD and OC simultaneously. Finally, it is to highlight the disease screening scheme. This study aims for binary classification of normal and glaucomatous image. In this future, it would be more beneficial to develop an automated
## 4 Simulation
### Simulation Parameters  
Table 2 describes the parameters for the simulation of this research. For simulator validation, this work considers comparing the saturated throughput trend of IEEE 802.11 MAC protocol in the experiment with the original work presented in [48]. The payload, headers, and control packet sizes were kept the same as in the previous literature to maintain consistency in measurement. The number of stations ranged from 5 to 70, with increments of 5 stations, until saturation was reached. Further, the simulation was configured with IEEE 802.11b, which allows three concurrent channels without overlap by having three usable channels in the simulation. Similarly, IEEE 802.11n had 4 non-overlapping channels, and an upper-bound case for 12 usable channels was also simulated [49; 50]. In addition, the results are verified by comparing the normalized throughput of SA-MMAC to confirm the correctness of the Python-based simulation.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Configuration** \\ \hline Packet payload & 8224 \\ PHY header & 192 bits \\ MAC header & 224 bits \\ Frame (Payload + PHY + MAC) & 8640 bits \\ RTS & 176 bits + PHY header \\ CTS & 128 bits + PHY header \\ ATS & 128 bits + PHY header \\ ACK & 304 bits \\ Channel Bit Rate & 1 Mbit/s \\ SIPS & 10 us \\ DIFS & 50 us \\ \(CW_{min}\) & 32 \\ \(CW_{max}\) & 1024 \\ Number of Stations & 5-70 \\ Channels available & 3, 4, 12 \\ Maximum simulation time & 10,000 frame time \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation Parameters.
## 3 Results and Discussion
### Quality Parameters
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Free Fatty Acid (\%=nm/Oie Acid)**} & \multicolumn{1}{c}{**K322 (Absorbance Units)**} & \multicolumn{1}{c}{**\(P\)-Value**} & \multicolumn{1}{c}{**Month**} & \multicolumn{1}{c}{**C1**} & \multicolumn{1}{c}{**C2**} & \multicolumn{1}{c}{**C3**} & \multicolumn{1}{c}{**C4**} & \mul  
The oxidation of unsaturated fatty acids resulting from the formation of peroxyl radicals and hydroperoxides alters the configuration of the double bonds, transforming the normal configuration of interrupted methylene into its conjugated forms. These conjugated compounds absorb light at 232 nm (K232) and are indicative of primary oil oxidation [32]. Table 1 shows a marked increase in K232 up to 3 or 4 months of storage, with C4 and C5 conditions yielding the largest increases during this period. After 4 months, K232 values remain relatively stable until 12 months of storage, reaching values of 2.02 and 1.96 for the C4 and C5 storage conditions, respectively. The storage condition with light exposure, C2, showed only a moderate increase in K232, most likely due to the rapid decomposition of hydroperoxides where the breakdown of the conjugated dienes would give way to the formation of secondary oxidation products. Condition C3 demonstrated fluctuating K232 values, reaching levels similar to C4 and C5, while condition C1 (control) remained stable. This fluctuation of the K232 values could be explained by the decomposition of hydroperoxides, as noted previously. K270 measures secondary oxidation products (aldehydes and ketones) produced by the breakdown of hydroperoxides, reflecting a more advanced oxidation state than PV or K232 [33]. Fresh oil had a low K270 value of 0.09 and C1 (control) remained unchanged. However, when evaluating the effect of light on the formation of secondary oxidation products, storage at room temperature with light (C2) resulted in a higher formation of these compounds, reaching a K270 value of 0.15. Conversely, storage at room temperature in darkness (C3) remained relatively stable. When evaluating the effect of temperature (in darkness), it was observed that storage at 40 \({}^{\circ}\)C (C5) yielded the highest formation of secondary oxidation products, and surpassed all the other conditions by reaching a K270 value of 0.18. In support, the study by [PERSON] et al. [5] noted that the effect of temperature was significant in the formation of secondary oxidation products.
# A Trust-Based Model for Secure Routing against RPL Attacks in Internet of Things
## 3 Proposed SMTrust Model
### Proposed Parent Selection Algorithm  
The parent selection procedure of standard RPL is modified to develop a trust-based communication across DODAG. The proposed algorithm for trustworthy parent selection is illustrated below.
# A Feasibility Study for a Hand-Held Acoustic Imaging Camera
## 2 Acoustic Imaging Concepts
### Beamforming  
Beamforming is the digital signal processing technique that allows microphone arrays to focus on particular directions [20]. It computationally mimics the capability of parabolic dish antennas to isolate radio sources. Delay-and-sum is the simplest beamforming approach. Signals originating from the look direction arrive simultaneously and in phase at the central reference point when appropriately time-shifted. Coherent summation of the aligned microphone signals passes the source undistorted. Off-axis sources remain misaligned, causing attenuation after summing. More advanced optimal and adaptive methods synthesize filters to achieve configurable directional selectivity. The ability to digitally steer the focus point enables scanning to form full acoustic images. Beamforming transforms the microphone array into a highly directional virtual sensor with sensitivity patterns tailored through data-dependent signal processing. However, fundamental limits arise from the array geometry and ambient noise. Robust acoustic imaging requires jointly optimizing the array configuration with advanced beamforming techniques [21; 22; 23; 24] designed to maximize directional resolution.  
Ideally, the array would be infinitely large with continuous spatial sampling. In practice, size constraints necessitate designing optimized configurations to maximize imaging capabilities given physical limitations. There are inherent trade-offs between aperture dimensions, microphone density, aliasing artefacts, and processing load that acoustic camera architectures must balance.  
The filter-and-sum beamforming algorithm [17; 19; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34] provides improved performance over the delay-and-sum algorithm by applying filters to the microphone signals. This allows the array to focus on a specific direction more effectively and reduce the sidelobes, resulting in a clearer and more detailed acoustic image.
## 3 Results and Discussion  
### Structural Analysis  
Figure 2 shows the XRD patterns of all Tb-doped Lu\({}_{2.5}\)Y\({}_{0.5}\)(Al\({}_{2.5}\)Ga\({}_{2.5}\))O\({}_{12}\) samples (with Tb concentrations of 0.5%, 1%, 5%, and 10%) that closely match the standard Y\({}_{3}\)Al\({}_{5}\)O\({}_{12}\) (PDF#63-0291) reference pattern, confirming the formation of a single-phase garnet structure without any detectable secondary phases or impurity peaks across the entire doping range. Despite increasing the Tb content up to 10 mol%, the major diffraction peak positions remain largely unchanged, and the peaks stay sharp and well-defined, indicating high crystallinity. This structural stability, even at higher Tb\({}^{3+}\) doping levels, is attributed to the similar ionic radii of Tb\({}^{3+}\) (0.92 A), Lu\({}^{3+}\) (0.86 A), and Y\({}^{3+}\) (0.90 A), all with coordination number 6 [21,22], which minimizes lattice distortion. Maintaining a pure garnet phase is essential for optimal optical and scintillation performance, as secondary phases can reduce light yield and introduce undesirable scattering.  
Figure 1: The photograph of the Tb-doped Lu\({}_{2.5}\)Y\({}_{0.5}\)(Al\({}_{2.5}\)Ga\({}_{2.5}\))O\({}_{12}\) single-crystal samples under room light and under UV light source at wavelength 254 nm.  
Figure 2: X-ray diffraction (XRD) patterns of LuYAGG:Tb single crystals, confirming phase composition and crystallinity.
## 4 Discussion
### Tree Canopy Delineation
It is possible that the CHM reconstitution process is more accurate if a flight plan with different views and crossed image lines is adopted, increasing the overlap between the scenes. Although it is possible to improve the CHM generation, the RGBC model shows promising results in crown detection and it is comparable with the results of previous studies. As per Ref. [15], using Local Maxima and Marker-Controlled Watershed techniques used to segment tree canopies in agricultural regions, the best results for apple plants were obtained: R\({}^{2}\) = 0.87, RMSE = 0.72 and MAE = 0.57 for the correlation between the estimated and reference crown area and R\({}^{2}\) = 0.81, RMSE = 0.48, MAE = 0.39 for the perimeter. For the detection of dense canopies in a forest environment using the Mask R-CNN, R\({}^{2}\) = 0.93 was obtained in the correlation analysis of the inferred canopy areas with those in the reference as per Ref. [29]. A CNN-based model to delimit individual trees in apple plantations was used by Ref. [39]. The results of the correlation coefficient between the area and perimeter of the results obtained with the reference were R\({}^{2}\) = 0.80 and R\({}^{2}\) = 0.79 for area and perimeter, respectively. Regarding these studies, the results with the proposed method are extremely viable to increase the viability and precision of tree canopy delimitation in the agricultural environment, which facilitates those studies involving the structural characteristics of each plant in the plot.