esa-sceva/satcom-chunk-collection huggingface dataset:
# A Gamified AI-Driven System for Depression Monitoring and Management
## 5 Discussion
### Validity of Synthetic Evaluation  
Synthetic data enables flexible, early-stage evaluation by facilitating scenario testing, algorithm validation, and stress testing without raising privacy or ethical concerns. Our synthetic dataset was designed to capture realistic behavioral, cognitive, and environmental variability based on empirical ranges reported in prior literature. This approach supports construct validity by simulating plausible depressive states, although it cannot fully replicate the complexity and unpredictability of real-world human behavior.  
Model evaluation across three distinct classifiers, random forest, logistic regression, and neural networks, showed consistently strong performance, with the neural network slightly outperforming others (ROC-AUC: 0.931; F1 score: 0.860). These results suggest that the chosen feature space is informative and that depressive patterns are learnable under simulated conditions. We used stratified sampling, cross-validation, and identical training inputs across models to strengthen internal validity and ensure fair comparisons.  
To further validate the models' design, the data were simulated using parameter distributions derived from mental health studies. This grounding in empirical findings adds realism to the simulation process and offers confidence in early system performance.  
However, generalizability (external validity) remains a key limitation. While the synthetic data reflects diverse behavioral profiles, it may omit latent factors in real populations, such as cultural influences or clinical comorbidities. We aim to address this by validating the platform in upcoming user studies and providing naturalistic data for fine-tuning the models. Ultimately, the synthetic framework enables agile testing of both system logic and adaptive workflows before ethical and logistical challenges of real-world trials arise.  
While synthetic evaluation offers a valuable foundation for prototyping and testing, it is not a substitute for longitudinal, real-world validation. These results provide a proof of concept. They are intended to guide future research using real-world data.
# Quantum-Secure Coherent Optical Networking for Advanced Infrastructures in Industry 4.0
###### Abstract  
Modern industrial ecosystems, particularly those embracing Industry 4.0, increasingly depend on coherent optical networks operating at 400 Gbps and beyond. These high-capacity infrastructures, coupled with advanced digital signal processing and phase-sensitive detection, enable real-time data exchange for automated manufacturing, robotics, and interconnected factory systems. However, they introduce multilayer security challenges--ranging from hardware synchronization gaps to protocol overhead manipulation. Moreover, the rise of large-scale quantum computing intensifies these threats by potentially breaking classical key exchange protocols and enabling the future decryption of stored ciphertext. In this paper, we present a systematic vulnerability analysis of coherent optical networks that use OTU4 framing, Media Access Control Security (MACsec), and 400G ZR+ transceivers. Guided by established risk assessment methodologies, we uncover critical weaknesses affecting management plane interfaces (e.g., MDIO and PJC) and overhead fields (e.g., Trail Trace Identifier, Bit Interleaved Parity). To mitigate these risks while preserving the robust data throughput and low-latency demands of industrial automation, we propose a post-quantum security framework that merges spectral phase masking with multi-homodyne coherent detection, strengthened by quantum key distribution for key management. This layered approach maintains backward compatibility with existing infrastructure and ensures forward secrecy against quantum-enabled adversaries. The evaluation results show a substantial reduction in exposure to timing-based exploits, overhead field abuses, and cryptographic compromise. By integrating quantum-safe measures at the optical layer, our solution provides a future-proof roadmap for network operators, hardware vendors, and Industry 4.0 stakeholders tasked with safeguarding next-generation manufacturing and engineering processes.  
MACsec; OTU4; quantum key distribution; optical networks +
Footnote †: journal: Information  
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_Article_
## 2 Preliminaries
### Exploitation Strategy  
The exploitation rate, within the context of intelligent optimization algorithms, signifies the likelihood or frequency of leveraging known high-quality solutions. A higher exploitation rate implies a greater propensity for utilizing established reasonable solutions with the anticipation of further optimization around them. Typically, this rate is achieved by selecting the best solution or weighting the selection based on solution quality. While a higher exploitation rate aids rapid convergence towards local optima, it also raises the risk of entrapment in local optima, potentially overlooking the global optimum. Hence, selecting the appropriate exploitation strategy is crucial.  
MOFECO-MS utilizes a mutation operator to enhance exploitation capability, similar to the crossover operator. This mutation operator is bifurcated into two segments: mutation of the route plan and mutation of the item plan. For the route plan, we employ the swap operator [29]; for the item plan, we introduce a novel heuristic operator proposed in this paper.  
Within the heuristic operator, the first step involves computing the remaining knapsack capacity, denoted as \(restofWeight\). Subsequently, we identify those whose weights are lower than the remaining knapsack capacity from the untaken items. Next, randomly select an item meeting the weight criteria and place it into the knapsack to augment profit. The pseudocode outlining the heuristic operator is delineated in Algorithm 4.  
```
0:\(Q\), \(\mathbf{z}_{ij}(k),weights\)
0:\(\mathbf{z}_{ij}(k)\)
1:\(YesItems\Leftarrow find(\mathbf{z}_{ij}(k)==1)\)
2:\(resoftWeight=Q-sum(weights(YesItems))\)
3:\(NoItems\Leftarrow find(\mathbf{z}_{ij}(k)==0)\)
4:\(Am\Leftarrow find(restofWeight>weights(NoItems))\)
5:if\(Am\
eq 0\)then
6: Randomly select one item \(t\) from \(Am\)
7:\(z_{t,ij}(k)\gets 1\)
8:endif
9: Return \(\mathbf{z}_{ij}(k)\)
```  
**Algorithm 4** Heuristic operator  
Indeed, the exploitation phase is also segregated into expanded and narrowed exploitation. Expanded exploitation involves mutating any of the current optimal elements \(\mathbf{x}_{aps}(k)\), while narrowed exploitation entails mutating an element that is potentially a good solution in the exploitation phase, represented by \(\mathbf{x}_{gs}(k)\). The frequency of expanded and narrowed exploitation is determined by the probability \(p_{m}\). The pseudocode outlining the exploitation phase is depicted in Algorithm 5. Notably, the probability of expanded exploitation is \(p_{m}\), while the probability of narrowed exploitation is \(1-p_{m}\).  
```
0:\(\mathbf{x}_{gs}(k),\mathbf{x}_{aps}(k)\),
0:\(\mathbf{x}_{new}(k)\)
1:if\(rand<p_{m}\)then
2:\(\mathbf{x}_{new}(k)\Leftarrow\texttt{Mut}(\mathbf{x}_{aps}(k))\)
3:else
4:\(\mathbf{x}_{new}(k)\Leftarrow\texttt{Mut}(\mathbf{x}_{gs}(k))\)
5:endif
6: Return \(\mathbf{x}_{new}(k)\)
```  
**Algorithm 5** Exploitation phase
## 2 Results
### Correlation between SIF and GPP  
In the subtropical evergreen coniferous forest, the correlation between the SIF of vegetation and GPP became more significant with the increase in temporal scales (Figure 3). On the daily scale, SIF and GPP showed a nonlinear correlation, and SIF only explained 23% of the changes to GPP. On the 8-day scale and the monthly scale, the correlation between SIF and GPP became more linear. On the 8-day scale, the correlation between SIF and GPP was 0.60, and the slope was 12.50. On the monthly scale, the correlation between SIF and GPP attained 0.76, and the slope was 18.43.
## 5 IoT Service Provisioning via NDN in the Smart Campus
### A Toy Example  
In order to better figure out how the proposed forwarding strategy works, let us refer to the toy example reported in Figure 4.  
**Service executed on-path**: A consumer \(C\) transmits a service interest (labeled S-INTEREST in Figure 4a) to request a named service which is routed towards the data provider, \(P\). The issued interest conveys the estimated service provisioning time in case the service is executed in the remote cloud. Each crossed node along the path estimates the service provisioning time and replaces the current value of the SPT field in the forwarded interest only if the time is lower than the one carried in the received interest. The interest is then forwarded until it reaches the PAN. In the example, the PAN is not able to execute the service within the shortest time but a node in the path (the CAN in the figure) is able to execute it. Thus, it transmits an ACK back. Once the ACK packet reaches the node that advertised the lowest SPT, it becomes the executor. Therefore, it starts the data retrieval from the IoT data producer through legacy NDN interest/data packets. Once data are collected, the executor performs the requested computation and sends the result to the consumer using the same name as the original service interest.  
**Service executed off-path**: In such a case (Figure 4b), the NDN-fog nodes of the domain cannot ensure a service provisioning time shorter than the cloud; hence, a NACK is sent back by the on-path nodes. The CAN, then, issues a service interest towards the cloud. All nodes forward the packet by overwriting the SPT field whenever possible. The root node detects the availability of an executor (\(N_{2}\)) and sends an ACK back. \(N_{2}\) is then in charge of retrieving data from the provider \(P\) and of sending the output of the computation to \(C\).
## 8 AM Techniques for Crash Boxes
### Fdm  
The FDM AM technique has been widely explored in studies to enhance the energy absorption characteristics of crash boxes [114; 115; 116].  
One of the key parameters in FDM additive manufacturing is the infill pattern, which significantly influences the mechanical properties and crashworthiness performance of printed objects. [PERSON] et al. [117] investigated the effect of infill pattern on the crashworthiness of tubes made from PLA, employing five different infill patterns, circular, square, triangle, zigzag, and cross, all with a 50% infill density, as shown in Figure 22a. The results reveal that the square infill pattern provides the highest values for \(PCF\) (24.38 kN), \(MCF\) (20.58 kN), \(E_{\text{T}}\) (673.38 kJ), and \(SEA\) (26.52 J/g), while the zigzag infill pattern achieves the highest \(CFE\) value of 0.91. Both [PERSON] and [PERSON] [118] and [PERSON] et al. [119] conducted comparative studies examining the energy absorption performance of structures manufactured using FDM and metal materials. [PERSON] and [PERSON] [118] explored the energy absorption of a honeycomb lattice structure fabricated from PLA using FDM. Their findings reveal that the \(SEA\) of the PLA honeycomb structure (0.25 J/g) outperforms an aluminum counterpart, which exhibits an \(SEA\) of 0.182 J/g. [PERSON] et al. [119] investigated the performance of a cubic lattice structure produced with carbon-fiber-reinforced nylon  
Figure 21: SLS: (**a**) origami-inspired designs [112]; (**b**) lattice structures [113].  
using FDM. They report that the \(SEA\) increases with beam diameter and decreases with unit-cell size, with the design offering a 25% weight reduction compared to conventional steel components. Exploring bio-inspired structures manufactured using the FDM technique, [PERSON] et al. [120] examined the mechanical behavior of a Voronoi structure inspired by the microstructure of the Paracentrotus lividus sea urchin shell. PLA specimens were fabricated using FDM, with variations in rod thickness, node count, and edge smoothness. The results show that the \(SEA\) improves as these geometric parameters' values increase, with the highest \(SEA\) recorded at 8.85 \(\pm\) 0.27 J/g.
## 2 Materials and Methods
### Data Preparation and Basic SVM Prediction Model  
#### 2.1.1 Three-time Stages Related to Transient Stability  
The electromechanical transient stability of the power system is related to three-time stages, pre-fault (steady state), during-fault and post-fault, as shown in table Figure 1. The pre-fault stage reflects the initial operation state of the system, the during-fault stage reflects the severity of the fault disturbance, and the post-fault stage reflects the dynamic performance of the system after the fault is cleared.  
Since the power system is a complex non-linear system, its transient stability is not only related to the post-fault information, but also influenced by the steady-state operation point of the power system and the severity of disturbance during the fault. In order to analyze the different contributions of the variables during these three time stages to the system transient stability, the three time stages are grouped into 4 series of sampling data, as shown in Figure 2.  
Comparisons on these 4 series of sampled data are tested on the IEEE 39-bus system, which is shown in Figure 3, using the dual-axis generator model, IEEE DC Exciter Type 1 exciter model, and constant-impedance load model. The simulation software is MATLAB toolbox PST3.0 [35].  
Figure 1: Three-time stages related to transient stability.  
Figure 2: Different sampling series.  
The transient instability criterion is set as, if the maximum rotor angle difference between any pair of generators exceeds 180\({}^{\circ}\) at the end of the transient simulation (such as 5s), the case will be recognized as unstable [36]. Figure 4 shows the bus voltage amplitude curves of a typical transiently stable sample (in blue) and an unstable sample (in red), with the four series of sampled data. Here, the fault occurred at 0.10 s and was cleared at 0.20 s in all cases. For the stable sample, the load is 0.9 times the base load level, the faulted line is 1-39, and the fault location is 10% from bus-39 side. For the unstable sample, the load is 1.1 times the base load level, the faulted line is 21-22, and fault location is 40% from the bus-21 side.  
It can be seen from Figure 4 that the first three series cannot fully reflect the exact dynamic behavior of the power system, due to the loss of information. In order to better characterize power system transient performance, it is necessary to utilize the sampling data covering all three time stages, that is, the stages of pre-fault, during-fault and post-fault, as shown in Series 4 of Figure 2. It should also be noted that in actual power systems, the typical sampling frequency for the fundamental phasor of the trajectory variables is 10 ms for the PMUs in the wide area measurement system (WAMS).  
Figure 4: Voltage curves of all buses for the four-sampling series.  
Figure 3: Topology of the IEEE 39-bus system.
## 2 Named Data Networking: Nuts and Bolts
### Security Principles in NDN
#### 2.1.3 Trust Model  
The _Data_ packet's signature validates the authenticity of the received content and its origin. However, it does not indicate whether the signer has the authorization to produce the received content [16]. To verify whether a producer is authorized to generate data under a given namespace, consumers require a mechanism to check if the producer's key has the right to sign a _Data_ packet under that namespace [17]. Application-based trust policies define which keys have the authorization to sign which _Data_, as illustrated in Figure 2. Furthermore, applications can use access control policies to protect the _Data_ packet's content through encryption and permit only authorized nodes to decrypt it [18].
# Road Extraction Based on Improved Convolutional Neural Networks with Satellite Images
###### Abstract
In addition, different structures of images can be plugged into the mode [15]. Consequently, an improved method was proposed by adjusting the kernel size with multiscale context aggregation to develop the road extraction precision [16]. Low-level road extraction from satellite images presents many difficult tasks for mapping complex scenes in the remote sensing field. For instance, roads are always narrow and only occupy a tiny part of the whole image, so capturing this global feature is a difficult job. Further, different kinds of roads have different surface features, such as diverse roads, expressways, urban roads, mountain roads, and country roads. In addition, road areas are covered with trees, cars and shadows in satellite images. The above factors make automated extraction of roads from satellite images a difficult task. Many researchers had applied deep learning method to information extraction with the remote sensing images, and proposed effective results [17; 18; 19]. Further, classification has been given more attention in the deep learning field [20; 21]. Obviously, road extraction can be taken as a classification problem, and each pixel is distinguished as in or out of a road [7]. In this research, an improved network has been proposed combining dilated convolution layers and previous layers in a building block, and D-LinkNet and the D-Dense block are also introduced in networks for low-level road extraction. Meanwhile, the advantage of dilated convolution and the D-Dense block can come with challenges. Further, the presented network has been tested through controlled experiments with the research dataset and the public dataset. The experiments show that the improved networks with the D-Dense block can develop pixel-level accuracy for semantic segmentation, and model size and IoU are better than that of other models after comparison.
## 2 Material and Methods  
### Study Site  
Two EC flux towers integrated into the FLUXNET micrometeorological tower sites [6] have been selected for this research.  
The first one is situated in Gebesee (Germany) in an experimental agricultural area which has been cultivated since 1970 mainly with cereals, potato and sugar beet. More details on the site can be found in [PERSON] et al. [7]. The study period analyzed of this flux tower is from 1 January 2001 to 31 December 2014 in which has been cultivated rainfed crops such as wheat, barley and potato. The area is characterized by a temperate climate (Cfb) according to Koppen-Geiger classification.  
The second one is placed in El Saler-Sueca in Valencia (Spain) in a rice crop [8]. The area is characterized by a Mediterranean climate (Csa) and the available data of this flux tower site is from 1 January 2004 to 31 December 2010.  
The following pre-processed daily variables were used: (1) air temperature (Ta, \({}^{\circ}\)C); (2) vapor pressure deficit (VPD, hPa); (3) precipitation (PPT, mm day\({}^{-1}\)); (4) soil water content (SWC, % vol); (5) latent heat (LE, W m\({}^{-2}\)) (6) sensible heat (H, W m\({}^{-2}\)); and (7) gross primary productivity (GPP_T, g C m\({}^{-2}\) day\({}^{-1}\)).
# Assessment of Diagnostic Radiology Facilities Technical Radiation Protection Requirements in KSA
###### Abstract  
The national regulatory body in the state regulates the source of ionizing radiation such as diagnostic radiology to minimize exposure to the operators and the patients. The Saudi Food and Drug Authority (KSA-SFDA) cover all medical regulatory aspects of ionizing radiation such as diagnostic X-ray facilities for all practices and intervention requirements. The study presents an assessment and analysis of the level of technical radiation protection requirements and the status of applying the national regulatory standards for different diagnostic facilities in KSA. Based on the online scientific recent database published in the field of radiation protection regulations and dose assessment for diagnostic radiology in KSA, and from the data published by the KSA-SFDA report in 2015. About 109 diagnostic X-ray facilities were selected from 35 governorates distributed in the kingdom. More than 95% of the examined facilities were in good condition concerning the national radiological protection technical regulation. About 11.9% of the facilities had a radiation leakage or cracks in the wall of the X-ray room/entrance door of the room. 16.5% of the facilities did not have a radiation warning sign written in Arabic/English languages. About 21.9% of the operators did not use any personal radiation dosimeter such as TLD or OSL. More than 40.7% of those facilities do not keep a record of the personal dosimeter reading at their facilities. Only 11.1% of the examined facilities do not have any personal protective tools such as a lead apron or thyroid shield. About 38.2% of the examined facilities do not carry out the annual periodic maintenance for the used X-ray machines at those facilities. From the obtained results, it was concluded that the majority of the radiation protection, radiation safety requirements and physical security measures undertaken in these facilities were in good implementation of the national and international technical regulations. The study suggests a need to apply for a quality control test procedures program regularly at those facilities.  
radiation protection; diagnostic X-ray safety and regulation; X-ray room shielding; quality control +
Footnote †: journal: Applied Sci.  
Nations Scientific Committee on the Effects of Atomic Radiation (UNSCEAR) estimates that the number of X-ray machines examined worldwide every year accounts for 3.6 billion diagnostic radiographs; accordingly, the international radiological survey is important for diagnostic radiology machine inspection [4; 5]. The responsibility of the medical physicist in the medical imaging field is to prepare the records and report documents inside the facility. The most important reports include the inventory report, the quality control test document, the calibration of the area radiation survey, and the record of the personal dosimetry for the radiation workers at such facility [6].
# RBF-Based Monocular Vision Navigation for Small Vehicles in Narrow Space below Maize Canopy
###### Abstract  
Maize is one of the major food crops in China. Traditionally, field operations are done by manual labor, where the farmers are threatened by the harsh environment and pesticides. On the other hand, it is difficult for large machinery to maneuver in the field due to limited space, particularly in the middle and late growth stage of maize. Unmanned, compact agricultural machines, therefore, are ideal for such field work. This paper describes a method of monocular visual recognition to navigate small vehicles between narrow crop rows. Edge detection and noise elimination were used for image segmentation to extract the stalks in the image. The stalk coordinates define passable boundaries, and a simplified radial basis function (RBF)-based algorithm was adapted for path planning to improve the fault tolerance of stalk coordinate extraction. The average image processing time, including network latency, is 220 ms. The average time consumption for path planning is 30 ms. The fast processing ensures a top speed of 2 m/s for our prototype vehicle. When operating at the normal speed (0.7 m/s), the rate of collision with stalks is under 6.4%. Additional simulations and field tests further proved the feasibility and fault tolerance of our method.  
inter-row mobile vehicle; monocular vision; path identification; radial basis function (RBF) +
Footnote †: journal: Applied Sciences