esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Intelligent Connected Vehicles (ICVs) impose stringent requirements on real-time computational services. However, limited onboard resources and the high latency of remote cloud servers restrict traditional solutions. Unmanned Aerial Vehicle (UAV)-assisted Mobile Edge Computing (MEC), which deploys computing and storage resources at the network edge, offers a promising solution. In UAV-assisted vehicular networks, jointly optimizing content and service caching, computation offloading, and UAV trajectories to maximize system performance is a critical challenge. This requires balancing system energy consumption and resource allocation fairness while maximizing cache hit rate and minimizing task latency. To this end, we introduce system efficiency as a unified metric, aiming to maximize overall system performance through joint optimization. This metric comprehensively considers cache hit rate, task computation latency, system energy consumption, and resource allocation fairness. The problem involves discrete decisions (caching, offloading) and continuous variables (UAV trajectories), exhibiting high dynamism and non-convexity, making it challenging for traditional optimization methods. Concurrently, existing multi-agent deep reinforcement learning (MADRL) methods often encounter training instability and convergence issues in such dynamic and non-stationary environments. To address these challenges, this paper proposes a MADRL-based joint optimization approach. We precisely model the problem as a Decentralized Partially Observable Markov Decision Process (DecPOMDP) and adopt the Multi-Agent Proximal Policy Optimization (MAPPO) algorithm, which follows the Centralized Training Decentralized Execution (CTDE) paradigm. Our method aims to maximize system efficiency by achieving a judicious balance among multiple performance metrics, such as cache hit rate, task delay, energy consumption, and fairness. Simulation results demonstrate that, compared to various representative baseline methods, the proposed MAPPO algorithm exhibits significant superiority in achieving higher cumulative rewards and an approximately 82% cache hit rate.  
U
## 5 Conclusions  
We investigated the positional accuracy of UAV geospatial products and analyzed the influences of flight direction and camera orientation on this accuracy. For this purpose, many sets of DSMs and orthophoto mosaics were created by processing UAV images without GCPs and then with five GCPs at different combinations of flight directions and image acquisition angles. In this study, 474 images were acquired from a DJI Mavic 2 multi-rotary UAV equipped with a Hasselblad camera during three flight missions. The workflow in this project was conducted using a case study on the west bank of the Euphrates river in Iraq. The flight altitude was set at 60 m and the overlap in the forward and lateral directions was set at 75% and 70%, respectively. For the first two missions, the images were acquired with a vertical (90\({}^{\circ}\)) image acquisition angle, and for the third mission, an oblique (70\({}^{\circ}\)) image acquisition angle was set.  
The calculated root means square errors (RMSEs) in the horizontal and vertical planes showed that the horizontal accuracy was higher than the vertical accuracy in all cases. This result supports previous studies that have looked at the horizontal and vertical accuracies of UAV geospatial data. In this study, it was shown that processing UAV imagery using a direct georeferencing method for low-cost UAVs resulted in low-accuracy geospatial data products even at low altitudes. The RMSEs (X, Y, Z, and total) for geospatial data products in the first case were 1.830, 1.203, 2.885, and 3.622 m, respectively. The indirect georeferencing approach led to high accuracy, where the RMSEs decreased to several centimeters. On the other hand, this study found that a fusion of two sets of images acquired with different acquisition angles and flight directions could significantly improve positional accuracy. This result was evident from the analysis of the RMSEs (X, Y, Z, and total) of the geospatial products for the last ten cases. The highest positional accuracy for these ten cases came from the eleventh case, where the processed imagery, in this case, combined imagery acquired with a 70\({}^{\circ}\) acquisition angle with longitudinal flight direction and imagery acquired with a 70\({}^{\circ}\) acquisition angle with a transversal flight direction. The X, Y, Z, and total RMSEs for this case were 0.008, 0.009, 0.028, and 0.031 m, respectively.  
Conceptualization, S.A.; Formal analysis, S.A. and W.A.; Methodology, S.A.; Project administration, A.E.-S.; Supervision, A.E.-S., F.A. and W.A.; Validation, S.A.; Visualization, S.A.; Writing--original draft, S.A.; Writing--review & editing, F.A. and W.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 2 Results and Discussion
### Analysis of the Volatiles in P. Nobby's Pacific Sunset Flowers
#### 2.2.2 Volatile Compounds of \(P\). Nobby's Pacific Sunset Flowers in a Blooming Cycle  
As shown in Figure 4, the volatile components content peaked on day 8, slowly decreasing thereafter. The ratios of the main compounds \(\alpha\)-farnesene, geraniol and linalool also changed on different days. Linalool levels were reduced to 30% whereas geraniol increased to 30%. Therefore, it can be obviously found that the aroma of \(P\). Nobby's Pacific Sunset will change as a result of the flowering days (Figure 5). [PERSON] _et al._[34] reported that the highest aroma of \(P\). _bellina_ occurred during days 5-7. [PERSON] _et al._[35] also indicated that for _Oncidium_ Sharry Baby, during flower blooming and senescence, the aroma contents of alkenes, alcohols and esters increased, whereas the contents of aldehydes, ketones and alkanes decreased. [PERSON] _et al._[36] sought to determine the optimal timing for harvesting _Rosa damascena_ flowers. Thus, the flowers were divided into eight different developmental stages for analysis. The results showed that the best harvesting time is during full bloom when the aroma is at its highest concentration but does not contain the carcinogenic component, methyl eugenol. [PERSON] _et al._[37] reported the vase-life of _Narcissus tazetta_ var. _chinensis_ Roem, the total volatile component content of which peaked on day 2 for single-flowered and day 3 for double-flowered narcissus, with both decreasing significantly by day 4.  
Figure 3: Changes in the major volatile components of \(P\). Nobby’s Pacific Sunset over a 24 h period.
## 2 Overview of Machine Learning Principles
### Evaluation and Validation Strategies
#### 2.2.1 Evaluation Metrics for Classification Tasks  
Classification studies of student performance prediction, in which the aim is to determine the student performance according to classes (i.e., Low/Medium/High, or Pass/Fail, etc.), generally consider accuracy based on correct and misclassified samples [42; 43]. The accuracy is obtained by dividing the total number of correctly classified samples by the total number of samples within the test set. However, there are limitations in terms of accuracy if the dataset is imbalanced. If any class or output of the dataset has significantly more or fewer samples than the other classes, the obtained accuracy prevents the models' ability from being evaluated [44; 45]. The formula for accuracy is shown in Equation (1).  
\[Accuracy=\frac{TP+TN}{FP+FN+TP+TN} \tag{1}\]  
where \(TP\), \(FP\), \(TN\), and \(FN\) denote the true positive, false positive, true negative, and false negative values obtained by the model.  
Even though uncertainties remain in measuring the results obtained on imbalanced data, the receiver operating characteristics area under curve (ROC AUC) is one of the most commonly used metrics, especially for two-class imbalanced data [45]. It is used to measure the ability of the model using the area under the curve of the recall (sensitivity/true positive ratio) and the false positive ratio.  
Another common metric is the F1 score. It is defined as the harmonic mean of precision and recall [45]. The F1 score is one of the metrics commonly used in both binary and multiclass problems with imbalanced data. The formula of the F1 score is shown in Equation (2):  
\[F1\ Score=\frac{TP}{TP+\frac{1}{2}(FP+FN)} \tag{2}\]  
The other evaluation metrics of classification tasks can be listed as recall (sensitivity), specificity, and precision, which are used to measure the particular abilities of the models in detecting specific output classes. The formulae for recall, specificity, and precision are shown in Equations (3)-(5).  
\[Recall=\frac{TP}{TP+FN} \tag{3}\]  
\[Specificity=\frac{TN}{TN+FP} \tag{4}\]  
\[Precision=\frac{TP}{TP+FP} \tag{5}\]
# Wave Propagation in Laminated Cylinders with Internal Fluid and Residual Stress
## Appendix A WFE Eigenvalue Problem  
This appendix provides a brief summary of the 2D WFE eigenvalue problem formulation for axisymmetric structures. For further details, the reader can refer to [25]. If the dynamic stiffness matrix \(\mathbf{D}\) in Equation (4) is partitioned into submatrices corresponding to the super-nodes \((\mathbf{q}_{1},\mathbf{q}_{2},\mathbf{q}_{3},\mathbf{q}_{4})\), i.e.,  
\[\mathbf{D} = \left[\begin{array}{cccc}\mathbf{D}_{11}&\mathbf{D}_{12}&\mathbf{ D}_{13}&\mathbf{D}_{14}\\ \mathbf{D}_{21}&\mathbf{D}_{22}&\mathbf{D}_{23}&\mathbf{D}_{24}\\ \mathbf{D}_{31}&\mathbf{D}_{32}&\mathbf{D}_{33}&\mathbf{D}_{34}\\ \mathbf{D}_{41}&\mathbf{D}_{42}&\mathbf{D}_{43}&\mathbf{D}_{44}\end{array} \right], \tag{18}\]  
then the reduced WFE eigenvalue problem in Equation (11) becomes  
\[\begin{array}{c}[(\mathbf{D}_{11}+\mathbf{D}_{22}+\mathbf{D}_{33}+\mathbf{ D}_{44})\lambda_{a}\lambda_{y}+(\mathbf{D}_{12}+\mathbf{D}_{34})\lambda_{a}^{2} \lambda_{y}\\ +(\mathbf{D}_{13}+\mathbf{A}_{2})\lambda_{a}\lambda_{y}^{2}+\mathbf{D}_{32} \lambda_{a}^{2}+\mathbf{D}_{23}\lambda_{y}^{2}+(\mathbf{D}_{21}+\mathbf{D}_{ 43})\lambda_{y}\\ +(\mathbf{D}_{31}+\mathbf{D}_{42})\lambda_{a}+\mathbf{D}_{14}\lambda_{a}^{2} \lambda_{y}^{2}+\mathbf{D}_{41}]\mathbf{q}_{1}=\mathbf{0}.\end{array} \tag{19}\]  
For closed cylindrical structures, \(\lambda_{a}=e^{-in\mathbf{a}}\) is known for a given circumferential order \(n\); therefore, Equation (19) becomes, for a given \(\omega\), a quadratic eigenproblem in \(\lambda_{y}\) of the form  
\[\Big{[}\mathbf{A}_{2}\lambda_{y}^{2}+\mathbf{A}_{1}\lambda_{y}+\mathbf{A}_{0} \Big{]}\mathbf{q}_{1}=0, \tag{20}\]  
where  
\[\begin{array}{c}\mathbf{A}_{2}=\mathbf{D}_{23}+(\mathbf{D}_{23}+\mathbf{D}_ {24})\lambda_{a}+\mathbf{D}_{14}\lambda_{a}^{2};\\ \mathbf{A}_{1}=\mathbf{D}_{21}+\mathbf{D}_{43}+(\mathbf{D}_{11}+\mathbf{D}_{ 22}+\mathbf{D}_{33}+\mathbf{D}_{44})\lambda_{a}+(\mathbf{D}_{12}+\mathbf{D}_ {34})\lambda_{a}^{2};\\ \mathbf{A}_{1}=\mathbf{D}_{41}+(\mathbf{D}_{31}+\mathbf{D}_{42})\lambda_{a}+ \mathbf{D}_{32}\lambda_{a}^{2}.\end{array} \tag{21}\]  
The standard linear companion form  
\[\mathbf{L}(\lambda_{y})=\left[\begin{array}{cc}-\mathbf{A}_{2}^{-1}\mathbf{ A}_{1}&-\mathbf{A}_{2}^{-1}\mathbf{A}_{0}\\ \mathbf{I}&\mathbf{0}\end{array}\right]-\lambda_{y}\bigg{[}\begin{array}{cc} \mathbf{I}&\mathbf{0}\\ \mathbf{0}&\mathbf{I}\end{array}\bigg{]}. \tag{22}\]  
is considered. Solutions of Equation (22) yield the relationship between the wavenumber and frequency (dispersion curves) and the DoFs \(\mathbf{q}_{1}\) of the cross-section due to the passage of a wave (wave mode shapes). The obtained wavenumbers may be purely real, purely imaginary or complex. Thus the complex frequency spectrum can be determined for any given circumferential mode order.
# A Multi-Node Detection Algorithm Based on Serial and Threshold in Intelligent Sensor Networks
## 8 Analysis of Simulation Results
### BER Analysis
Figure 6 shows the comparison of the average BER performance among the S-T-Max-log-MPA algorithm experiments: the original MPA algorithm and the T-MPA algorithm, in which the maximum number of iterations \(\rm t_{max}\) = 5 and the threshold is th = 0.60. It can be seen from Figure 6 that the BER performance of the S-T-Max-log-MPA algorithm is lower than that of the T-MPA algorithm, which is 4.26% lower than that of the T-MPA algorithm when \(\rm E_{b}\)/\(\rm N_{o}\) = 0 dB. When \(\rm E_{b}\)/\(\rm N_{o}\) = 14 dB, it is 10.397% lower than the T-MPA algorithm. The BER performance of the S-T-Max-log-MPA algorithm is higher than that of the original MPA algorithm, which is 6.47% higher than that of the original MPA algorithm at \(\rm E_{b}\)/\(\rm N_{o}\) = 0 dB. At \(\rm E_{b}\)/\(\rm N_{o}\) = 14 dB, it is 4.4997% lower than the original MPA algorithm. According to the comparison results, we can see that although the BER performance of the S-T-Max-log-MPA algorithm is higher than that of the original MPA algorithm, it is lower than that of T-MPA algorithm. Figure 5: Comparison of the average BER performance when \(\rm t_{max}\) = 5. Figure 6: Comparison of average BER performance when \(\rm t_{max}\) = 5 and th = 0.60.
# Perioperative Nutritional Support: A Review of Current Literature
## 5 Perioperative Nutritional Support in Oncological Pathology
### Other Types of Cancer  
Up to 60% of patients with head and neck cancer (HNC) present with significant malnutrition due to the oral feeding difficulties associated with this disease [113]. Between 20-50% of patients with high-risk surgical HNC will present with post-surgical complications such as wound infections, fistulas, anastomotic leaks and worse prognosis [114]. The most recent studies regarding perioperative nutrition in HNC have focused on the role that immunonutrition (especially arginine-rich formulas) might have in improving outcomes. In 2018, the Cochrane Library published a systematic review [115] which included 19 clinical trials (1099 patients) comparing the use of immunonutrients with the use of other formulas or not using them in patients with HNC. They found no significant differences in terms of wound infection or length of hospital stay, although they did find differences in relation to fistulas. In general, the quality of the studies was evaluated as low or very low, which is in agreement with previous meta-analyses by [PERSON] et al. [116] and [PERSON] et al. [117].  
There is little evidence regarding perioperative nutrition in patients with urological tumors, despite the fact that up to 55% of patients undergoing radical cystectomy (RC) and/or urinary detour (DU) present with malnutrition [118], and that this has been associated with worse prognosis and increased mortality 90 days after surgery [119]. In 2013, [PERSON] et al. [120] conducted a clinical trial with patients undergoing RC/DU comparing two groups: one had PN during the first five postoperative days and oral supplements and the other had only ONS. PN was associated with more complications in general and infectious complications in particular. In 2014, [PERSON] et al. [121] compared historical controls with patients who were given immunonutrient formulas prior to surgery, finding a reduction in postoperative complications and infection. In 2016, [PERSON] et al. [122] confirmed these results with a clinical trial of 29 patients. In 2019, a Cochrane library review [123] evaluated the role of perioperative nutrition in patients with bladder cancer undergoing surgery with RC, including eight clinical trials. The main conclusion was that studies in this regard are currently of low or very low quality and that the evidence for benefit in these patients is very limited.
## 2 LoRaWAN Network Downlink Communication
### LoRaWAN Downlink Communication Protocol  
The sequence interaction process of the downlink communication between the gateway and the server in the LoRaWAN network is shown in Figure 3. It is seen that the periodically transmit keepalive messages is needed to maintain the connection. PUSH_DATA messages are periodically sent out to inform the network server gateway of the UDP port number for receiving PULL_RESP data. The server responds to the PULL_DATA message with a PULL_ACK. Only after the interaction between the gateway and the network server is completed, the latest data can be delivered by the server. Application data is encapsulated in a JSON payload of PULL_RESP messages by the server. Lastly, the gateway decides whether to send a TX_ACK message to the network server according to the currently adopted LoRa gateway message protocol (GWMP) version. Before the network server sends data, it needs to confirm whether it has received the latest PUSH_DATA message. If the message is not received, a new PUSH_DATA needs to be sent out by the gateway.  
Figure 2: The LoRaWAN downlink modeling topology based on the SDN framework.  
PUSH_DATA and PULL_ACK are interactions that need to be completed before the server sends out data. The purpose is to ensure normal communication between the gateway and the service, and to keep the firewall of the gateway open. The sending period of the PULL_DATA message is set up by the gateway. In the downlink communication process, the network server calculates the modulation profiles (spreading factor, SF; code rate, CR; bandwidth, BW, etc.) of the downlink frame and the transmission timestamp in the downlink frame scheduling stage. When the internal clock reaches the downstream timestamp, the LoRaWAN gateway can only be programmed to leave one frame at a time. Therefore, downlink frames are buffered in a just-in-time queue. This hardware limitation exacerbates the problems caused by the half-duplex mode of the gateway and accelerates the duty cycle saturation process of downlink traffic, resulting in uneven link resource allocation in downlink routing and it is very likely to induce communication congestion.
# Multi-Level Joint Feature Learning for Person Re-Identification
## 4 Results
### Comparison with Market1501  
Comparison with the proposed method on Market-1501 is detailed in Table 3. The MFF model is compared with several state-of-the-art person re-identification methods on Market-1501 in recent years, for example, the bag of words model BoW+KISSME [33] with a hand-crafted method, the SVDNet [34] using global features extracted by deep learning model, and the part-aligned representation PAR [17] using part features extracted by a deep learning model. We can observe from Table 3 that the proposed MFF model gets best results in Rank-1 accuracy, Rank-5 accuracy and Rank-10 accuracy. In the experiment, we use mean average precision (mAP) as an evaluation index of person re-identification. The MFF model achieves 87.9% mAP on the Market-1501, which is 18.8% higher than the best proposed method. In addition, the MFF model achieves Rank-1 accuracy of 96.0%, which is 11.1% higher than the best method. Rank-5 accuracy of our model achieves 98.7%, 4.5% better than the best compared method. This is because the MFF model fuses the global features and local features together. Moreover, adding PMN when extracting local features is also helpful to obtain better results.
# Degradation Characteristics and Reliability Assessment of 1310 nm VCSEL for Microwave Photonic Link
## 3 Discussions
### Low-Frequency Noise Characterization

Table 2: The frequency index factor and noise amplitude at different bias currents. indicates that with the increase of electrical stress, the active region dislocation and oxide layer defects of the device increase, and the performance degradation becomes more serious.
## 5 Discussion  
This study employs numerical analysis and machine-learning techniques to develop a model for predicting pile axial stress during tunnel excavation adjacent to a piled raft. The numerical analysis considers various design parameters for the piled raft and tunneling, and uses settlement and pile axial stress results from multiple cases as input data for machine learning. The impact of each independent variable on the prediction model is analyzed using feature importance and SHAP values.  
During the training phase, RF, XGB, and LightGBM algorithms were applied, with LightGBM exhibiting the best performance. This trend is observed consistently across all cases, indicating that these three algorithms are the most reliable for deriving a predictive model of pile axial stress. In the validation phase, LightGBM proved to be the most accurate and exhibited the lowest RMSE in all cases, as compared with RF and XGB. In the prediction phase, LightGBM performed the best in all cases, followed by cases 3, 1, 4, and 2. For cases 1 and 2, the same variables used in the numerical analysis were employed, while a combination of features was used for cases 3 and 4. The model that does not account for settlement performed the best, but overall, the model that incorporates settlement as an independent feature performed well.  
This finding is supported by the feature importance and SHAP value analyses, which indicate that settlement is a significant variable to consider. According to the SHAP value analysis, factors that reduce the pile axial stress due to tunnel excavation include pile top, long pile, loose ground, large pile diameter, large foundation settlement, narrow pile spacing, close pile-tunnel distance, large number of piles, and large foundation width.
## 4 Validation  
This section presents several simulations that were performed in Xilinx Vivado 2019.2, considering the Zynq-7000 XC7Z020 CLG484-1 board (Xilinx, San Jose, CA, USA), to validate the Ethernet parser, the hash table, and the complete switch. For the Ethernet parser, simulations were performed to validate the reading of the IPv4 protocol and IEEE 802.1Q fields; the reading of packets with a PRP tailer; and the addition of the PRP tailer to the packets to be duplicated. Regarding the hash table, the aging algorithm was tested to validate its capability of deleting and updating the correct elements and also how the insertion of an element would affect the aging algorithm while it was running. Finally, the REDSwitch core (Figure 5) was simulated to verify that the destination port was correctly codified, so that it could select one output FIFO from each node (LAN A and LAN B), duplicating the packet.  
The packets used in the following simulations were generated using the software CatKarat (version 1.51.200) [25], except in one of the simulations, where a real packet was used. In this way, we could have packets respecting the desired parameters to perform the required simulations.  
To confirm that the CRC was correctly calculated, an online CRC calculator tool was used [26]. Different polynomials can be used to calculate a packet CRC, with CRC-32 being the most commonly used on Ethernet networks.
# _Applied sciences_
## 3 Results and Discussion
### Effect of \(\upgamma\)-Irradiation on Aflatoxin Levels
Overall, it can be inferred that \(\gamma\)-irradiation can barely be used as a means of eliminating aflat in almonds. Figure 1: Levels of aflatoxin (B1, B2, G1, and G2) solutions (5 ug/Kg) in control samples and after four \(\gamma\)-irradiation doses (1, 2, 4, and 8 kGy) (\(n=9\)). Each aflatoxin’s statistically significant alterations (\(p<0.05\)) are shown with small letters (a–e, for example). The next step was to examine whether \(\gamma\)-irradiation could prevent the formation of aflat in almonds. This can be achieved via the elimination of the fungi population prior to the production of aflat. To this end, almonds were spiked with _A. flavus_ (producing aflatoxin B1), and the samples were irradiated. According to the results, almonds irradiated with 1 kGy contained 46% less CFU/g of _A. flavus_ compared to the control sample (non-irradiated). When higher doses of \(\gamma\)-irradiation were employed, the fungi were eliminated. This resulted in almonds in which aflatoxin B1 could not be detected. As a result, it can be said that, while irradiating almonds cannot effectively remove aflat, it can efficiently remove fungi and hence prevent aflat' formation.