esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Conclusions and Future Issues  
With a view to applying a DTN to a content delivery service in daily use, this paper has proposed a routing method that is designed to reduce network loads. This new routing is based on information about the routes of fixed-route traveling nodes, such as buses. It expresses the destination of a bundle  
Figure 29: Characteristics of the proposed and other methods selected for comparison. (**a**) Percentage of reduction in the bundle arrival rate and the numbers of copies; (**b**) percentages of reduction in the number of hops and the maximum required buffer size.  
not by the identifier of each receiving terminal, but by the location information of the destination area that contains receiving terminals. It uses fixed-route traveling nodes as relay nodes. When a relay node receives a forwarding-bundle request from a sending terminal, it checks whether the bundle can reach its destination area. The paper has presented an outbound-type bundle protocol, which makes the above check possible. Using simulation, the proposed routing was compared to the existing DTN routing methods. It was shown that the proposed routing imposes smaller network loads than the existing routing methods.  
One of the future issues is to implement the proposed routing in a hardware system in order to evaluate how effective the new routing can be when it is applied to a commercial service. Other issues are to consider cases where the routes of traveling nodes need to be updated and cases where content filtering is used in coordination with the application concerned. It is also necessary to address the general issues related to the DTN, such as security, battery power consumption, the problems of low communication quality and low throughput in outdoor use, the problem of how to assign globally unique terminal addresses and standardization of bundle identifiers. In addition, it is necessary to ascertain the superiority of the proposed method by comparing it with the vehicular delay-tolerant network (VDTN) protocol, which has been studied mainly for the application to inter-vehicle communication and by evaluating it with a wider variety of simulation scenarios.  
[PERSON] designed the proposed method, developed the software prototype of the evaluation system, collected the evaluation data and wrote the initial draft of the paper. [PERSON] provided the direction for his research activities and refined the proposed method, the analysis of the evaluation results and the writing of the paper.  
The authors declare no conflict of interest.
## 4 Biosynthesis and Health Implications of Pyrrolizidine Alkaloids  
### Pyrrolizidine Alkaloid-Producing Plants  
Pyrrolizidine alkaloids (PAs) are a class of alkaloids based on the heterocyclic pyrrolidine motif (C7H13N). These carcinogenic and hepatotoxic phytotoxins occur in approximately 3% of the world's flowering plants, and their role is to act as deterrents against herbivores and parasites. They are prevalent in the plant families of Asteraceae (tribes Senecioneae, genera _Senecio_ and Eupatoriae), Boraginaceae (genera _Heliotropium_, _Echium_, and _Symphytum_), Apocynaceae, Orchidaceae, one genus of the Fabaceae (genus _Crotalaria_). Many plant species which accumulate PAs are used in traditional medicine for the treatment of various diseases, others are used as foods or fodder crops [48; 49] as seen in Figure 3.  
Food contamination by PAs occurs directly through the co-harvesting of food crops intended for humans and animals with PA-producing plants and indirectly through carryover into animal products such as honey, eggs, and milk [50]. Recently the occurrence of dehydro PAs in teas and herbal teas has gained increasing attention from the EU, due to the high levels of dehydro PAs found in commercially available teas and herbal teas in Germany and Switzerland.
## 3 Methodology
### Data Augmentation  
Given the relatively small size of the Fall Detection dataset, which includes only 463 annotated image-label pairs, a systematic data augmentation strategy was employed to artificially expand the training data and enhance the model's ability to generalize. Augmentation serves to mitigate overfitting by introducing controlled randomness into the training samples, simulating real-world variability in human posture, orientation, lighting, and scale. This step is crucial in privacy-preserving contexts like federated learning, where collecting and sharing new data is often restricted.  
1. Augmentation Pipeline Design  
The augmentation pipeline was implemented using the Keras.Sequential API, configured to apply a series of stochastic image transformations. The transformations applied to each image are as follows:  
* RandomFlip (horizontal): Mirrors the image along the vertical axis, simulating orientation changes of human subjects.
* RandomRotation (0.05): Introduces slight rotational shifts up to \(\pm\)5%, mimicking variations in camera angle or user posture.
* RandomZoom (0.05): Applies minor zoom-in or zoom-out effects, accounting for different spatial distances between the subject and camera.
* RandomContrast (0.05): Adjusting contrast by \(\pm\)5%, reflecting variations in ambient lighting conditions.  
These transformations were chosen for their ability to generate realistic visual variability without distorting the critical structural features necessary for fall detection. The stochastic nature of these operations ensures that each augmented image maintains semantic fidelity while appearing to be visually distinct from its original counterpart.  
2. Augmentation Function and Dataset Expansion  
A custom augmentation function, augment_dataset(X, y, mult = 2), was developed to generate multiple augmented instances from the original training set. The mult parameter controls the augmentation factor: for instance, setting mult = 2 results in each original image being duplicated with one augmented variant, effectively doubling the training set size.  
This function was applied only to the training subset to preserve the integrity of the validation and test performance subsets. After augmentation, the final dataset dimensions were as follows:  
* Training Set (after augmentation): 872 samples of size (128, 128, 3)
* Validation Set: 54 samples
* Test Set: 55 samples  
The original image-label pairs were first processed by extracting cropped regions around labeled human subjects, using YOLO bounding boxes. Each crop was resized to a standardized shape of 128 \(\times\) 128 pixels and normalized to the range [0, 1] [0, 1] [0, 1]. Following this preprocessing, the augmentation was applied exclusively to the training subset to reinforce model robustness during federated learning.  
By incorporating augmentation, the diversity and volume of training data were significantly improved, enabling the deep learning models--particularly MobileNetV2 and VGG16--to generalize better across unseen data, while maintaining data privacy constraints.
## 3 Results and Discussion
### Physiological Characterization
#### 3.3.2 Alcohol Compounds  
Alcohol compounds play a key role in shaping wine structure and enhancing flavor complexity. Among them, higher alcohols are important secondary metabolites formed during fermentation; their moderate presence can enhance aroma complexity and improve wine body richness [31]. In this study, 18 higher alcohols were identified, with the overall content of higher alcohols in the JH group exceeding that in the JS group, indicating a superior flavor profile. Specifically, the content of 3-methyl-1-butanol increased from 4841.99 \(\upmu\)g/L in the JS group to 5199.39 \(\upmu\)g/L in the JH group. This compound has been proven to accumulate during fermentation and impart banana-like fruity notes, potentially enhancing overall flavor complexity and improving the wine's sensory profile [32]; the phenylethanol levels remained consistently high in both groups (6896.69 \(\upmu\)g/L in the JS group and 6636.33 \(\upmu\)g/L in the JH group), contributing to rich rose and honey-like aromas that enhance the wine's floral and sweet characteristics [33]. Notably, 2,3-butanediol (32.17 \(\upmu\)g/L) was exclusively detected in the JH group, accumulating significantly during fermentation. This may be attributed to the metabolic characteristics of _Pichia kudriavzevii_ GAAS-JG-1 under mixed fermentation conditions. Studies have reported that the production of 2,3-butanediol is typically associated with high yeast fermentation activity and imparts fruity and buttery notes to fruit wine [34]. The presence of this compound in the JH group may therefore contribute to enhancing the wine's flavor complexity. Previous research suggests that higher alcohols at concentrations below 300 mg/L can optimize wine quality, whereas excessive accumulation (>400 mg/L) may exert a negative impact on flavor [35]. In this study, the higher alcohol content in the JH group remained well below this threshold, indicating a favorable concentration range. This not only prevented adverse effects caused by excessive accumulation but also allowed higher alcohols to play a positive role in enhancing flavor complexity and improving mouthfeel structure. In conclusion, the JH group effectively enhanced the higher alcohol content within an optimal range, improving the aroma complexity and wine body richness while maintaining the overall flavor balance. These results highlight the potential of mixed fermentation to regulate higher alcohol metabolism and optimize the sensory quality of fruit wine.
## 5 Passive Transparent Antenna Results and Discussion  
As described in Section 2, metal strips were used in passive antenna designs to achieve beam tilt in three specific orientations (State 1, 2, and 3), as illustrated in Figure 7, which provides an example of the configuration for State 3.  
Figure 5: Efficiency of the opaque passive Vivaldi antenna operating in states 1, 2, and 3.  
Figure 6: Fabricated transparent Vivaldi antenna: (**a**) clean room, zoomed view by optical microscopy (scale bar = 500 μm); (**b**) a mesh petal.  
The measurement results presented in Figure 8 demonstrate the performance of the passive transparent antenna in different operating states (1, 2, and 3). The passive transparent prototypes exhibit improved matching compared to the results in Figure 3 for the opaque prototype. This probably occurs due to the presence of a metallic grid with very thin meshes. The antenna shows operation within a frequency range from 15 to 48 GHz for state 1, state 2's operation occurs from 21 to 48 GHz, and state 3 works from 19 to 45 GHz with an S\({}_{11}\) magnitude below \(-\)10 dB.
# Antijamming Improvement for Frequency Hopping Using Noise-Jammer Power Estimator
## 2 Proposed Estimator and Detector  
In FHSS systems for military communications, the same data are repeatedly transmitted over several slots and combined in the receiver to mitigate jammers. When jammer power is strong, and jammed slots are not discarded, BER performance degrades. Thus, the detection of jammed slots is important. In FHSS systems, however, frequency-selective fading and unequal frequency responses cause different slot powers and make the detection of jammed slots difficult. In this section, we show the developed NJPE and the jammer detector based on gamma distribution of NJPE. The proposed detector can detect both narrowband and wideband jammers. Figure 1 demonstrates the proposed method that detects and discards jammed slots, and combines the remaining slots.
# Considering the Optimization Design of Urban Bus Network Scheduling
## 1 Introduction
Overall, the optimization research of bus networks has achieved numerous excellent research results in both theoretical methods and model construction as well as algorithm development, providing a theoretical research basis for the optimization design work of this paper's network. However, these achievements still have some shortcomings in their practical application:  
(1) Ignored Passenger Route Selection: Both domestic and international scholars often assume that passengers choose the shortest time or the fewest transfers when optimizing bus networks. However, when multiple paths between the same origin-destination (OD) stations meet the passengers' criteria, passengers may selectively choose different routes to travel. (2) Did Not Consider the Impact of Scheduling on Bus Network Optimization: Previous research has often overlooked the influence of route scheduling on passenger route selection during the network layout phase. When calculating the total travel cost for passengers, waiting costs at bus stops were not taken into account. This incomplete consideration in calculating the total time cost for passengers is directly related to vehicle scheduling because passengers' waiting costs are directly correlated with scheduling. To help readers better understand the structure and content of this paper, the technical roadmap of chapter contents is illustrated in Figure 1. A brief overview of each chapter is provided below:  
Section 2 introduces the construction and solution of the integrated optimization model for the bus network and scheduling. It first defines the research scope of the bus network problem in this paper, presents the assumptions for model construction, and then describes the objective function and constraints of the integrated optimization model. Based on the construction of an actual-virtual bus network, which balances the interests of both passengers and operators, the chapter establishes the integrated optimization model. Section 3 applies [PERSON]'s algorithm to analyze reachable paths between the same origin-destination (OD) pairs. The paths are sorted based on their weights, and the parameter K is flexibly set to accommodate different ODs. Since such problems are typically NP-Hard and involve high computational difficulty and complexity, intelligent optimization algorithms are usually employed. Given the computational complexity of large-scale bus network construction, we chose the efficient genetic algorithm to solve the integrated optimization model. Section 4 validates the effectiveness of the integrated optimization model for a bus network and scheduling based on the genetic algorithm through specific case studies. By setting weights between stations, the algorithm's effectiveness in the optimization process was verified. The cases were optimized in stages and synchronously, with results compared between them. Finally, sensitivity analysis was conducted focusing on different stakeholders, confirming the conclusion that cost reduction effects are more significant.
# MFCosface: A Masked-Face Recognition Algorithm Based on Large Margin Cosine Loss
## 4 Experimental Results
### Attention Mechanism Experiment  
The basic network Inception-ResNet-v1 is mainly composed of the Reduction module and the Inception-ResNet module. We set up the attention mechanism experiment based on different modules--that is, we added the CBAM attention mechanism to different modules. Table 5 shows the experimental results, where CBAM_Reduction represents CBAM being added to the Reduction module. The network structure is shown in Figure 10a (take the Reduction-A module as an example). V represents the padding mode as valid; Att-Inception means that CBAM was added to the Inception-ResNet module. The network structure is shown in the Figure 10b (take the Inception-ResNet-A module as an example). CBAM_All is where CBAM was added to all modules.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Location** & **Dataset** & **Noise Type** & **Example** & **Method** & **Accuracy** \\ \hline UP & LFW & salt and pepper noise (probability = 5\%) & FaceNet & 84.55 \\ Down & LFW & salt and pepper noise (probability = 5\%) & FaceNet & **97.00** \\ All & LFW & salt and pepper noise (probability = 5\%) & FaceNet & 82.05 \\ All & LFW & salt and pepper noise (probability = 5\%) & FaceNet & 77.45 \\ UP & CASIA-FaceV5 & Gaussian noise (Mean = 0, variance = 1) & FaceNet & 75.71 \\ Down & CASIA-FaceV5 & Gaussian noise (Mean = 0, variance = 1) & MFCosface & **93.62** \\ Down & CASIA-FaceV5 & Gaussian noise (Mean = 0, variance = 1) & FaceNet & 75.58 \\ All & CASIA-FaceV5 & Gaussian noise (Mean = 0, variance = 1) & MFCosface & **96.47** \\ UP & MFR2 & random noise (num = 1000) & FaceNet & 74.21 \\ Down & MFR2 & random noise (num = 1000) & FaceNet & **92.10** \\ All & MFR2 & random noise (num = 1000) & FaceNet & 76.75 \\  & & & & \\ \end{tabular}
\end{table}
Table 4: Recognition accuracy rate in the noise experiment.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **LFW\_m** & **CF\_m** & **MFR2** \\ \hline CBAM\_Reduction & 99.08 & 96.78 & 98.00 \\ CBAM\_All & 99.18 & **97.17** & 98.00 \\ Att-Inception & **99.33** & 97.03 & **98.50** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of the attention mechanism experiment.  
According to the experimental results, the Att-Inception module constructed in this paper is significantly better than the other two modules. On the CF_m dataset, our recognition accuracy was not the highest, but it was only 0.14% worse than the highest--a relatively high performance. Experiments on several datasets proved that our method performs better in most cases and achieves higher recognition accuracy.
## 4 Methodology
### The Proposed CGFace Model
#### 4.1.2 Batch Normalization  
In the proposed model, before the fully connected layers, one batch normalization layer was added. The purpose of this layer is to improve optimization by introducing some noise into the network so that it can regularize the model besides the dropout layers a little bit more. It normalizes the input \(x\) from the preceding layer by deducting the batch mean and dividing it to batch standard deviation. The result is squashed through a linear function with parameters called \"standard deviation\" (\(\gamma\)) and \"mean\" (\(\beta\)). If \(\gamma=(var(x))\) and \(\beta=mean(x)\), the original activation is restored. The equation is what makes batch  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Layer** & **Configuration** & **Output (Rows, Cols, Channels)** \\ \hline Input & 64 \(\times\) 64 & \\ Convolution\_1 & 5 \(\times\) 5 with 8 kernels & (60, 60, 8) \\ Convolution\_2 & 5 \(\times\) 5 with 8 kernels & (56, 56, 8) \\ Maxpool\_1 & 2 \(\times\) 2 & (28, 28, 8) \\ Dropout\_1 & Probability at 0.2 & (28, 28, 8) \\ Convolution\_3 & 3 \(\times\) 3 with 16 kernels & (26, 26, 16) \\ Maxpool\_2 & 2 \(\times\) 2 & (13, 13, 16) \\ Dropout\_2 & Probability at 0.2 & (13, 13, 16) \\ Convolution\_4 & 3 \(\times\) 3 with 16 kernels & (11, 11, 32) \\ Maxpool\_3 & 2 \(\times\) 2 & (5, 5, 32) \\ Dropout\_3 & Probability at 0.2 & (5, 5, 32) \\ Convolution\_5 & 3 \(\times\) 3 with 16 kernels & (3, 3, 64) \\ Dropout\_4 & Probability at 0.2 & (3, 3, 64) \\ Flatten & Length: 576 & (576) \\ Dense & Length: 256 & (256) \\ Dense & Length: 2 & (2) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Layer breakdown for the proposed CGFace model.  
normalization robust. Initially, batch normalization is set to convert the input to zero means. However, during the training process, it can learn from any other distribution. Batch normalization enables each layer to learn by itself. The detailed description of the algorithm is shown in [26].
# CoFi: Coding-Assisted File Distribution over a Wireless LAN
## 5 Performance and Evaluation
### CoFi Performance in an Elementary Topology
This is because a smaller batch size also entailed more interaction overhead, such as the ACK feedback and the switch between the AP and relay. In the extreme case when batch size equals 1, CoFi was essentially degraded into a persistent retransmission protocol, and the benefit of MAC caching also diminished. Another problem of interest is: To what extent does the MAC caching scheme help? Figures 12 and 13 compare the two versions of CoFi with and without MAC caching, in terms of throughput and per-packet delay. Since the MAC caching scheme ensures that the throughput of high-quality links are not affected when they offer help to other links, client A achieves the same level of throughput and delay, no matter if MAC caching is used. However, the throughput of client B is nearly doubled with MAC caching. The reason is that MAC caching replaces the low quality link (AP\(\rightarrow\)B) with the high-quality link (A\(\rightarrow\)B) when relay A can offer help. The packets received by client A are recoded and sent to Client B to improve the transmission efficiency. From Figure 13, we also observe that MAC caching is especially helpful during the period when the (AP\(\rightarrow\)B) link experiences harsh channel condition, where the bursty losses can be smoothed out. Therefore, caching is particularly useful for jitter-sensitive applications, such as video streaming over the wireless LAN. Figure 10: The per-batch delay (averaged over 10-s periods) of each client running 802.11 protocol and CoFi protocol in the elementary topology. Figure 11: The per-packet delay (averaged over 10-s periods) of each client running 802.11 protocol and CoFi protocol in the elementary topology. Figure 12: The throughput of each client running CoFi with/without MAC caching in the elementary topology. Figure 13: The per-packet delay of each client running CoFi with/without MAC caching in the elementary topology.
## 6 Methodology
### Impact of Packet Injection Rate  
PIR is a parameter that has a huge impact on network performance. It defines the threshold between traffic saturation and smooth packet delivery. The linear design of Scenario 2 will produce a traffic accumulation gradient, forcing the nodes closer to the sink node (Node 0) to make use of the channel more frequently. This effect can be observed in Figure 11, showing that the farther the node, the higher the latency. The traffic relayed to the following node must be allocated joint to the self-generated traffic. In addition, collisions due to the simultaneous reception of ACK and ARQ messages from neighbouring nodes may induce packet errors. This effect is shown in Figure 12.  
Figure 11: Impact of PIR on latency per node.  
Figure 10: Packet Loss Rate (PLR) vs distance and number of retransmissions. Each distance corresponds to a node of Scenario 1.  
As observed, PLR is also greatly affected by PIR. The same saturation events that caused the latency increase, affected PLR as well. The increment of packet handling while nodes are closer to Node 0 increases transmission times, and this produces a higher number of collisions. STNAAG 5066 ARQ protocol implements selective retransmissions, but after a certain amount of time ARQ link is declared as broken, and all the messages buffered by STNAAG 5066 stack are discarded. STNAAG 5066 informs the upper layer about packet discard, but does not perform any action in this regard. The effect of PIR is more noticeable on those nodes which are closer to Node 0 since they accumulate more traffic, as expected.
## 3 Discussion  
### Dynamic Changes in SIF and GPP  
Previous studies have revealed the dynamic characteristics of SIF and GPP of canopy at different temporal scales, which showed that SIF and GPP of a coniferous forest ecosystem had obvious diurnal and seasonal changes, and both were the highest in summer and the lowest in winter [11]. In this study, we also found that the SIF and GPP a subtropical coniferous plantation appeared the similar diurnal and seasonal variation, and both reached their highest in summer.  
The strongest solar radiation in a day occurs at around 12:00, but the atmosphere is not directly heated from the solar radiation, because the solar radiation warms the ground first, absorbs the heat, reflects back to the atmosphere, and then warms up; therefore, the strongest solar radiation appeared at noon, and the highest atmospheric temperature occurs in the afternoon. In this study, we found that the times when PAR, SIF and GPP reach their maximum values are similar, while Ta lags behind these changes. This shows that SIF and GPP mainly respond to changes in PAR, which is the same conclusion as those drawn in previous studies [18].