esa-sceva/satcom-chunk-collection huggingface dataset:
## Results and Discussion
### Comparison of Aroma-Active Compounds in Wild Lowbush and Cultivated Highbush Blueberries
There was a significant interaction (\(p<0.001\)) in the frequency of descriptors chosen between blueberry species and the descriptor. Of the 16 descriptors provided to the panelists, \"floral\" was chosen most frequently followed by \"fruity\" and \"sweet\" in both wild lowbush and cultivated highbush fruit. The fourth and fifth most frequent descriptors for wild lowbush fruit were \"blueberry\" and \"green-grassy\", whereas for cultivated fruit, \"green-grassy\", \"herb-like\", \"rancd-cheesy\" and \"earthy-musty\" were chosen more frequently than \"blueberry\". The descriptors \"floral\", \"fruity\", \"sweet\" and \"blueberry\" were chosen significantly more times to describe aroma-active compounds in wild lowbush than in cultivated highbush blueberry fruit. These results suggest that wild lowbush blueberries were perceived to have a fruitier and more \"blueberry-like\" aroma than cultivated highbush fruit in this study. In addition to differences in volatile profiles that contribute to flavor differences between lowbush and highbush fruit, differences in sugar and acid composition may also impact flavor differences. Wild lowbush blueberry fruit have higher sugar content than cultivated highbush fruit, and the predominant acid is quinic acid compared to citric acid in highbush fruit [11]. Quinic acid has a less tart taste compared to citric acid [39], which would contribute to a sweeter less tart flavor of wild lowbush fruit compared to cultivated highbush fruit. The fruit of 'Duke', which has 4% _V. angustiifolium_ in its parentage, was reported to have higher quinic acid content than the other four cultivars in this study [11]. However, the volatile composition of 'Duke' did not show additional similarities to wild lowbush fruit. Figure 3: Frequency of descriptors chosen by sensory panelists to describe aroma-active compounds from wild lowbush and cultivated highbush blueberry fruit samples analyzed by gas chromatography-olfactometry (GC-O). Values represent the mean frequencies of descriptors chosen by five evaluators. Significant differences determined by LSD\({}_{0.05}\) are indicated by \"\"are\" next to the descriptor. GC-O analysis was conducted on fruit collected from three wild lowbush blueberry fields located in four provinces (\(n=12\)), and from five cultivars of cultivated highbush blueberries from three fields (\(n=15\)).
## 5 Validation of Results
### Experiments Settings
Other considerations to take into account would be: (i) type of input and output, for both platforms the RESTful protocol is used to communicate and HTTP methods (GET, POST, DELETE and PATCH). However, the data sent or received are delivered in JSON (Javascript Object Notation) format, since this format is easier to be interpreted by developers or electronic platforms. However, these platforms support other formats like HTML, YAML, XML, XLSX or TXT; (ii) general accessibility, the application was deployed in a cloud environment that can be accessed through a browser (i.e., Google Chrome, Fire-box, Opera or others); (iii) number of tests performed, four joint tests were created divided into two concepts (Response time and Network consumption, and Efficient use of infrastructure) with an average of 200,000 samples per test for 20 min, grouped in two groups (SPIDEP-SOA-K8s-T1/T2 and SPIDEP-MSA-K8s-T1/T2). It should be noted that both environments follow the same steps to summation their services. The first step is to authenticate all active sessions using the JWT token that will be verified by the API gateway (all user interface \"UI\" use is excluded). The second step is to generate approximately 150,000 initial data points in each database, using as seed the existing medical data (8500 records). In this sense, all the data generated is only intended  
Figure 3: Process Flowchart for the simulation performed on the K8s SPIDEP environments. to generate a workload that will drastically increase (ten of ten users every five minutes) to emulate a specific number of concurrent user sessions (50 virtual users); it will not be used to train, validate or test any expert system. The third step is to randomly execute an HTTP command (e.g., GET, POST, PATCH and DELETE) in the specified service. We have chosen the two most demanding services (approximately 75,000 consultations per session) for both platforms (medical data management (service E) and intervention management (service G)). Services H and I are excluded because of their incompatibility in SPIDEP-SOA; they are in the experimental phase of being integrated with SPIDEP-MSA. The fourth step is to monitor and record all the queries made in each test.
# Energy-Aware Control of Error Correction Rate for Solar-Powered Wireless Sensor Networks
## 3 Energy-Aware Reed-Solomon Scheme
### Energy Prediction  
In this section, we explain the energy models of \(\mathbb{E}_{\text{harvest}}^{i}\) and \(E_{\text{consume}}^{i}\), which represent the predicted amount of harvested and consumed energy, respectively, for the node \(i\) during a given period.  
Assume that the current period is the \(n\)-th period.
# GAN Training Acceleration Using Frechet Descriptor-Based Coreset
## 2 Related Works
### Blob Identification  
While large objects can often be automatically or semi-automatically isolated, small objects (blobs) are difficult to identify (detect and segment). Blobs can range in size and location in images. Examples of blobs include cells or nuclei in images from optical microscopy [17], glomeruli in cationic ferritin-enhanced magnitude resonance images (CFE-MRI) of the kidney [18; 19]. Major challenges to identify small blobs include low image resolution and high image noise. The small blobs are often massive and can overlap together. To overcome the challenges of blob identification, a number of blob detectors have been developed for blob identification such as nuclei detection and cell detection, among which scale-space-based blob detectors have attracted great attention. For example, [PERSON] et al. [20] proposed the generalized Laplacian of Gaussian (gLoG), which accurately detected blobs of various scales, shapes and orientations from histologic and fluorescent microscopic images. [PERSON] et al. developed Hessian-based Laplacian of Gaussian (HLoG) [21] and Hessian-based Difference of Gaussian (HDoG) [22] to automatically detect glomeruli in CFE-MRI. However, these blob detectors are not robust to noise [23], leading to high false positive rates. Recently, deep learning-based blob detector, U-Net joint with Hessian-based Difference of Gaussian (UH-DoG) [24] has been proposed to take advantage of the complementary properties of U-Net [25] to alleviate the over-detection issue of HdoG. However, UH-DoG may overlook large variations in blob size. As an extension of UH-DoG, another small blob detector using Bi-Threshold Constrained Adaptive Scales, BTCAS [26], is able to address this issue by searching local optimal DoG scale, which is adapted to the range of blob sizes to better separate touching blobs, and thus, the under-segmentation issue of U-Net is addressed. While UH-DoG and BTCAS detect blobs to some extent, there are limitations such as domain difference between public datasets and target dataset, geometric difference between small blobs, etc. To address these limitations, a denoising convexity-consistent Generative Adversarial Network (BloGAN) [8] has been proposed for improved small blob identification. Experiments have shown that this blob detector could achieve high denoising performance and selectively denoise the image without affecting the properties of blobs. However, the training of the BlobGAN is slow and computationally expensive. In this research, we propose a Coreset-based approach to accelerate its training.
## 3 Method
### Reinforcement Learning to Optimize Fusion Parameters  
#### 3.3.1 State Representation  
Reinforcement learning-based strategies can effectively optimize parameter configurations during model fusion. This feedback mechanism allows RL-Fusion to adjust the strategy in real time based on environmental feedback, thus continuously improving the performance and efficiency of the fused model in a given task. The state \(s_{t}\) consists of model parameters \(P_{t}\), performance metrics \(M_{t}\), historical feedback \(H_{t}\), and fusion history \(F_{t}\).  
\(P_{t}\) denotes the model parameters at time t, including the weights of the base model and the fusion method, it can be described as follows:  
\[P_{t}=[w_{t},f_{t}]. \tag{6}\]  
\(w_{t}\) is the weight value used for model fusion at the current moment. \(f_{t}\) indicates the fusion method at the current moment.  
\(M_{t}\) denotes the performance metric at time t, generated by the SOTA LLM evaluation module (calculated from Equation (5)) to provide real-time feedback;  
\(H_{t}\) records the parameters and their corresponding rewards for each past model configuration. It can be described as follows:\[H_{t}=\{P_{t},r_{t}\}. \tag{7}\]  
\(P_{t}\) denotes the model parameters at time t (calculated from Equation (6)), and \(r_{t}\) denotes the reward value obtained after the fusion of the current parameter (calculated from Equation (7)).  
\(F_{t}\) records the evolution of the model's parameter configurations, helping the intelligentsia to decide whether to continue to utilize previously successful configurations or to explore new ones. It can be described as follows:  
\[F_{t}=\{P_{1},P_{2},\ldots P_{t}\}. \tag{8}\]  
The state \(s_{t}\) can be expressed by the following Equation:  
\[s_{t}=[P_{t},\ M_{t},\ H_{t},\ F_{t}]. \tag{9}\]
## 2 Background
### Future Enabling Technologies  
Previous work [25, 26, 27, 28, 29] demonstrates a possible future for autonomy in planetary science. This is, of course, one of many prospective approaches to the operations of autonomous data collection and analysis missions; it is, however, illustrative of the importance of developing planetary science community embraced autonomous control technology.  
With Model-Based Transmission Reduction (MBTR) and its highest-level component, Model-Based Findings Transmission (MBFT), data is autonomously assessed onboard the spacecraft based on an a priori model. If the data is found to support the model a validation finding is sent; however, if a model is determined to be invalid, then it is corrected. A MBFT model can be a very high-level scientific thesis (e.g., conditions on Mars were previously suitable for known life), which can be deconstructed into a set of supporting statements that must be true or false in order for this high-level thesis to be true or false. The spacecraft automatically seeks out additional data to allow it to assess the various statements that it must ascertain the truth status of.  
This approach can be utilized to control the operations of a network of heterogeneous craft [29] like those proposed in [30, 31, 32, 33]. The combination of the finding driven goal generation and goal based autonomous control could prospectively lead to lower-cost, lower human operational involvement science missions. A technology of this sort is also required to enable further-from-Earth missions where decisions must be made without human involvement for most matters due to transmission delay constraints.
## 4 Digital Watermarking System for JPEGs
### Security  
Security is considered in both scenarios, especially given the application, where the integrity of digital evidence must be guaranteed. A PRNG is used to randomly select \(8\times 8\) blocks for watermark embedding. This prevents unauthorised users from easily detecting or removing the watermark. The security key for block selection uses a consistent PRNG seed across all images in an investigation or disk image batches to optimise computational efficiency. This ensures uniform randomisation of block locations without needing a new seed for each image. However, the watermark embedded in each image is unique to each recipient. This approach maintains security and minimises computational complexity.
# Insights into Native Fermentation Process of Apples (_Malus domestica_) in Low Sodium Conditions
## 3 Results  
### Physical and Chemical Analysis  
#### 3.1.1 Mass Evaluation  
Mass evaluation is important in finding the physical quality of the pickled products [23]. Generally, the mass variation during pickling is due to several factors like osmosis or fermentation, leading to mass decrease, or liquid absorption (inverse osmosis), leading to mass increase. The percentage variation of apple samples during fermentation is shown in Table 1.  
During the first 7 days, no mass variation could be observed, probably due to the short time interval; when fermentation started, the rate of the process was too low to be visible. Starting on day 14, a slight decrease in mass could be noticed for all the samples. This decrease continued until day 28 of the fermentation for NaCl and MgCl\({}_{2}\) samples and until day 21 for the KCl samples. This behavior may be caused by both the osmosis phenomenon (the water from the apples migrates into the pickling solution that has a higher salt concentration) and the fermentation process (the saccharides are transformed into substances with lower molecular mass that migrate into brine or into gases that are eliminated from the system). Within the final interval, an increase in mass was noticed for the NaCl samples, while for the other samples, the decrease was lower than within the previous intervals. This could be explained by diffusion-driven uptake of brine components into the apple tissue. [PERSON] et al. [23] reported a mass decrease of sauerkraut by 4.6-7.9% in seven days of fermentation. In another study, a decrease in the mass of black garlic during 20 days of fermentation was reported [24].
## 4 Case Analysis  
### Initial Network Environment and Parameter Settings  
In this study, we use the Sioux Falls network, a small-scale urban network commonly used in traffic simulation studies, to evaluate the TRS. The data set, which includes 84,110 individual travel activities representing commuting trips during the morning peak period (07:00-09:00), was selected for its ability to simulate congestion, delays, and traveler behavior under real-world conditions [41]. The 07:00-09:00 time window was specifically chosen because it reflects the morning peak traffic period, when commuter traffic is at its highest and congestion tends to be most severe. This time period is crucial for assessing how TRS can optimize traffic flow and reduce delays during the busiest hours of the day.  
The Sioux Falls network offers a mix of high-traffic roads and public transportation routes (bus and metro), making it suitable for examining the impact of TRS on multimodal travel behavior. Although it is not a fully realistic representation of larger urban road systems, its manageable size, readily available data, and complexity make it an ideal case for model validation. This study investigates the effects of incentive and penalty mechanisms during peak hours on travel time, mode choice, and route decision-making, and further evaluates the improvements in traffic flow brought about by TRS.
# Postbuckling and Free Vibration of Multilayer Imperfect Nanobeams under a Pre-Stress Load
###### Abstract  
This paper investigates the postbuckling and free vibration response of geometrically imperfect multilayer nanobeams. The beam is assumed to be subjected to a pre-stress compressive load due to the manufacturing and its ends are kept at a fixed distance in space. The small-size effect is modeled according to the nonlocal elasticity differential model of Eringen within the nonlinear Bernoulli-Euler beam theory. The constitutive equations relating the stress resultants to the cross-section stiffness constants for a nonlocal multilayer beam are developed. The governing nonlinear equation of motion is derived and then manipulated to be given in terms of only the lateral displacement. The static problem is solved for the buckling load and the postbuckling deflection in terms of three parameters: Imperfection amplitude, size, and lamination. A closed-form solution for the buckling load in terms of all of the beam parameters is developed. With the presence of imperfection and size effects, it has been shown that the buckling load can be either less or greater than the Euler buckling load. Moreover, the free vibration in the pre and postbuckling domains are investigated for the first five modes. Numerical results show that the effects of imperfection, the nonlocal parameter, and layup on buckling loads and natural frequencies of the nanobeams are significant.  
Imperfection; nonlocal elasticity; buckling; postbuckling; vibration; NEMS +
Footnote †: journal: Applied Mechanics and Physics Letters
# HSM4 SSL: Leveraging HSMs for Enhanced Intra-Domain Security +
## 3 HSM4 SSL Architecture
### HSM4 SSL: Low-Level Architecture
#### 3.3.5 ADCP Assignment Policy Set  
The effective operation of the HAL depends on a well-structured JSON-based assignment policy set. The policy set design is a critical aspect of the HSM4 SSL architecture, as it determines the proper assignment of requests to the available HSMs based on predefined policies. The policy set is designed to consider various factors, including the capabilities, algorithms, hash functions, and utilization of each HSM, which ensures efficient resource utilization and optimal performance. For example, the policy set shown in Figure 8 includes the _\"hsm\"_ key, which identifies the specific HSM to which the policy applies. For each HSM, the _\"capabilities\"_ key specifies the capabilities of the HSM, indicating the operations or functions it can perform. For example, the policy set may specify that HSM1 is capable of public-key pair generation, certificate generation, and encryption, while HSM2 can only perform key pair generation and encryption. Plus, the _\"algorithms\"_ key lists the supported cryptographic algorithms for the specified HSM. It includes algorithms such as RSA, ECC, AES, and RC4, which can be used for various cryptographic operations. The _\"hush_funcs\"_ key defines the supported hash functions for the HSM. Hash functions like MD5, SHA1, SHA256, and SHA512 are commonly used for data integrity and verification purposes. Finally, the _\"utilization\"_ key reflects the current utilization level of the HSM. It can be categorized into different levels, such as low, medium, or high, based on factors like the workload, performance, and resource availability of the HSM, enabling efficient decision-making in request routing.  
By designing a comprehensive and well-defined assignment policy set, the HSM4 SSL architecture can effectively optimize resource allocation and ensure compatibility with cryptographic operations.
## 2 System Model
### Half-Duplex UAV Relay  
In this existing scheme, the half-duplex relay is mounted on the UAV to assist the signal transmission from the BS to the UE. In the first phase, the BS transmits the signals to the UAV relay, and then the UAV relay processes and forwards the signals to the UE in the second phase. For the signal processing, the signals are decoded by the DF relaying scheme, whereas the AF relaying scheme amplifies the signal before retransmitting it to the UE. The received signals at the half-duplex UAV relay and the UE are defined as [12]:  
\[y_{sr\_hd}=\sqrt{P_{bs}^{\text{{\it{bld}}}}h_{sr}}+n_{r}, \tag{25}\]  
\[y_{rd\_hd}=\sqrt{P_{r}^{\text{{\it{bld}}}}h_{rd}}+n, \tag{26}\]  
where \(P_{bs}^{\text{{\it{bld}}}}\) and \(P_{r}^{\text{{\it{bld}}}}\) are the transmit power at the BS and the UAV relay, respectively, while \(n_{r}\sim CN\left(0,\sigma_{r}^{2}\right)\) is the thermal noise introduced at the UAV relay. The end-to-end SNR for the half-duplex DF, \(\gamma_{\text{{\it{hd\_df}}}}\), and AF UAV relays, \(\gamma_{\text{{\it{bld\_df}}}}\), are defined as:  
\[\gamma_{\text{{\it{hd\_df}}}}=\text{min}\left\{\frac{P_{bs}^{\text{{\it{bld}}} }\mid h_{sr}\mid^{2}}{\sigma_{r}^{2}},\frac{P_{r}^{\text{{\it{bld}}}}\mid h_{ rd}\mid^{2}}{\sigma^{2}}\right\}, \tag{27}\]  
\[\gamma_{\text{{\it{hd\_af}}}}=\frac{P_{bs}^{\text{{\it{bld}}}}\mid h_{sr}\beta_ {h}h_{rd}\mid^{2}}{\mid\beta_{h}h_{rd}\mid^{2}\sigma_{r}^{2}+\sigma^{2}}, \tag{28}\]  
respectively. In addition, \(\beta_{h}\) represents the amplification factor of the half-duplex AF UAV relay, where  
\[\beta_{h}=\sqrt{\frac{P_{r}^{\text{{\it{bld}}}}}{P_{bs}^{\text{{\it{bld}}}} \mid h_{sr}\mid^{2}+\sigma^{2}}}. \tag{29}\]
## 6 Conclusions  
In this work, we have proposed a LoRaWAN compliant reliable and low-latency solution to fulfil the requirements of a FMAR scenario. To this aim, a low-cost and low-power wearable device was developed to detect the leakage of hazardous and flammable gases. The proposed approach allows to reliably transmit urgent data to the central server. This goal is achieved by leveraging the transmission of DL control messages aimed at avoiding collisions among concurrent transmissions. Finally, we validated the proposed approach with extensive experimental tests in an industrial-like scenario. Numerical results suggest that LoRaWAN can be exploited to obtain the required level of reliability in the considered scenario.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Test Set** & **SF** & **Number of Loss Packets** \\ \hline
1 & 7 & 10 \\
2 & 8 & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Analysis of residual loss using double GW for ED8.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Paper/Scenario** & **Proposed Scheme (LoRaWAN Compliance)** & **Reliability (PDR)** & **Latency Constraint** & **Number of Nodes Simulated** \\ \hline
[15]/General & Standard LoRaWAN MAC (\(\mathbf{\
u}\)) & 0.6 (CONF) 0.8 (UNCONF) & Not discussed & 100 \\ \hline
[7]/General & New MAC protocol RS-LoRa (\(\mathbf{\
u}\)) & 0.84 & Not discussed & 100 \\ \hline
[6]/Air pollution monitoring (mIoT) & TDMA-based FDMA-based (\(\mathbf{\
u}\)) & \(\approx\)1 & Not available & \(<\)2000 \\ \hline Our/FMAR & Coordinated transmission through DCP (\(\mathbf{\
u}\)) & \(>\)0.999 & \(<\)500 ms & 15/cluster can be accommodated \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance comparison with other works from the literature.  
**Author Contributions:** Conceptualization, A.A.; methodology, D.T., A.P., L.P., A.F. and A.A.; software, D.T. and L.P.; validation, D.T., A.P., L.P., A.F. and A.A.; formal analysis, A.A.; investigation, D.T.; resources, D.T.; data curation, A.A. and A.F.; writing--original draft preparation, D.T.; writing--review and editing, D.T., A.P., L.P. and A.A.; visualization, D.T. and A.A.; supervision, A.A.; project administration, A.F. and A.A.; funding acquisition, A.A. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work has been supported by INAIL, the Italian National Institute for Insurance against Accidents at Work, within the framework of the CP-SEC project: Cyber-Physical system (CPS) for the safety of factories at major accident risk.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.