esa-sceva/satcom-chunk-collection huggingface dataset:
# Low-Cost Software Evaluation of Communication Delays in Networked Industrial Systems
## 8 Conclusions  
Our approach has shown that sub-microsecond delay measurements can be reasonably achieved with the aid of conventional hardware and open-source software. This can be significantly useful in a number of networked industrial applications, where the adoption of a new device cannot abstract from assessing the impact on the whole system performance before its actual deployment. This is particularly true for small and medium enterprises that cannot afford the acquisition of complex and expensive laboratory testing equipment.  
The solution presented in this paper requires moderate expertise and has modest hardware and software requirements that make it easily deployable in a variety of real-world network scenarios. The system includes a built-in traffic generator and also supports external alternatives. This, together with its ability to be configured and calibrated remotely, without any manual intervention, enhances its flexibility and makes fully automatic measurement cycles possible.  
Experimental results show that the built-in traffic generator compares favorably with respect to related work in many cases, although external generators can still be used when desired, for instance, to explore specific traffic patterns the built-in generator is currently unable to support.  
The baseline system configuration was fully characterized regarding packet transit delay and delay variability.  
Finally, the evaluation of an exemplar DUT, that is particularly demanding because of its intrinsically small delay and delay variance, was performed successfully, reaching remarkably tight confidence interval widths for the mean DUT delay and delay variance. Areas of future work include the further improvement of the built-in traffic generator and the characterization of a larger set of DUTs.  
**Author Contributions:** Conceptualization, I.C.B., A.V. and L.D.; Methodology, I.C.B., A.V. and L.D.; Software, M.C. and I.C.B.; Data curation, M.C.; Writing--original draft, M.C., I.C.B., A.V. and L.D.; Writing--review and editing, M.C., I.C.B., A.V. and L.D. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was partially supported by the project SERICS (PE00000014) under the MUR National Recovery and Resilience Plan funded by the European Union--NextGenerationEU.  
**Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Recent Advances in Nanoparticle-Mediated Treatment of Inflammatory Bowel Diseases
## 10 Lectin-Conjugated Nanoparticles  
Lectin-decorated nanoparticles were proposed for colon-targeted drug delivery due to the enhanced binding specificity, further exploring their therapeutic efficacy [28]. The authors prepared peanut (PNA)-functionalized NPs and wheat germ (WGA)-functionalized NPs as they are quite resistant to strong acidic conditions and present great advantages in terms of enzymatic degradation and low cytotoxicity. As WGA is mostly characterized by non-specific binding, PNA offers selective drug targeting to the site of inflammation.  
The authors presented active targeting strategies and novel carriers with high adhesion and anti-inflammatory effects. The therapeutic efficacy of lectin-decorated drug loaded nanoparticles is supported by their selective adhesion to the inflamed tissue. As lectin is a stable targeting moiety in the GIT, it indicates itself as resistant to digestive enzymes, as reported by previous studies.  
The authors showed that the use of the nano-compound improved nanoparticle adhesion to the intestinal mucosa in the murine colitis model. The degree of bio-adhesion was attributed to the specific sugar-lectin molecular adhesion mechanism. As lectin increased the accumulation of nanoparticles to the site of inflammation, it also enhanced the therapeutic efficiency of the nanoparticles. Results show that active targeting using lectin could be beneficial for the delivery of various drugs in the treatment of IBD (including drugs that are normally avoided due to their potential serious side-effects), with strong anti-inflammatory effects [28].
# The Carbon Footprint of Milk Production on a Farm
## 3 Results and Discussion
### The CF Range in Milk Production
The averaged emissions for the entire country were 1.02 kg CO\({}_{2}\) eq kg\({}^{-1}\) milk, with the values in individual provinces varying by more than 20%. These differences were influenced by factors such as the length of the grazing season, feed structure/composition, local cows' milk yield, and management of waste/milk production by-products. In contrast, [PERSON] et al. (2017) demonstrated that in the Netherlands, in the years 1990-2019, the CF for milk dropped by 35%, from 1.52 to 0.99 kg CO\({}_{2}\) eq kg\({}^{-1}\) FPCM. This decrease was achieved through increased milk yield, better feed efficiency, and reduced nitrogen fertilisation. In contrast, in the Azores (2019), grazing systems were characterised by a low CF value (0.83 kg CO\({}_{2}\) eq kg\({}^{-1}\)). The map shows the average CF values for milk production on farms in individual countries around the world (Figure 9), with the highest value noted in Ethiopia and the lowest in New Zealand and the Azores. The CF in other countries ranged from 1 to 3 kg CO\({}_{2}\) eq kg\({}^{-1}\) milk. Analysis of the CF values by continent (Figure 10) showed that the highest average values were noted in Africa (2.81 kg CO\({}_{2}\) eq kg\({}^{-1}\)), followed by Asia (2.29 kg CO\({}_{2}\) eq kg\({}^{-1}\)) and South America (2.05 kg CO\({}_{2}\) eq kg\({}^{-1}\)), whereas the lowest values were noted in Europe (1.33 kg CO\({}_{2}\) eq kg\({}^{-1}\)), North America (1.27 kg CO\({}_{2}\) eq kg\({}^{-1}\)) and Oceania (0.98 kg CO\({}_{2}\) eq kg\({}^{-1}\)). Single results were obtained for Latin America (2.56 kg CO\({}_{2}\) eq kg\({}^{-1}\)) and Australia (1.1 kg CO\({}_{2}\) eq kg\({}^{-1}\)). The highest variability was observed in North America (37%), Oceania (29%) and Europe (24%), and the lowest was in Asia (10%). Figure 9: Average carbon footprint values for the production of 1 kg of milk (kg CO\({}_{2}\) eq kg\({}^{-1}\)) on farms in various countries around the world.
# SROR: A Secure and Reliable Opportunistic Routing for VANETs
## 3 System Model and Routing-Related Metrics
### Network Model
\(T_{i,j}^{\text{tr}}\) and \(T_{i,j}^{\text{pr}}\) are used to calculate the end-to-end delay in Section 6. To this end, the end-to-end delay that a packet is transmitted through link \(l_{i,j}\) is denoted as \(T_{i,j}^{\text{to}}\), satisfying \(T_{i,j}^{\text{to}}=T_{i,j}^{\text{tr}}+T_{i,j}^{\text{pr}}\). For the sender \(\vartheta_{i}\), it expects its packet to be forwarded quickly and reliably toward the next-hop node. Here, quickness means low delay and reliability means the success of forwarding the packet; that is, whether the receiver \(\vartheta_{j}\) successfully received the packet within the time limit. Additionally, the free-space path-loss model is considered as the propagation model in our work. In this model, the reception power (in dBm ) is expressed as:  
\[P_{r}=P_{t}+10 log\left(\frac{G_{t}G_{r}\lambda^{2}}{16\pi^{2}d^{3}}\right), \tag{2}\]  
where \(P_{t}\) is the transmission power in dBm, \(G_{t}\) and \(G_{r}\) are the antenna gains in the transmitter and receiver, respectively, \(\lambda\) is the wavelength, \(d\) is the distance between the transmitter and receiver, and \(\eta\) is an exponent.
## 4 Numerical Study Considering the Presence of the Soft and Hard Interbedded Bedrock Layer
### Effect of the Dip Angle of Soft and Hard Interbedded Bedrock Layer on Stabilizing Piles  
To rigorously study the influence of the dip angle of soft and hard interbedded bedrock layer on the displacement and internal force of the embedded section of the stabilizing pile, 16 numerical simulations are performed with the layered bedrock dip angle increasing by 5\({}^{\circ}\) from 0\({}^{\circ}\) to 75\({}^{\circ}\). Since the trends of the curves were consistent, to clearly display the trends, 6 typical curves were selected to plot. These simulation results are shown in Figures 20-22.  
To concretely study the influence of the dip angle of soft and hard interbedded bedrock layer on the displacement and internal force of the embedded section of the stabilizing pile, the bedrock layer inclination is 90\({}^{\circ}\), and 16 sets of numerical experiments are performed when the layered bedrock dip angle increases by 5\({}^{\circ}\) from 0\({}^{\circ}\) to 75\({}^{\circ}\). Considering that the trend of the curves is consistent, to display the trend of the curves more intuitively, 6 typical groups are selected for analysis. Numerical simulation results are shown in Figures 20-22.  
Figure 19: Shear force distributions along the embedded section of the stabilizing pile.  
Figure 18: Bending moment distributions along the embedded section of the stabilizing pile.  
From Figure 20, as the dip angle of the soft and hard interbedded bedrock layer increases, the displacement of the embedded section of the stabilizing pile shows an increasing trend in the range of 0\({}^{\circ}\) to 45\({}^{\circ}\) and reaches the maximum displacement at 45\({}^{\circ}\). In the range of 45\({}^{\circ}\) to 75\({}^{\circ}\), the influence of the dip angle of the layered bedrock on the displacement of the embedded section of the slide pile is  
Figure 21: Bending moment of the embedded section of the stabilizing pile.  
Figure 22: Shear distribution of the embedded section of the stabilizing pile.  
gradually reduced. The distribution of the bending moments and the shear forces of the stabilizing piles with different dip angles are shown in Figures 21 and 22.  
From Figure 21, as the dip angle of the soft and hard interbedded bedrock layer increases, the maximum value of the bending moment gradually decreases, and the position of the maximum moment point on the bending moment curve gradually moves downward. Above 4 m, the bending moment gradually decreases as the dip angle increases; however, under 4 m, the bending moment value gradually increases as the dip angle increases. From the shear force diagram, it is observed that as the dip angle increases, the point where the shear force is zero gradually moves downward, while the maximum shear force initially decreases and then increases. When the dip angle is less than or equal to 45\({}^{\circ}\), the position of the maximum shear point remains unchanged, while the position of the maximum shear point moves downward when the dip angle is greater than 45\({}^{\circ}\).
## 5 Field Monitoring and Analysis
### Analysis of the Upper Support Pressure in Front of the Work during the Recovery Period  
During the mining process of the working face, the ZYC-10 flexible hydraulic coal body stress sensor (calibrated) and an infrared data collector were used to monitor the distribution of the advance support pressure. The sensors were arranged in the solid coal side of the 130,205 ventilation return lane. The flexible detection units were installed at distances of 310 m, 312 m, and 316 m from the working surface open cut eye to monitor the change of the advanced support pressure. The drill hole for the stress measurement  
Figure 10: Deformation map of the surrounding rock of the 130,205 ventilation return lane 150 m from the open cut eye.  
Combined with the observation and analysis of the surrounding rock deformation of the advanced roadway and the force of the anchor rod, the observation of the ore pressure of the 130,205 ventilation return lane is consistent with the deformation observation. The analysis shows that starting from 60 m to 80 m in front of the work, the support pressure begins to gradually increase, the stress reaches its peak at a position 20 m from the working surface, the plastic zone appears in the range of 20 to 30 m, and the fractures in the coal body develop obviously, are more broken, and have no impact failure effect. Until the goat position stress gradually decreases, the front of the peak is in a state of elastic compression, the back of the peak is in a state of plastic deformation, and the stress concentration coefficient is approximately 2.5 to 3. The deformation of the roadway in the plastic area is large, and the interior of the coal rock mass is broken. The slope is in a stress-reduced area with no impact damage, and the rock cover movement of the roadway will cause ore pressure to occur, but it will be within the controllable range.
# AI-Driven Safety and Security for UAVs: From Machine Learning to Large Language Models
## 4 AI for UAV Security
### UAV Identity Authentication Security  
UAV authentication technology is an important means of ensuring the legitimate use of UAVs, preventing illegal intrusion, and safeguarding UAV security. A simplified illustration of the authentication procedure is presented in Figure 15. In addition to traditional methods, such as authentication and key agreement (AKA) used in mobile communication networks, the introduction of UAV service provider-based authentication and trust transfer-based authentication can be considered.  
In 2019, [PERSON] et al. proposed an authentication scheme based on a PUF and fuzzy extractor between UAVs and ground stations [172]. This method considers the  
Figure 14: Jamming attack.  
influence of hardware noise and environmental changes on authentication and enhances the flexibility and security of authentication at the same time. [PERSON] et al. explored the application of ML algorithms in UAV communication identity authentication [174]. In 2020, [PERSON] et al. proposed a method for UAV detection and identification that is based on DL technology to analyze and classify the RF signal characteristics of UAVs for identity authentication [175]. In 2021, [PERSON] et al. proposed a CNN-based method to achieve real-time detection and authentication of UAVs, improving the efficiency of identity authentication and reducing the false alarm rate [176]. [PERSON] et al. analyzed the communication signal features of UAVs by applying a DL model to verify the identity of UAV pilots, thus helping prevent unauthorized user manipulation [177].  
However, UAV authentication involves handling a significant volume of sensitive data, such as biometrics and communication records, which could be vulnerable to leakage or misuse. In addition, traditional AI methods have limited accuracy in processing multimodal data such as images, sounds, and communication signals that need to be fused, affecting UAV authentication accuracy. Moreover, UAV communication environments are complex and changing, and it may be difficult for traditional AI models to adapt quickly to new authentication scenarios or attack patterns.  
In contrast, LLMs combined with privacy-preserving methods, such as federated learning [178], and multimodal learning methods, such as CLIP and Florence [179], can enable efficient fusion and feature extraction of multimodal data, improving UAV authentication accuracy. By transferring knowledge pretrained with large amounts of general datasets to UAV authentication scenarios, LLMs reduce their reliance on domain-specific data. The models' online learning capabilities also allow dynamic adjustments to authentication strategies, adapting seamlessly to environmental changes.
# Sample-Efficient Deep Learning Techniques for Burn Severity Assessment with Limited Data Conditions
## 5 Conclusions  
In this study, we developed machine learning models for burn severity assessment. Considering common situations where well-annotated medical images are often not sufficient for model training, we employed and evaluated several sample-efficient deep learning techniques, including TL, SSL, FL, and GAN-based data augmentation for burn severity assessment models. Through extensive experiments with burn images under different dataset conditions, we showed the benefits and limitations of these sample-efficient deep learning techniques to establish the design principles related to specific data conditions in which some techniques can be more effective than others. Specifically, our SSL-based models, which were pretrained on unlabeled images with self-supervised learning schemes and learned on a small labeled dataset task-specifically, achieved comparable performance in burn severity assessment accuracy to a baseline model learned on a six-times larger dataset. This achieved sample-efficiency in model training is an important factor in handling medical image analysis tasks successfully, where only a limited quantity of labeled data is available. To the best of our knowledge, our work is the first to evaluate various deep learning techniques using real-world burn injury images from the perspective of sample-efficiency in model training.  
We intend to develop a generalized framework containing sample-efficient deep learning techniques and reference model structures, which can be used to automate the building of deep learning models in the field of medicine. We are also implementing deep learning models for some medical image analysis tasks other than burn severity assessment, in the same vein as this study, focusing on sample-efficiency in model training.  
Conceptualization, H.S. ([PERSON]); Data curation, H.S. ([PERSON]), H.S. ([PERSON]) and E.K.; Investigation, H.S. ([PERSON]), H.S. ([PERSON]), W.C., J.P., M.P. and H.W.; Methodology, E.K. and H.W.; Project administration, E.K. and H.W.; Software, W.C., J.P. and M.P.; Writing--original draft, H.S. ([PERSON]) and E.K.; Writing--review & editing, H.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the Institute for Information & communications Technology Planning & Evaluation (IITP) under grant No. 2021-0-00875, and in part by the ICT Creative Consilience program supervised by the IITP under grant No. IITP-2020-0-01821.  
Not applicable.  
Figure 11: Performance comparison of TL, SSL, and GAN-based data augmentation models: the model accuracy on dataset170 by the baseline and the TL models in Figure 6, the SSL-burn models in Figure 7, and the GAN-based data augmentation models in Figure 10 is compared. SSL-burn-all and SSL-burn-last denote FT-all and FT-last of SSL-burn, respectively. In addition, SSL-burn-all+CycleGAN denotes a combined method of SSL-burn and CycleGAN-based data augmentation.
## 4 Results
### Phantom Experiments  
The experimental B-mode images in Figure 3a-d with \(p\) = 2.0 show that, compared to the 2D-DAS counterpart, all three DMAS beamforming can make the wire reflectors stand out from the speckle background. This is expected since the echoes from the wire reflectors have much higher signal coherences than those from speckle scatterers. Consequently, the image magnitude of the speckle background appears to be relatively suppressed in any DMAS beamforming. Note that, among the three DMAS beamforming methods, the suppression of speckle background is the most obvious for 2D-DMAS and is the least for Tx-DMAS. Moreover, both 2D-DMAS and Rx-DMAS exhibit noticeable dark-region artifacts around the wire reflectors and the hyperechoic cyst while the dark-region artifacts are relatively alleviated for Tx-DMAS. Figure 3e-h further shows that the edge of cysts gets clearer in all three DMAS beamforming methods due to the lower clutter artifacts within the anechoic regions. The 2D-DMAS exhibits the lowest clutter level compared to the Rx-DMAS and Tx-DMAS beamforming but at the cost of reduced magnitude and elevated variation of speckle background. These observations are consistent with those in the simulation results.  
Figure 2: Quantitative analysis of image resolution, ghost-artifact level, and image contrast for the simulated B-mode images in Figure 1.  
Quantitative analyses of experimental B-mode images for the DMAS beamforming method with different \(p\) values are shown in Figure 4. The experimental results are generally similar to those in the simulations. Figure 4a,b shows that the image resolution is the highest for 2D-DMAS when the same \(p\) value is considered. For example, the LW decreases from 0.53 mm for the 2D-DAS reference to 0.36 mm for 2D-DMAS with \(p=2.0\). This corresponds to an improvement of 32% in lateral resolution.  
Figure 3: Experimental B-mode images of wire reflectors and various contrast cysts in speckle background for 2D-DAS, Rx-DMAS, Tx-DMAS, and 2D-DMAS beamforming: The \(p\) value in DMAS beamforming is fixed to 2.0.  
In Figure 4c, it is also demonstrated that the image CR of the anechoic cyst significantly improves for 2D-DMAS and Rx-DMAS respectively by 15.6 dB and 13.7 dB relative to the 2D-DAS reference. Nonetheless, the results in Figure 4d indicate that the conventional 2D-DAS still provides a higher CNR than all three DMAS beamforming methods. For \(p=2.0\), the CNRs are respectively 0.82, 0.86, and 0.86 for 2D-DMAS, Rx-DMAS, and Tx-DMAS while that for 2D-DAS is 0.99. On the other hand, all DMAS beamforming methods result in higher GCNR than 2D-DAS especially for 2D-DMAS and Rx-DMAS.
## 3 Results  
### Main Effects of Parameters  
The main effects of the parameters for Response 1 (average stress in cortical peri-implant bone) are summarized in Figure 3. The null effect corresponds to the overall average of this response of the factorial design. Cortical bone thickness is the most influential parameter in the system, and a thickness of 1 mm significantly increases the average stress in cortical bone. Implant diameter is also an influential parameter, even if its range of variation is small (only 1 mm). A large diameter significantly reduces the average stress in the bone. Taper, Young's modulus of the cancellous bone and length have significant effects on cortical bone, even if they are less than the previous ones. Finally, the Young's modulus of the implant and its angle of inclination show limited influence on cortical bone compared to other parameters.  
The results are different when Response 2 (stress jump between bone and implant) is considered; see Figure 4. Young's modulus of the implant has the greatest impact on this response, and a lower Young's modulus significantly reduces the stress jump between the implant and the cortical bone. This parameter became crucial for stress jump when it had little influence on average stress in the bone. The parameters that characterize the implant geometry (diameter, length and taper) are also influential, especially diameter. Cortical bone thickness has less effect on this response compared to other parameters but is still nonnegligible. Finally, Young's modulus of the cancellous bone and the angle of inclination of the implant have almost no influence on Response 2.  
Figure 2: (**a**) Example of Von Mises stress distribution in the vestibulo-lingual plane and (**b**) representation of the cortical bone peri-implant area (unrepresented mesh).
# A Linear-Arc Composite Beam Piezoelectric Energy Harvester Modeling and Finite Element Analysis
## 3 Finite-Element Simulation
### Finite Element Analysis  
#### 3.4.1 Resonant Frequency  
In order to obtain the relationship between curvature and resonant frequency, the PEH-C with curvatures of 0 m\({}^{-1}\), 20 m\({}^{-1}\), 40 m\({}^{-1}\), 60 m\({}^{-1}\), 80 m\({}^{-1}\), and 100 m\({}^{-1}\) were selected for research. The results are shown in Figure 7.  
It is evident from Figure 7 that the resonant frequency of the PEH-C gradually increased with the increase in curvature. It can be seen that under the condition of keeping the length unchanged, the curvature of the curved beam part increased, the bending radius decreased, and the corresponding stiffness increased, resulting in an increase in the resonance frequency. Conversely, when the curvature was smaller, the curved beam was closer to the straight beam, the stiffness decreased, and the resonance frequency decreased accordingly.