esa-sceva/satcom-chunk-collection huggingface dataset:
# IPv6 Convergence for IoT Cyber-Physical Systems
## 1 Introduction  
The concept of the internet of things (IoT) has served as a precursor to what will likely lead to an evolved internet featuring decentralized adaptation, perceptive behaviors on a multiple-topology heterogeneous network infrastructure. Networking technology is evolving to a state that can enable anyone or anything to become a network operator and part of a global compute fabric. As a part of this evolution, networking is converging towards a common data and control plane approach.  
Beyond the conventional IoT use cases, emerging cyber-physical systems targeting Industry 4.0 and connected vehicular systems will also require an unprecedented level of networking in the future. We envisage that networking comprising wireless and wired technologies will converge towards a single unifying encapsulation protocol, namely IPv6. We depict this convergence in Figure 1. Therefore, devising new methods to support IP control and data traffic on constrained networks, for example, low-power wide-area networking (LPWA) technologies, represents a significantly impactful research challenge.  
Interconnecting heterogeneous networks and providing a service layer to support new applications and services is a critical ingredient in the evolving technology mix required to achieve these objectives. In Figure 2, we depict an example of multiple services operating in an overlay fashion on a heterogeneous network comprising a combination of WiFi, LPWA, cellular mobile and wireless mesh technologies. A converged IP layer on a heterogeneous network infrastructure is a necessary requirement to support the proliferation of future cyber-physical systems (CPS) targeted for new industrial/manufacturing and vehicular network use cases.
## 4 Performance Evaluation  
We evaluate the proposed detectors with simulated and measured data in this section. To measure the relative variation of the clutter covariance matrix \(\mathbf{R}\) across consecutive iterations, we define the relative variation function  
\[\Delta R=\frac{\big{\|}\big{\|}\mathbf{\hat{R}}^{(t)}\big{\|}_{\text{F}}- \big{\|}\mathbf{\hat{R}}^{(t-1)}\big{\|}_{\text{F}}\big{\|}}{\big{\|}\mathbf{ \hat{R}}^{(t-1)}\big{\|}_{\text{F}}} \tag{37}\]  
where \(\|\cdot\|_{\text{F}}\) represents the Frobenius norm of a matrix and \(\mathbf{\hat{R}}^{(t)}\) denotes the estimation of \(\mathbf{R}\) after \(t\) iterations.  
Figure 1 illustrates the relative change in the estimation of the covariance matrix \(\mathbf{R}\) over successive iterations under hypotheses H\({}_{0}\) and H\({}_{1}\). Obviously, as the number of iterations increases, the degree of relative change in \(\mathbf{R}\) gradually decreases. When the number of iterations exceeds 15, the degree of relative change gradually slows down. Considering the time complexity of the algorithm, the number of iterations for the power mismatches \(\gamma_{l}\) and the coordinate vector \(\xi\) is set to be 15.  
Figure 1: The relative variation in the estimation of the covariance matrix across successive iterations.
## 3 Results
Therefore, UAV operations should not be performed with a solar activity factor KPI (Planetary K-index) greater than 4 [48,49]. Another reason for the loss of control that may result in exiting the zone is the loss of the radio link between the UAV and the operator. This problem can be reduced by using more powerful transmitters and receivers and by appropriately designing the flight path in such a way as to avoid blocking the radio signal by obstacles. To further increase safety and reliability, it is reasonable to use several modules ensuring the positioning of the UAV in space (doubling or even tripling). Additional security in operations may be the use of observers who will supervise the position of the UAV in space from a perspective other than the operator. In this case, the operator and the observers must have continuous two-way communication. The Matrice 600 drone will be used in the considered scenario. It has a geofencing function and is additionally equipped with three GNSS modules and three IMUs (Internal Measurement Units). No additional observers are foreseen in the scenario due to short-range VLOS flights. The last step in the risk analysis using the SORA method is the \"Comprehensive Safety Portfolio\". It summarizes the entire risk analysis and provides evidence that there is an adequate level of assurance that the operation will be performed safely. In civil UAV applications, such a section should be presented to the relevant offices and institutions for approval to operate, if it goes beyond the standard scenario. At this stage, other aspects and factors that are not included in the SORA should be analyzed. These are, for example, security and environmental protection. In the case of security, the scenario includes seeing unknown UAVs. Therefore, the team of authors believes that additional procedures should be developed in the event of an unauthorized intrusion by an unidentified UAV so that each of the FR services knows how to proceed and what their responsibility is in such a situation. This scenario also assumes the use of UAVs to cover the area with a Wi-Fi network. A swarm of drones is to be used for this purpose. As the current version of SORA methodology is not adapted to the analysis of the risk of collisions between drones, the authors' team assumed that the first flight would be made with the use of the DJI Matrice 600, and then a swarm would be used. Risk analysis of a drone swarm operation is beyond the scope of this article.
## 3 Results and Discussion  
### Characterization of Elderberry Pomace  
Elderberry pomace after freeze-drying presented an average dry mass content of 99.54 \(\pm\) 0.03%. Solvent extraction with water, after two sequential extractions, allowed us to obtain a total phenolic content of 2.98 mg GAE g\({}^{-1}\) of pomace, 1.48 mg cy3 glcE g\({}^{-1}\) of total monomeric anthocyanins, and ABTS**-**  
The overrun is related to the volume and amount of air present in the sorbet and is one of the main criteria for evaluating the quality and profitability of the product [38]. The overrun depends on several factors, such as the presence of fat, sweeteners, and stabilizers. Sorbet P, which was prepared with the addition of powdered pomace, had the highest overrun value (28.3%), followed by sorbet E-50 (19.0%), which contains the most concentrated pomace extract, while sorbet C without added pomace and sorbet E-5 prepared with the most diluted pomace extract showed lower values, 13.7 and 10.9%, respectively.  
The melting rate of ice cream and other frozen desserts is influenced by several factors, including overrun, the amount of air incorporated, the nature of the ice crystals, and the extent of fat destabilization [48]. The sorbet melting curves are shown in Figure 3. Sorbet E-50 melts faster than the other sorbets. Sorbets E-5 and P present similar melting curves and melt more slowly than sorbet C (sorbet without added pomace), however, sorbet P has a behavior very close to sorbet C in the first 40 min. Sorbets P and E-5 melt more slowly than sorbet C.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sorbet** & **pH 2** & \({}^{\circ}\)**Brix 2** & **Overrun (\%)** \\ \hline C & 3.52 \(\pm\) 0.02 b  & 26.25 \(\pm\) 0.08 a  & 13.7 \\ P & 3.51 \(\pm\) 0.05 b  & 25.90 \(\pm\) 0.25 b  & 28.3 \\ E-5 & 3.52 \(\pm\) 0.01 b  & 10.9 \\ E-50 & 3.68 \(\pm\) 0.05 a  & 26.30 \(\pm\) 0.09 a  \\ \hline \hline \end{tabular}
\end{table}
Table 2: pH, \({}^{\circ}\)Brix, and overrun values of prepared sorbets 1.  
The sorbet color attributes are presented in Table 3. The \(L^{*}\) parameter presented values between 39.4 and 47.5. Compared with the sorbet obtained without adding pomace (sorbet C), sorbent E-50, which contains concentrated elderberry pomace extract, exhibited lower values of the parameter \(L^{*}\), indicating that the addition of pomace contributed to darker color attributes in sorbet. The lower values of \(L^{*}\) in this sample are related to the presence of a higher content of anthocyanins that give a darker hue to the sorbet. The \(a^{*}\) values are between 25.1 and 27.3 and the \(b^{*}\) values between 8.0 and 11.5. Sorbet E-50 that contains the most concentrated pomace extract in its composition showed a tendency toward a lower \(b^{*}\) value. Although this difference was not statistically significant, it is consistent with a higher anthocyanin content, contributing to a bluer hue in the final product.
# Assessment of Negative Gingival Recession: A Critical Component of Periodontal Diagnosis
## 6 Clinical Assessment Approaches
### Imaging Technologies
We know from earlier studies that inflammation greatly impacts the probing depth measurement with the probe going past the junctional epithelium in inflamed sites while stopping coronal to apical termination of junctional epithelium in noninflamed sites [109; 110; 111]. Most of the time, clinical attachment levels by probing are within 1 mm of the true histological level of the connective tissue attachment [71]. Although current B-mode ultrasound imaging cannot directly determine the true CAL, it can aid in its calculation by accurately measuring the amount of negative GR. This can supplement the clinical probing depth to calculate CAL directly. Alternatively the distance from the CEJ-bone crest can be measured by ultrasound, then the CAL can be indirectly calculated by subtracting 1 mm of average dimension of connective tissue attachment [8] from the CEJ-bone crest distance. In addition, ultrasound-image modalities other than B-mode might be able to detect the junction between the detached and attached gingival tissues and are currently under active investigation. Figure 3: Modern miniature-sized ultrasound probe (transducer) with high frequency (24 MHz). Together, the advantages and disadvantages of different clinical approaches to assess negative GR are summarized in Table 2. Accurate assessment of the gingiva recession is a key component of periodontal diagnosis, especially in the incipient stage of the disease. Future research is warranted to differentiate the relationship between detached epithelium and connective tissue attachment using high-frequency ultrasound imaging. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Advantages** & **Disadvantages** \\ \hline Periodontal probing & Easy, economical, quick & Prone to visual and tactile error \\ \hline
2D radiography & Economical & Only detect interpersonal CEJ, poor soft-tissue contrast resolution, errors related to image-acquisition angle \\ \hline
3D radiography & CEJ is identifiable at bucco/lingual and interproximal sites & Ionization, expensive, poor soft-tissue contrast, lower image resolution \\ \hline Ultrasound imaging & Noninvasive, nonionizing, chairside, providing digital image including dimension from gingiva margin to CEJ & Coupling agent needed, time-consuming, technique-sensitive \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of the methods to determine negative gingival recession. Figure 4: Example of an ultrasonographic cross-sectional image showing negative gingival recession on a maxillary central incisor. Colored areas represent different anatomic locations. Red: gingiva; yellow: alveolar bone; green: tooth. Cyan round point represents the CEJ. X measurements indicate the amount of negative gingiva recession.
# Review of Remote Sensing Methods to Map Coffee Production Systems
## 3 Results
### Hybrid Approaches  
Other, generally more complex, approaches used multiple sensors and/or approaches within the same classification model to map coffee aiming to leverage the strengths of different methods. For example, [PERSON] et al. (2017) used tone, color, texture, shape, size, pattern, and shade as elements to classify coffee and four other land cover classes using visual interpretation. While the exact method they used was not fully documented, we considered this a hybrid approach. [PERSON] et al. (2017) performed an analysis technique that combined a pixel-based approach with a texture-based approach. This study used a \(3\times 3\) pixel window to map tree canopies using a multi-layer perceptron and divided the canopies based on heterogeneity and size using NDVI, VIN, and 48 textural attributes to identify suitable areas for coffee inter-specific hybridization. [PERSON] et al. (2017) utilized an integrated pixel-based analysis and object-based approach to create extent maps of various coffee production systems in Sumberjaya, Lamping Province, Indonesia, with an overall accuracy of 84.5%, and [PERSON] et al. (2017) utilized pixel-based analysis and an object-based approach to create a cropland extent map of the African continent, with an overall accuracy of 94.5%. [PERSON] and [PERSON] (2017) created a model using both object-based and subpixel-based analyses. They also used data mining techniques to create objects based on spectral metrics from a spectral mixture analysis (using dark green vegetation and non-photosynthetic vegetation endmembers) and a root mean square error fraction image. Their approach effectively distinguished coffee classes from non-coffee classes, with an accuracy greater than 70%. Other hybrid examples include the studies by [PERSON] et al. (2017), which involved combining a spectral pixel-based method and photointerpretation, and [PERSON] et al. (2017), which incorporated textural analysis in conjunction with spectral pixel analysis.
# Single Trace Analysis on Constant Time CDT Sampler and Its Countermeasure
## 5 Conclusions  
In this paper, we showed that the existing constant-time CDT sampling method for Gaussian sampler is vulnerable against STA and proposed its countermeasure. Since the knowledge of the sampled value can reveal the secret key by using the Gaussian elimination, security regarding the implementation has been recognized as an important problem. However, as efficiency is as important as security when implementing the Gaussian sampler, only constant-time algorithms for Gaussian samplers have been proposed in order to prevent the timing side-channel attacks. However, we proposed that STA was possible for the constant-time CDT sampling method. The constant-time CDT sampling is vulnerable to STA due to the gap of Hamming weight of negative and positive values. This value can be related to adding one and zero to some variable, and, consequently, the sampled value can be revealed. We also recovered the sign bit of the sampled value in a similar way. Therefore, we were able to recover the exact sampled values each time the algorithm was executed. In order to resist STA, we proposed an alternative algorithm for the CDT-based Gaussian sampler. The idea is to use a look-up table that precomputes the sampled value from a given input. Unlike calculating the differences in the constant-time CDT sampler, the proposed countermeasure uses the random value as the index of the precomputed table and outputs the corresponding table value. Since there is no Hamming weight difference or the difference in the execution of an algorithm depending on the selected random value, an attacker cannot extract information from a single power consumption trace. In addition, we implemented our countermeasure in Lizard and check its performance. The proposed countermeasure does not degrade the performance since it works by directly referencing table values.  
[PERSON] performed the experiments, analyzed the data, and wrote the paper. [PERSON] analyzed the data and verified the validity of results.  
This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2018-2015-0-00385) supervised by the IITP (Institute for Information & communications Technology Promotion).  
The authors declare no conflict of interest.
## 3 Proposed System Model
### Neural Network Architecture in DQN  
DQN are an extension of Q-learning, leveraging DNN to approximate the Q-value function in environments with large or continuous state spaces as depicted in Figure 5. Traditional Q-learning methods struggle with high-dimensional state spaces due to their reliance on tabular representations. DQN mitigates this limitation by using a DNN to generalize Q-values across similar states, thereby improving convergence and stability.  
The neural network in DQN consists of multiple layers that process the state representation and estimate the Q-values for each possible action as depicted in Figure 6. Let the state space be represented as \(S\in\mathbb{R}^{d}\), where \(d\) is the dimensionality of the state vector. The neural network approximates the optimal action-value function:  
\[Q^{*}(s,a)=\max_{\pi}\mathbb{E}\left[\sum_{t=0}^{\infty}\gamma^{t}r_{t}|s_{0}=s,a_{0}=a,\pi\right] \tag{21}\]  
where \(Q^{*}(s,a)\) is the optimal Q-value for state-action pair \((s,a)\), \(\gamma\in[0,1]\) is the discount factor, and \(r_{t}\) represents the immediate reward at time step \(t\). The neural network is trained to minimize the difference between predicted and target Q-values through a loss function.  
Figure 5: DAWPC-MAC protocol workflow.  
Figure 6: The Deep Q-Network (DQN) Architecture.
###### Abstract  
Carbon nanotube-polymethyl siloxane (CNT-PDMS) composite transducers generate shock waves using optoacoustic technology. A thin layer of thermally conductive CNT and elastomeric polymer, PDMS, is applied on the concave surface of transparent polymethylmethacrylate (PMMA) to convert laser energy to acoustic energy using the thermoelastic effect of the composite transducer. The efficient conversion of laser energy requires an optimum utilization of the different properties of composite transducers. Among these properties, the diameter of composite transducers is a significant parameter. To practically verify and understand the effect of the diameter of composite transducers on the properties of shock waves, CNT-PDMS composite transducers with different diameters and focal lengths were constructed. Increases in the diameter of the composite transducer and input laser energy resulted in increased peak pressures of the shock waves. The maximum positive and negative pressures of the shock waves generated were \(53\) MPa and \(-25\) MPa, respectively. This practically demonstrates that high peak amplitudes of shock waves can be achieved using larger transducers, which are suitable for practical applications in transcranial studies.  
Keywords:therapetic ultrasound; photoacoustic effect; shock waves; laser-generated focused ultrasound; PDMS; carbon nanotubes +
Footnote †: journal: Applied
## 3 Results
### Positioning Patients on Their Prone Position Compared to the Supine Positioning  
[PERSON] et al. conducted a pilot study in which they proved the beneficial effect of the DIBH technique on mean and maximum cardiac radiation doses in patients in the supine position [10]. The researchers determined that the mean dose and maximum dose for OARs in DIBH on the abdomen compared to free-breathing patients appeared to be lower by 0.21 Gy and 8.51 Gy, respectively, for the heart but higher by 0.23 Gy and 8.7 Gy for the left lung. These doses were comparable for the LAD [10]. The largest meta-analysis involving as many as 19 studies and 751 hospitalized patients conducted by [PERSON] et al. drew the conclusion that DIBH treatment on the prone position (P-DIBH) guarantees better OAR protection than the same lying on the back (S-DIBH) or free breathing lying on the prone position (P-FB) [12]. Based on the recommendations of the German Society for Radiation Oncology (DEGRO), [PERSON] et al. concluded that the abdominal DIBH method is mainly performed in patients with increased breast volume, and the results of organ-sparing studies in this position are not consistent. [PERSON] et al. suggested that patients with smaller breasts report more benefits from DIBH lying in the prone position [17]. Comparing nationwide trends in DIBH practice, [PERSON] et al. determined that in a group of 530 U.S. physicians, 244 of whom had more than 15 years of seniority, as many as 440 survey respondents reported using abdominal and/or DIBH patient positioning in clinical practice, with Varian RPM being the most common system (286 respondents) [14]. In contrast, [PERSON] et al. reported higher dosimetric doses in 0.62 patients with left-sided breast cancer treated on the abdomen [28].  
The use of both techniques is dependent on the facilities that the center has and the physicians performing the treatment, and the superiority of one technique over the other is debatable [10; 28]. The positive effect on sparing the heart during DIBH radiotherapy in the abdominal position is explained by a gravity-induced increase in the distance between the breast and heart [4; 7; 29]. [PERSON] et al. confirmed the protective effect on the LAD of the abdominal DIBH method [6]. In the supine position, in the vast majority of cases, the radiation dose to the heart is lower than in patients treated in the supine position with free breathing [7]. The treatment of patients on the abdomen also has positive effects in patients with small breasts [17]. A study by [PERSON] et al. showed that the change in radiation dose dependent on positioning is influenced by the plasticity or breast depth ratio of patients, among other factors [28]. Undoubtedly, the technique of treating patients in the abdominal position has promising results. Despite the above studies, there is a further need to isolate patients who will benefit most from either technique.
# Towards a Reference Architecture for Cargo Ports
## 2 Background
### Cargo Ports
#### 2.2.1 Overview  
For millennia, mankind has shipped goods across the oceans, from one land to another. The loading and unloading of a ship have always been very labor-intensive. A ship could easily spend more time in port than at sea while dock workers handled cargo in and out of tight spaces below decks. The introduction of containerization has greatly simplified this process, and nowadays goods can be moved seamlessly between ships, trucks, and trains [27]. The U.S. Bureau of Transportation reports that more than seventy-seven percent of freight tonnage entering the U.S. came by water, compared to 22 percent by land and only 0.3 percent by air [28]. By any measure, marine transportation is the primary means of moving goods and raw materials to and from the U.S. While the U.S. represents only 4.5 percent of the world's population [29], it accounts for 9 percent of the worldwide container traffic, with one container out of every eleven engaged in global trade either bound for or originating in the U.S. [30]. The largest amount of cargo traffic in the world is done by China [31]. The largest container ships can currently carry over 20,000 TEUs (a TEU is the volume of a 20 ft. container).  
Port automation has been playing an increasingly important role with the introduction of robots, artificial intelligence, and other digital tools that keep the goods flowing into and out of major ports. This technology is widely seen as the most efficient way for seaports to cope with rising global shipping traffic and massive new ships that haul more and more containers. By digitizing and automating activities once handled by human crane operators and cargo haulers, seaports can reduce the amount of time ships sit in port and thus boost port productivity by up to 30%, according to some estimates [32]. IoT advances have had a strong impact on the automation of port activities, allowing the remote control of cranes and vehicles.