esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Architecture
### Virtual Infrastructure Session  
The VIS is created as soon as the endpoints and intention are known. It offers a distributed querying and control layer distributed across the different domains which functions like an Identity Management system. Identifiers are mapped according to the domain and layer of the request. A URL might become an IP or a MAC address depending on the layer at which the data is indexed. The advantage of this system is that it is agnostic to the protocols offering a general purpose API to deal with data about a communication at all levels. In sum, a VIS has an identity of its own. Below is a description of the interfaces and their main functions:  
**Application (Alpha) API:** The alpha API provides applications with the controls to create new VISs, populate them and use them to transport data at an application level. The API uses the identities and intents, together with the context of the access to provide discovery and session setup.  
Figure 2: Future Internet identity architecture using Virtual Infrastructure Sessions.  
**Control (Gama) API:** This API provides VIS with a mechanism to interact with domain specific control layer. For example, it could be used to setup QoS or pinholes to allow for traffic to traverse across domains. Since our architecture could potentially use many different types of transport, the API ensures compatibility between Internet, Content Centric Networking, NodeID, _etc._ In some extreme cases, there might be no Beta API since the transport layer supports this architecture natively.  
**Transport (Beta) API:** In cases where transport is not native, this API ensures the communication and abstraction between the different control layer protocols and the actual transport.  
Each of these APIs vary from simple to complex depending on the functions to be supported, and are designed to be open for future extensions. The intention is to reuse existing protocols and standards as far as possible and only extend these when needed. The overall architecture may thus also be seen as a framework that encompasses the existing state of the art.
# Effects of Freeze-thaw Cycles on the Mechanical Properties and Microstructure of a Dispersed Soil
## 3 Result and Discussion
### Soil Sample Dispersion Identification Test
#### 3.1.3 Double Hydrometer Test  
The double hydrometer test is an additional method employed to assess soil dispersibility. The double hydrometer test provides insights into the particle composition of soil by dispersing a soil sample into primary particles in water. The grading of soil particles has a significant influence on soil dispersibility. The experimental data are presented in Table 4. This dispersion exceeds 85% in this experiment (Figure 6), indicating strongly dispersed soil. The double hydrometer test results demonstrate that a significant portion of clay particles can be separated under vacuum conditions, indicating that the soil samples exhibit a relatively loose structure and poor particle aggregation after undergoing freeze-thaw cycles. As a result, the soil samples exhibit a state of high dispersion.  
Figure 5: Photos of the results of the crumb test: (**a**) 2 min, (**b**) 1 h, and (**c**) 6 h.  
The outcomes of the pinhole, crumb, and double hydrometer tests consistently indicate a progressive increase in the dispersibility of the examined soil samples as the number of freeze-thaw cycles rises. During the freezing process, water-facilitated movement of sodium ions results in the augmentation of exchangeable sodium ions enveloping soil particles and an expansion of the diffusion layer. This phenomenon effectively elucidates the elevated dispersibility of the dispersed soil in the investigated region. Throughout the winter season, certain sodium salts crystallize within the frozen soil layer, establishing a concentration gradient between the frozen and unfrozen soil layers. Consequently, sodium salts migrate to the frozen layer alongside a surge in thin-film water. The accumulation of substantial salt content on the soil surface drives notable dispersion. Moreover, frost heave during freezing augments the inter-particle spacing, leading to a reduction in cohesive forces between soil particles and encouraging soil dispersion [2]. Additionally, freeze-thaw cycles alter the interconnection among soil particles, disrupting the existing colloidal connections and causing aggregates to disintegrate, further enhancing soil dispersibility.
# Non-Cooperative Spacecraft Pose Measurement with Binocular Camera and TOF Camera Collaboration
## 3 Detailed Description of the Proposed Method  
The specific details of the pose measurement method are presented in this section. Figure 3 illustrates the general flow of the proposed method in this study.
# Efficient and Secure EMR Storage and Sharing Scheme Based on Hyperledger Fabric and IPFS
## 5 Scheme Implementation
### Data Storage Phase
#### 5.3.1 Encrypting Medical Records  
Patient \(U_{i}\) undergoes diagnosis and treatment at hospital \(H\), generating medical data plaintext \(M_{EMR}\) and its corresponding EMR's number \(N_{EMR}\). After patient confirmation, \(H\) encrypts the medical data excluding the patient's personal information to obtain the ciphertext of medical data, \(C_{EMR}=En_{pk}(M_{EMR})\), and calculates the message digest of the medical data ciphertext: \(MD_{EMR}=Hash(C_{EMR})\).  
The doctor uses their private key \(sk_{doc}\) to sign \(MD_{EMR}\) and \(uid_{doc}\), resulting in a digital signature \(DocSign_{EMR}\) as shown in Equation (3):  
\[DocSign_{EMR}=Sign_{sk_{doc}}(MD_{EMR},uid_{doc}). \tag{3}\]
# K/Ka-Band Very High Data-Rate Receivers: A Viable Solution for Future Moon Exploration Missions
###### Abstract  
This paper presents a feasibility study for a very high data rate receiver operating in the K/Ka-band suitable to future Moon exploration missions. The receiver specifications are outlined starting from the mission scenario and from a careful system analysis. The designed architecture uses a low noise front-end to down-convert the incoming K/Ka-band signal into a 3.7 GHz intermediate frequency (IF). For maximum flexibility, a software defined radio (SDR) is adopted for the I/Q demodulation and for the analog to digital conversion (ADC). The decoding operations and the data interface are carried out by a processor based on field programmable gate array (FPGA) circuits. To experimentally verify the above concepts, a preliminary front-end breadboard is implemented, operating between 27.5 and 30 GHz. The breadboard, which uses components off the shelf (COTS) and evaluation boards (EVBs), is characterized by a 46 dB gain, a 3.4 dB noise figure and a \(-\)37 dBm input-referred 1 dB compression point. Finally, a 40 Msym/s quadrature phase shift keying (QPSK) signal is demodulated by means of a commercially available SDR, demonstrating the above concept. The importance of these results is that they have been obtained exploiting a class of miniaturized and low cost microwave integrated circuits currently available on the market, opening the way to a dense communication infrastructure on cislunar space.  
**Keywords:** microwave receivers; K/Ka-band; Moon; cislunar space; exploration missions; cubesats; phase-noise; effective noise figure
## 3 Materials and Methods
### Experimental Design  
#### 3.5.1 Evaluation Metrics  
Model performance was evaluated using metrics such as precision, recall, accuracy, and F1-score, which assess classification performance from various perspectives. Precision measures the proportion of correctly predicted positive samples among all samples predicted as positive, while recall reflects the model's ability to identify all actual positive samples. Accuracy quantifies the proportion of correctly predicted samples among the total, providing a general measure of performance. However, due to the trade-off between precision and recall, the F1-score, defined as their harmonic mean, is used to balance these metrics, especially in scenarios with imbalanced class distributions. The corresponding formulas are expressed as follows:  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}}, \tag{37}\]  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}}, \tag{38}\]  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}}, \tag{39}\]  
\[\text{F1-Score}=2\cdot\frac{\text{Precision}\cdot\text{Recall}}{\text{ Precision}+\text{Recall}}. \tag{40}\]  
In these formulas, TP (true positive) represents the number of correctly predicted positive samples, FP (false positive) represents the number of incorrectly predicted positive samples, TN (true negative) represents the number of correctly predicted negative samples, and FN (false negative) represents the number of actual positive samples that were not correctly predicted. Precision and recall capture the model's performance in terms of prediction accuracy and detection capability, respectively, while accuracy serves as an intuitive overall performance indicator. However, when class distribution is imbalanced, relying solely on accuracy may misrepresent model performance,as it does not effectively reflect the model's ability to predict minority classes. F1-score is particularly useful in these cases as it balances precision and recall.
## 3 Results
### Comparison between the Adsorption and the Photocatalysis Methods  
As mentioned in the introduction, this work comprehensively compared two methods for the water treatment, i.e., adsorption and photodegradation techniques, using the same support like AC-OH and AC-TiO\({}_{2}\) for adsorption and photodegradation, respectively. Despite the interesting results obtained in the above section for both processes, a distinguishing difference can be illustrated in many aspects, particularly the reusability and the cost. Accordingly, the most useful and more suitable methods for wastewater treatment can be selected. On the one hand, the photocatalytic degradation of pharmaceutical products is based on utilizing TiO\({}_{2}\) nanoparticles, which represents one of the most promising methods to decontaminate water containing organic pollutants, such as AT and PR. In its nano-metric form, titanium dioxide is considered the first interesting catalyst for photocatalytic wastewater treatments, as it presents high stability. Also, in light of the results obtained in this work, it was found that TiO\({}_{2}\) incorporated over AC is attractive due to its highly reactive surfaces, which make them a good photocatalyst of pollutants. Unlike the high energy consumption caused by the UV light during the sorbent activation, nanoparticle powders can be released in treated water, contaminating the environment. As the toxicity of the environment is not accepted to any degree, photodegradation via AC-TiO\({}_{2}\) is limited  
Figure 8: Effect of regeneration cycle on adsorption removal for PR and AT pharmaceutical contaminants.  
and cannot be developed on a large scale, including any industrialization of the process. However, the adsorption process represents an appreciable removal capacity of up to 89% of AT and PR pharmaceutical pollutants. Likewise, the adsorption methods are widely employed since it provides many advantages, such as low energy consumption, strong reusability, cost-effectiveness, and easy handling.  
Interestingly, no secondary contamination can be caused by this eco-friendly adsorption during water treatment. To date, wastewater treatment by adsorption can be used in full-scale applications, and very effective results are obtained using this method [15; 16; 46; 47]. On the other, to assess the possible environmental impact and the safety of the whole process, the absence of toxic effects of the process effluents must be guaranteed. Accordingly, the adsorption process is more efficient than photodegradation methods.
## 2 Theory and Simulation
### Seismic Wave Selection and Working Condition Setting  
#### 2.3.1 Seismic Wave Selection  
The cable-stayed pipe bridge structure system discussed in this paper is situated in the 8-degree area, classified as a class II site, with a characteristic period of 0.35 s. Therefore, the EI-Centro seismic wave-recorded data were selected as the input seismic wave for analysis. This study primarily focuses on investigating the structural damage mechanism under strong earthquake events; hence, the peak acceleration of the seismic wave was adjusted to 10.0 m/s\({}^{2}\), and an earthquake duration of 50 s with a time interval of 0.02 s was considered for analysis purposes. It should be noted that this paper solely examines consistent excitation inputs of seismic waves for the pipe bridge structure.
###### Abstract  
This study investigated the potential of five pyrrole-imidazole alkaloids from the marine sponge _Agelas_ sp. to inhibit key targets in neuroblastoma, the most common pediatric malignant solid tumor. Molecular docking analysis using GOLD software (v4.1.2) revealed that Strepoxazine A (MoJ) and Tuarodispacamide A (MoJ5) exhibited the strongest inhibition of focal adhesion kinase 1 (FAK), caspase-3 (ca3), phosphatidylinositol 4.5-bisphosphate 3-kinase catalytic subunit gamma isoform (PI3K), telomerase reverse transcriptase (TERT), osm-9-like TRP channel 1 (TRPV1), and RAC-alpha serine/threonine-protein kinase (AKT1). Normal mode analysis using MODIS server confirmed the stability of the best complexes and pharmacokinetics, such as toxicity and predictions of biological activity as inhibitors of anticancer targets, indicating a balance between efficacy and safety for bothMol3 and Mol5. The remaining compounds (Ageladine A, Oroidine, and Cyclooroidine) showed moderate effects, with significant toxicity, suggesting limited therapeutic potential. The promising results of our in silico-study suggest that Strepoxazine A and Taurodispacamide A could serve as novel therapeutic agents for neuroblastoma, potentially leading to more effective treatment options and improved survival rates for pediatric patients suffering from this challenging malignancy, although further in vitro and in vivo validation is needed.  
neuroblastoma; _Agelas_ sp.; pyrrole-imidazole alkaloids; GOLD; Strepoxazine A; Taurodispacamide A
# The Impact of Internal Structure Changes on the Damping Properties of 3D-Printed Composite Material
###### Abstract  
This article investigates the impact of changes in the internal structure of composite materials on their dynamic properties. The present research focuses on 3D-printed specimens with different reinforcement fiber arrangements. The specimens are printed on a Markforged Mark Two 3D printer. The base material is nylon filled with chopped carbon fibers (Onyx) and the reinforcement is in the form of long carbon, glass and Kevlar fibers. The experiment is carried out by the impact method. The principle of this method is to expose the specimen to a short impulse of force while monitoring its frequency response. The obtained results determine the natural frequencies and internal damping of the individual structures. We found that the highest damping is achieved by specimens with glass and Kevlar fibers in 45\({}^{\circ}\), 90\({}^{\circ}\) and \(\pm\)45\({}^{\circ}\) configurations. On the other hand, the lowest damping is achieved by specimens with carbon fibers and 0\({}^{\circ}\) and 0\({}^{\circ}\),90\({}^{\circ}\) configurations. Also, the specimens with circumferential reinforcement show lower damping coefficient values. The knowledge and results of this work can be used in the development of new components; for example, in the transport industry, where the low weight and sufficient strength of components are important factors. These components have to absorb vibrations from various sources, such as motors and external influences.  
internal damping; composite material; long fiber reinforcement; 3D printing bump test; half-power bandwidth method +
Footnote †: journal: Applied sciences
# An Efficient Routing Protocol with Overload Control for Group Mobility in Delay-Tolerant Networking
## 2 A Proposed Group Mobility Model and Group Delivery Predictability Model
### A Proposed Delivery Predictability for Group Mobility Model  
In this paper, a PRoPHET protocol is considered for message delivery between nodes basically. Then, it is extended for a group mobility model. In a PRoPHET protocol, a message is forwarded based on delivery predictability between two nodes, and delivery predictability between node \(a\) and node \(b\), i.e., \(P(a,b)\), is calculated as follows [8]:  
\[P(a,b)=P(a,b)_{old}+(1-\delta-P(a,b)_{old})\times P_{encounter}, \tag{1}\]  
\[P(a,b)=P(a,b)_{old}\times\gamma^{K}, \tag{2}\]  
\[P(a,c)=\max(P(a,c)_{old},P(a,b)\times P(b,c)\times\beta), \tag{3}\]  
where \(P_{encounter}\), \(\gamma\), \(K\), and \(\beta\) are a scaling factor, a limiting parameter, an aging constant, the number of elapsed time units after the last contact, and a parameter to control transitivity, respectively.  
In this paper, we extend the above equations for delivery predictability to accommodate a group mobility model. To distinguish the proposed delivery predictability for group mobility, we denote it as group delivery predictability. The delivery predictability defined in Equations (1)-(3) is called individual delivery predictability for convenience. Since nodes within the same group move by following the same group mobility model, they have more contacts with each other than those between nodes, which belong to different groups, and thus have more opportunities to deliver messages to other nodes within the same group. Therefore, we define delivery predictability between a node and a set of nodes within a group as group delivery predictability. For simplicity, we assume that the group information of a node can be extracted from a node identifier. When node \(n_{A}\) in group \(A\) contacts node \(n_{B}\) in group \(B\), the group delivery predictability between node \(n_{A}\) and a set of nodes in group \(B\), \(P_{g}(n_{A},\mathbf{G}_{B})\), are defined and calculated as follows:  
\[P_{g}(n_{A},\mathbf{G}_{B})=P_{g}(n_{A},\mathbf{G}_{B})_{old}+\big{(}1-\delta-P_{g}(n _{A},\mathbf{G}_{B})_{old}\big{)}\times P_{encounter}, \tag{4}\]  
\[P_{g}(n_{A},\mathbf{G}_{B})=P_{g}(n_{A},\mathbf{G}_{B})_{old}\times\gamma^{K}, \tag{5}\]  
\[P_{g}(n_{A},\mathbf{G}_{C})=\max\big{(}P_{g}(n_{A},\mathbf{G}_{C})_{old},P_{g}(n_{A}, \mathbf{G}_{B})\times P_{g}(n_{B},\mathbf{G}_{C})\times\beta\big{)}, \tag{6}\]  
\[P_{g}(n_{A},\mathbf{G}_{A})=1, \tag{7}\]  
where Equation (4) shows the group delivery predictability calculation, where delivery predictability between nodes \(a\) and \(b\) in Equation (1) is changed to group delivery predictability between a node \(n_{A}\) and a set of nodes in group \(B\), \(P_{g}(n_{A},\mathbf{G}_{B})\). Equation (5) shows the aging of group delivery predictability and Equation (6) shows transitivity of group delivery predictability. Group delivery predictability between a node and a group including the node itself is defined as 1, as in Equation (7).
## 3 Results and Discussion
### Model Training Based on Machine Learning (ML)  
The training process began by splitting the data set into training and testing subsets, where the training set was used to develop the model, and the testing set was reserved for evaluation. This approach ensures that the model is not overfitted to the training data and can generalize to new, unseen data. The multi-regression model, which was built based on the data shown in Figure 6, was iteratively trained to optimize its coefficients for predicting the pre-reformer outlet temperature.  
Figure 6 depicts the relationship between the predicted responses (y-axis) and the true responses (x-axis) for the test data, highlighting the model's predictive performance. The points are scattered closely along the diagonal line, which represents the ideal case where predicted values perfectly match the actual values. The close proximity of the red data points to this line indicates that the model has been well-trained and is capable of making accurate predictions. This strong alignment between predicted and true values reflects the model's ability to capture the complex interactions among input variables and translate them into reliable predictions. Minor deviations from the diagonal line show areas where the model may have slightly under- or over-predicted the temperature, but these deviations are small, suggesting high accuracy.  
The visualization in Figure 6 confirms the fact that the machine learning model is effectively predicting the pre-reformer outlet temperature based on the multi-regression framework. It also emphasizes that the model's predictions generalize well to unseen test data, further supported by the performance metrics obtained through cross-validation. A k-fold cross-validation technique was applied to further refine the model and assess its stability. The data were divided into 10 folds, with the model being trained on 9 folds while the remaining fold was used for validation. This process was repeated 10 times, each time with a different fold as the validation set, which provided a robust estimate of the model's performance by averaging the results across all iterations. This method not only reduced the risk of overfitting but also helped in assessing how consistently the model performed across different subsets of data. Details of the procedure are given in Ref. [35].