esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### IOTA Challenges  
Given the business requirements for data integrity and immutability of the gathered sensor data, the use of a DLT is an appealing solution to an existing problem. Moreover, the usage of cryptocurrencies empowers the system to easily perform automated M2M payments without intervention of the human factor. The implementation concerns the precision agriculture domain, a domain where data can be essential for certain activities, such as crop insurance. Ensuring the accuracy and consistency of the stored data to the detected data sets is critical and requires the exploration of experimental technologies, such as the DLT. Since, there is intention to use the ledger as an immutable storage of sensor data, albeit a temporary one, it is expected to have several transactions per SN. This is a restricting characteristic since most blockchain based projects have a high fee for each transaction, spiraling the upkeep cost of the system. The IOTA protocol, on the other hand, supports transactions with zero fees, demanding only lightweight computation (1-2 min on a PC).  
Therefore, it is imperative to design and implement a system where each Super Node aggregates sensor data and issues a bundle at the FN that is hosted in the cloud. The SNs can select how many datasets will be encapsulated in each MAM message, varying on the granularity the system offers, in respect to granting access to specific data. In our case, the Super Node aggregates data from all the IoT Nodes that are placed in the field and forwards them to an Edge service on the cloud. Instead of running an Edge service on the SN, a simulated process was chosen in order to decouple sufficiently the two prototype implementations, the first being the prototype hardware infrastructure of the IoT Nodes and the Super Node and the second being the IOTA data and service layer. The Edge services process the aggregated data so each Node's data are encapsulated into a different MAM stream. This design choice allows the field owner to sell data streams from each Node. The implementation also includes a data marketplace interface, where an potential user can overview the stakeholders that use the system and make a choise to purchase access. Actually, the user purchases the necessary PSK in order to decrypt the messages found in the already known _child_ of the data stream. In the future, the implementation will support multiple authentication modes and workflows, such as the use of Private/Public key cryptography (NTRU). This is exceptionally important as it offers a wide range of access control to support multiple different use-cases and business requirements.
## 2 Design of Real-Time Communication Subsurface Mooring System
### System Hardware Circuit Design
#### 2.3.2 Conductivity-Temperature-Depth Sensor (CTD)  
The conductivity-temperature-depth sensors with an inductive modem (CTD-IM, TD-IM) developed by the Institute of Deep-Sea Science and Engineering, Chinese Academy of Sciences, were unitized in this system. Taking CTD-IM as an example, it has the function of inductive coupling communication and can collect three parameters including temperature, pressure, and conductivity. The measurement depth can reach 7000 m with high measurement accuracy and small long-term drift. The temperature sensor uses a negative temperature coefficient thermistor as the sensing element, and the proportional measurement method is used to measure the value with an accuracy of \(\pm\)0.002 \({}^{\circ}\)C [23]. The pressure measurement uses an oil-filled pressure probe of MEMS technology [24; 25], and the internal measurement network can be simplified into a set of Wheatstone bridges, combined with a constant current source excitation circuit with extremely low-temperature drift and external compensation resistance. The accuracy of the pressure measurement can reach up as \(\pm\)0.03% F.S. The conductivity is measured through the seven-electrode method, and the high-precision signal acquisition and processing circuit are designed to reach an accuracy of around \(\pm\)0.003 mS/cm [26].
## 4 Implementation of the Extended Algorithm
### Model Optimization
Thus, one can discover a process model optimized in one of these senses or both. The representativeness of a process model regarding a log is measured by the replayability [35; 45], also called fitness or fidelity. This metric works fine with flexible logs with highly diverse and complex behaviors of the traces. It also overcomes DFG issues with alignments. Replayability is directly related to model complexity (6) and (7): a model with higher complexity allows for more traces and therefore higher replayability, making these measures contradictory objectives. A remarkable feature of replayability (5) is that it is scaled to be a number in [0, 1]. So, it can be combined with the scaled complexity term (6) in one objective function (4). Here and after, we assume \(\alpha=0.5N^{-1}\) and \(\beta=N^{-1}\), where \(N\) is defined as the number of unique activities in the log. A complexity function could be performed as one of the network complexity measures [54; 55; 56]. In this study, we compare different measures, one of which is the Shannon entropy. Entropy can be measured across various network invariants [55]. For the current study, we chose a flattened adjacency matrix as a random variable \(X\) with two possible outcomes (0 or 1) to measure the Shannon entropy \(H(X)\):  
\[\mathcal{J}:\ H(X)=-\sum_{i=1}^{n}p(x_{i})\log_{a}p(x_{i}) \tag{8}\]  
Alternatively, we have introduced several structural complexity measures as follows:  
\[\mathcal{J}:\ K_{n}=\frac{m}{n(n-1)} \tag{9}\]  
\[\mathcal{J}:\ R=\frac{1}{2}\ \cdot\Big{(}\frac{m}{M}+\frac{n}{N}\Big{)} \tag{10}\]  
where \(N\) and \(M\) are the numbers of unique activities and transitions in the log, and \(n\) and \(m\) are the numbers of the activities and (unique) transitions presented in the model, respectively. The above measures, therefore, explain how many elements were displayed in the model among theoretical or possible ones. They can also specify model complexity. However, it may penalize model a lot and shorten process behavior. So, we currently chose a simple graph measure, an average degree. In a directed graph, it is just the number of edges divided by the number of nodes:  
\[\mathcal{J}:\ AD=\frac{m}{n} \tag{11}\]  
This measure is well suited to our aims: we want to reduce the number of transitions and conserve only the significant ones, while lengthening paths through the model by keeping more activities. This way, one may achieve a more transparent, less confusing, and consistent process model. In Section 5, we present an experiment on complexity optimization with different measures and show examples of process models automatically discovered with this approach.
# Embedding Equality Constraints of Optimization Problems into a Quantum Annealer +
## 8 Experiments
### Multi-Row Algorithm
#### 8.1.1 Embedding in the Real Chimera Graph  
We first sketch our approach to deal with imperfect Chimera graphs. The main idea is that we use only cells that are complete (meaning they have maximum number of vertices and edges) to represent gadgets, while all other (incomplete) cells are only used for interconnecting perfect ones. Specifically, if we have two complete cells \(\sigma_{1}\) and \(\sigma_{2}\) that are separated by one or more faulty cells, and need to connect interface variable \(t_{2}\) from \(\sigma_{1}\) with interface variable \(t_{1}\) from \(\sigma_{2}\) (see Figure 7), we create a \"chain\" between \(t_{2}\) and \(t_{1}\) from qubits from the faulty cells, with interconnecting couplers between qubits set to \(-1\), thereby forcing all qubits on the chain to get the same value. (A similar idea of constructing chains is used to minor-embed graphs representing general QUBO problems into the Chimera graphs.) For instance, Figure 13 shows two perfect cells \(\sigma_{1}\) and \(\sigma_{2}\) with one faulty cell \(\sigma_{f}\) between them. The coefficients \(\mathbf{a}\), \(\mathbf{b}\), and \(\mathbf{c}\) of \(\sigma_{1}\) and \(\sigma_{2}\) are set to the values determined from the solution of the optimization problem from Section 6. To connect the \(t_{2}\) variable of \(\sigma_{1}\) (top of \(\sigma_{1}\)), denoted by \(t_{12}\), to the \(t_{1}\) type variable of \(\sigma_{2}\) (top of \(\sigma_{2}\)), denoted by \(t_{21}\), the weights on the edges/couplers of \(\sigma_{f}\) as well as the edge connecting \(\sigma_{1}\) to \(\sigma_{f}\) are set to \(-1\), forcing all variables in \(\sigma_{f}\) to take, in a minimum-energy solution, the same values as \(t_{12}\). Hence, this type of a connection has the same effect as connecting \(t_{12}\) directly to \(t_{21}\).  
Figure 12: The structure of the tiles for the case of \(gap=4\). (**a**) A cell of the Chimera graph. Short lines indicate the couplers connecting to the neighboring cells. (**b**) The logical structure of the tile corresponding to that cell. Edges correspond to couplers shown in red in (**a**).  
With this simple approach, we were able to embed the constraint (10) for \(n=94\), see Figure 14. (Please note that some faulty cells have all eight qubits but miss one or more couplers, which cannot always be seen in the figure's resolution.) We want next to test how certain parameters of the approach affect the quality of the embedding.
# An Analytical Framework in OFDM Wireless Networks Servicing Random or Quasi-Random Traffic
## 3 The P-S Multirate Loss Model under Restricted Accessibility
### The Case of the BR Policy (P-S/BR Model)  
A proper selection of the values of \(pb_{kJ}(j_{1})\) results in the classical BR policy. In that policy, a call of service-class (\(k\),\(l\)) has a reservation parameter \(t_{l}\) and a requirement of \(b_{l}\) subcarriers. The parameter \(t_{l}\) denotes the number of subcarriers reserved to benefit calls of all service-classes except for (\(k\),\(l\)). Since the BR policy is used to favor calls of high subcarrier requirements, it is obvious that it provides QoS to certain service-classes.  
The call admission mechanism in the case of the BR policy (P-S/BR model) consists of the following two cases: (a) if \((M-j_{1}-t_{l}\geq b_{l})\cap(j_{2}+p_{k}b_{l}\leq P)\) then the service-class (\(k\),\(l\)) call is accepted for service in the system, (b) if \((M-j_{1}-t_{l}<b_{l})\cup(j_{2}+p_{k}b_{l}>P)\) then call blocking occurs.  
By assuming that \(pb_{kJ}(j_{1})=0\) when \(j_{1}\leq M-b_{l}-t_{l}\) and \(pb_{kJ}(j_{1})=1\) when \(j_{1}>M-b_{l}-t_{l}\), then the BR policy is incorporated in the model.  
In the P-S/BR model, the determination of \(q(j_{1},j_{2})\)'s is based on (14), whose complexity is \(O(\mbox{MPKL})\)[56]:  
\[q(j_{1},j_{2})=\left\{\begin{array}{ll}1,&\mbox{for $j_{1}=j_{2}=0$}\\ \frac{1}{j_{1}}\sum\limits_{k=1}^{K}\sum\limits_{l=1}^{L}\alpha_{kl}(j_{1}-b_ {l})b_{l}q(j_{1}-b_{l},j_{2}-p_{k}b_{l})\\ &\mbox{for $j_{1}=1,\ldots,M$ and $j_{2}=1,\ldots,P$}\end{array}\right., \tag{14}\]  
where \(\alpha_{kl}(j_{1}-b_{l})=\left\{\begin{array}{ll}\alpha_{kl},&\mbox{for $j_{1}\leq M-t_{l}$}\\ 0,&\mbox{for $j_{1}>M-t_{l}$}\end{array}\right..\)Having obtained the unnormalized values of \(q(j_{1},j_{2})\), the calculation of the CBP \(B(k,l)\) of service-class (\(k,l\)) can be based on:  
\[B(k,l)=\sum\limits_{\{(j_{1}+b_{l}+l_{2}\sim M)\cup(j_{2}+p_{k}b_{l}>P)\}}G^{-1 }q(j_{1},j_{2}), \tag{15}\]  
while \(E(k,l)\), BP, SU, and PU are calculated via (6)-(9), respectively.
# Deploying CPU-Intensive Applications on MEC in NFV Systems: The Immersive Video Use Case
## 4 The Immersive Video ME App
### The i-EVS ME App
#### 4.1.2 The eSTORE VNFC  
The eSTORE VNFC offers local storage capabilities to groups of users. In this way, the operation of content upload and/or download to a storage system can be significantly accelerated, since it can be carried out without accessing the core network. This feature is particularly relevant during crowded events, when the backhaul connection becomes a contended resource that can originate bottlenecks for any activity involving the core network [24].
# Performance of Pozzolan-Based Reactive Magnesia Cement Mixes against Sulphate Attack
###### Abstract  
Reactive magnesia cement (RMC) has gained interest due to its lower production temperatures when compared to Portland cement. In this study, the performance of pozzolan-based RMC concrete samples against sulphate attack was examined. Cube samples, after being removed from their moulds, were stored in a CO\({}_{2}\)-rich environment to gain compressive strength. Information obtained from XRD showed the formation of Mg carbonates in different forms. The use of fly ash and slag in large volumes reduces the environmental impact of concrete, but the use of these components have been found to greatly affect the formation of Mg carbonates in RMC mixes. This is mainly due to their filler effects. The coexistence of Ca- and Mg-based products was found in the slag-RMC mixes. The concrete samples based on RMC underwent mass and strength losses when stored in a MgSO\({}_{4}\) solution for up to 12 weeks. The removal of Mg from the microstructure of these samples was confirmed using SEM analysis. The use of the most widely used pozzolans at 50% by weight of the binder greatly affected the carbonation mechanism of the RMC mixes. This finding suggests that they should be limited in the design of Mg-based products that harden under CO\({}_{2}\)-rich conditions.  
reactive magnesia cement; fly ash; slag; carbonation; sulphate attack; microstructure Article
## 5 Discussion and Conclusions  
This article outlines the advantages and difficulties of applying blockchain technology and smart contracts to the health industry, specifically in medical records, and presents some examples of use cases. In addition to summarizing the operation of blockchain, the article also discusses its importance in the development of systems for the health sector in Mexico that comply with current regulations and takes a step forward in the coverage of the fundamental axes of medical records, such as those listed below:  
1. Privacy of information: Given the nature of the type of data--personal, diagnostic, and treatment--the actors will have the protection of the right to privacy regarding the records as well as the operations carried out on them and on the entities that execute them.
2. Immutability: Given that each block contains a previous block hash, any change in a medical record will result in different data in the block identification. In this way, a modification is identified, which avoids the alteration of the data.
3. Identity of the actors: Access to data must be performed only by authorized entities, and the system must be able to manage the different operations according to the type of user or node.
4. Data traceability: Data access must always be recorded. Writing, reading, and modifying data should be accessible to authorized actors on the blockchain without restrictions.  
Figure 3: Analysis of smart contract execution times.  
We demonstrated how the process of providing access rights may be conducted in a decentralized manner and without the involvement of a wholly untrusted third party by using a blockchain and smart contracts. To implement our suggested method, new medical knowledge will need to be updated on the blockchain, which calls for proper smart-contract lifecycle management.  
Conceptualization, A.I.M.A. and L.A.S.; methodology, J.L.G.A.; software, A.I.M.A.; validation, O.C.-M., J.L.G.A. and L.A.S.; formal analysis, L.A.S.; investigation, A.I.M.A.; resources, A.I.M.A.; data curation, A.I.M.A.; writing--original draft preparation, A.I.M.A. and L.A.S.; writing--review and editing, L.A.S.; visualization, J.L.G.A.; supervision, L.A.S.; project administration, A.I.M.A.; funding acquisition, L.A.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by CONACYT, grant number 1033480.  
Not applicable.  
Data Availability Statement: Data sharing does not apply to this article as no new data were created or analyzed in this study.  
We are grateful for the support of the Ph.D. program in Advanced Engineering Sciences (DOCIA) at the Universidad Autonoma de Ciudad Juarez.  
The authors declare that they have no competing interests. The study's design, data collection, analysis, or interpretation; the preparation of the paper; and the choice to publish the findings were all made independently of the funders.
# Covert Channel Communication as an Emerging Security Threat in 2.5D/3D Integrated Systems
## 5 Covert Channel Attacks in 2.5D/3D ICs
### Discussion on Mitigation Strategies  
In chiplet-based integration, design time techniques to mitigate side-channels and covert channels are limited since chiplets are typically obtained as a standalone IP, where only certain information is available, such as I/O characteristics, area, power, and performance. Thus, the applicable design-time techniques are related to the floorplan/placement of the chiplets. Specifically, to mitigate TCCs, the floorplan should reduce potential temperature gradients between the chiplets, particularly between secure and insecure chiplets. Reduced temperature gradients decrease the horizontal heat flow, thereby mitigating the efficacy of covert channel communication. Similarly, the placement and in-package design of the secure chiplet should favor vertical heat flow toward the heat sink. In-package structures for heat isolation can also be considered. For power covert channels, an important design-time countermeasure is obfuscating the power delivery network, so that the attacker cannot isolate the regulator of the chiplet, via semi-invasive approaches that target the interposer (where regulators are typically placed). This obfuscation would be helpful, but not sufficient, since the total power would still be correlated with the secure chiplet power profile. Runtime covert channel detection techniques described in Section 4.3 would be required.  
Figure 8: Attack model of a power covert channel between a secure chiplet and user/attacker.
## 2 Materials and Methods
### Feature-Mapping Motion Estimation Module  
Man-made objects often exhibit tiny sizes, small displacements, and low feature expressiveness in video satellite imagery. These characteristics result in a high correlation between the features of consecutive frames. The correlation volume, commonly used in stereo-matching [46; 47], depth estimation [48; 49], and optical flow estimation networks, can compute the matching similarity between two consecutive features, reflecting their mapping relationship. [PERSON], et al. [50] summarized different methods for constructing correlation volumes in depth-estimation research.  
When predicting displacements with an anchor-free model, we were particularly interested in the global mapping relationship between the center features of multiple objects in different frames. JDE models in [39; 40] learned relative displacement information by analyzing the corresponding identity features between two frames. FMME constructs an all-pairs pixel correlation [51] between the features of the current frame and the preceding one. It extracts the minimum-cost matching of correlated elements within object-central pixels of different features, thereby obtaining the mapping relationship between multiple object centers across features. Finally, the FMME model outputs the displacement prediction based on the constructed inter-frame mapping relationship. The overall architecture of the FMME model is illustrated in Figure 6.
# Infrared Image Superpixel Segmentation Based on Seed Strategy of Contour Encoding
## 4 Experiment
### Discussion  
From the experiments, it is evident that most algorithms could improve the boundary adherence capability with an increasing number of superpixels, which was achieved at the cost of over-segmentation. A better superpixel extraction algorithm should achieve high accuracy with fewer superpixels, in order to maintain compactness and reduce the difficulty of subsequent tasks. In the quantitative results, more superpixels contributed to boundary adherence and when a certain number was exceeded, the precision improvement was marginal. A large number of small superpixels is simply the result of over-segmentation on previous superpixels, which is invalid for boundary adherence. Therefore, adaptively selecting the appropriate size of superpixels based on the local image content is a more efficient way to improve accuracy. Our seed sampling strategy, based on contour encoding, is capable of placing fewer seed points in smooth regions and more seed points in complex texture regions, in order to generate more appropriate superpixels. In other words, our method can fix the effective superpixels early, without adding a large number of superpixels, such that SSCE can obtain optimal BR values. In addition, the contour constraint can limit the generation of superpixels within the contours, which reduces the leakage of pixels. Our method achieves higher initial segmentation accuracy with fewer superpixels and  
Figure 14: Refined results after merging similar initial superpixels. The initial superpixel maps of Figure 11f and the corresponding merged results are interlaced in (**a–f**).  
Figure 15: Ablation study on the impact of the spatial proximity weight and edge penalty term. (**a**) Result without any constraints; (**b**) result only with the spatial proximity weight; (**c**) result of SSCE.  
further reduces redundant superpixels by merging the initial superpixel map. Experiments on a self-built IR dataset, BSD500 and 3 Dircadb demonstrated the effectiveness of the proposed algorithm.
# When Intelligent Transportation Systems Sensing Meets Edge Computing: Vision and Challenges
## 2 Intelligent Transportation Systems (ITS)
### Traffic Prediction  
Traffic pattern analysis is fundamental to traffic prediction. In some cases, traffic prediction is critical for an ITS if decision-making requires information in advance. In traffic prediction, the models extract features and learn the current pattern of traffic in order to predict some measurements. Traffic prediction is crucial for intelligence. It is one of the areas in which artificial intelligence, especially deep learning techniques, have been heavily applied. Traffic prediction has covered many tasks in transportation systems. Examples are traffic flow prediction [26; 48; 49; 50; 51; 52; 53; 54], transit demand prediction [55; 56], taxi or ride-hailing demand prediction [57; 58; 59], bike sharing-related prediction [60; 61], parking occupancy prediction [62], pedestrian behavior prediction [63], and lane change behavior prediction [64]. LSTM, CNN, GAN, and graph neural networks are some of the most widely used deep learning methods for traffic prediction. The current trend of traffic prediction is larger scale, higher resolution, higher prediction accuracy, and real-time speed. For instance, [PERSON] et al. investigated the feasibility of applying LSTM for single-spot traffic flow data prediction [51]. Their work is a milestone in this field and has laid the foundation for sophisticated models that can capture network-wide features for large-scale traffic speed prediction [65; 66; 67].