esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Dyversify Architecture
### Event/Anomaly Detection  
A wide range of analysis techniques exists, as discussed in Section 3. The choice of technique often depends on the use case, efforts to implement, and characteristics of the technique such as throughput or data requirements. Techniques can also be combined, either as single service using boosting or bagging approaches or as multiple, independent services to further improve performance [56]. Some common requirements of techniques include availability of historic data, availability of labeled data, and scalability.  
In Dyversify, we investigated how to leverage the Healthbox data to detect certain user events as well as anomalous system behavior. Three teams, each with different backgrounds and a focus on different techniques, implemented their techniques as separate microservices. This resulted in four different microservices, as shown in Figure 1. We discuss each in detail below.  
Each of the detector instances reads measurements from the (semantic) measurement Kafka topic and writes any detected anomaly/event to the (semantic) event Kafka topic. Components that require historic data query Obelisk for measurements or the Stardog database for anomalies and events. Each detected anomaly/event is described with an event identifier, the originating Healthcare, the metric(s), the time range of the event, the timestamp of the detection, a description, and an identifier of the detector.  
Listing 3 shows a JSON event as generated by the MP-events component. The _id_ field contains an identifier for the event. We opted to have each component create HTTP ids so as to simplify the semantic conversion and for easier differentiation of the events when debugging. The _update_ field indicates whether the event is an updated version of a previously published event and is used to update the records in Stardog. The _generatedBy_ field provides provenance information. A versioning scheme for the component was foreseen but did not see real use in Dyversify. The _timestamp_ indicates the detection time, and the _anomaly_ field gives all information regarding the event, including the occurrence time of the event (in epoch time). Note that the timestamp and timing of the event may differ when older data is being processed, which might occur when a processor has been offline for a while. Finally, the _matches_ field is specific to the MP-events component; it links the detection to the labeled
The same information is present in the semantic format shown in Listing A1. The JSON-LD version is more verbose due to constraints imposed by the used ontology and the need to include a JSON-LD context, which links the JSON keys to semantic concepts.  
Because detector deployments may also crash or change due to rescaling, they use Kafka to persist their progress periodically. To avoid issues with reporting a single event multiple times when measurements are re-processed, we generate event identifiers based on the timestamp of the measurements. This way, duplicate detections will have the same identifier and require no special treatment.
# BDS-3 Integrity Risk Modeling and Probability Evaluation
## 3 BDS-3 Integrity Failure Model and Effect Analysis Algorithm
### BDS-3 Integrity Risk Factors Analysis  
There are many abnormal factors that affect GNSS integrity, which can usually be divided into hard and soft failures of space segments and ground control segments.  
In the early analysis of GPS integrity risk factors, considering different conditions of different users, the following factors are proposed: signal distortion (harmful waveform), excessively low signal power, separation of code and carrier, ephemeris error, step slope or acceleration error caused by signal anomalies, and response time of the ground control section to anomalies.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{4}{c}{**Aberration Identification**} \\ \multicolumn{4}{c}{**Aberration Name: Name Assigned to Aberration**} \\ \hline \multirow{2}{*}{**Description:**} & **Segment:** & **Cause:** \\  & Allocation to: & What most directly \\  & Ground Control System & causes the aberration? \\  & Space segment & \\ \hline \multicolumn{4}{c}{**Aberration Characterization**} \\ \hline \multicolumn{4}{c}{**Effect on signal:**} & **Magnitude:** \\  & Off, nil, ramp, step, noise, sinusoid & M, m/sec, m/sec squared \\ \hline \multicolumn{4}{c}{**Detection Means:**} & **Undetected duration:** \\  & Use data from measurements & Time until aberration is detected, and user \\  & notified \\ \hline \multicolumn{4}{c}{**Aberration Occurrence**} \\ \hline \multicolumn{4}{c}{**Probability of occurrence:**} & **Undetected Probability:** \\  & Per hour, per day, per year, per SV, per constellation & Per hour, per day, per year, per SV, per constellation \\ \hline \multicolumn{4}{c}{**Number of Aberration observed:**} & **Total observation Time (h):** \\  & Number of relevant aberrations observed over & Total observation time used to calculate the \\  & time & measurement onset probabilities \\ \hline \multicolumn{4}{c}{Recommendations} \\ \hline \end{tabular}
\end{table}
Table 2: Anomaly characteristic statistical table.  
Figure 2: IFMEA Process.  
Based on the analysis experience of GPS integrity risk factors, combined with the design and test results of BDS-3, the BDS-3 integrity risk factors are summarized and analyzed.
## 4 Discussion
### Fields of Application of CytoSorb(r)  
CytoSorb(r) is indicated for the removal of various molecules from whole blood, enabling the treatment of critical conditions. Its applications include sepsis and septic shock through the removal of inflammatory cytokines; hepatic failure by eliminating bilirubin and ammonia; rhabdomylysis by removing myoglobin; and the rapid elimination of anticoagulant drugs like ticagrelor and rivaroxaban. Sepsis and septic shock: sepsis is a leading cause of mortality in intensive care units, with septic shock pushing mortality rates above 80%. The \"cytokine cascade\" involves the uncontrolled release of pro- and anti-inflammatory cytokines, worsening the patient's condition. CytoSorb(r) is a valuable adjunctive therapy in sepsis, dramatically reducing inflammatory cytokines, stabilizing hemodynamics, decreasing the need for inotropic drugs, and reducing hospitalization time [30,31]. Recent studies emphasize the importance of early intervention, within 8-10 h of diagnosis. Other applications with severity: Beyond sepsis, CytoSorb(r) is used in conditions characterized by cytokine storms, such as cardiac surgery, ECMO, severe burns, polytrauma, acute pancreatitis, severe influenza, meningitis [32]. Furthermore, CytoSorb(r) improves liver function by removing bilirubin, bile acids, and ammonia--toxins that accumulate in the blood during hepatic insufficiency. It can support patients awaiting transplants or promote spontaneous liver regeneration. Compared to conventional dialysis, CytoSorb(r) provides significantly more effective and immediate detoxification, simplifying its application within CRRT circuits and reducing complications. A different application is in the case of rhabdomyolysis: This condition leads to the massive release of myoglobin, which can result in acute kidney injury [33]. Traditional therapies are only partially effective, while CytoSorb has been proven to rapidly remove myoglobin, preventing renal damage, and speeding up renal function recovery [33]. It can be used as a preventive treatment or in conjunction with CRRT when renal failure occurs and liver dysfunction or organ recovery in donation after circulatory death [34; 35; 36]. Cardiac Surgery and removal of anticoagulant drugs: CytoSorb(r) is highly effective at quickly removing Ticagrelor and Rivaroxaban, making emergency surgeries safer by reducing hemorrhagic risks. Studies have shown that the device removes approximately 99% of the drug in just two hours, significantly lowering intra- and postoperative complications. This efficiency has led to its inclusion in ESAIC guidelines. Additionally, CytoSorb(r) helps manage the inflammatory response in cardiac surgery and ECMO, reducing vasoplegic shock, drug usage, and mortality.
## 2 Methodology
### Bayesian Network  
A BN is a probabilistic graphical model that represents a set of random variables and their conditional dependencies [40]. A BN consists of directed acyclic graph (DAG) where each node denotes a random variable, each edge denotes direct influence of one variable over another, and each variable is independent of its non-descendants given its parents. Further, each node has a conditional probability distribution represented in Equation (5).  
\[P(X_{1},\cdots,X_{n})=\prod_{i=1}^{n}P(X_{i}\ \big{|}\ Parents(X_{i})) \tag{5}\]  
A BN provides a compact representation of a joint probability distribution, capturing independence and conditional independence if they exist (see also Appendix A for details on BN equations). Further, it encodes the relevant portion of the full joint probability among variables where dependencies exist. The values in the CPT and their corresponding influence on the subsequent nodes are calculated based on expert judgment; higher values of the probability of a HOE are assumed to reduce the reliability of the technical systems. Furthermore, each combination of the states of a HOE variable is associated with a value for conditional probability. Through [PERSON] theorem, the model allows calculations of the reliability of a technical system as a function of HOEs. HOE variables such as emergency plans, training, compliance with instructions and standards, maintenance, risk assessment, safety organization, and checking of rules are all analyzed for their impact on the reliability of a given technical system (e.g., sprinkler or detection system). The major outcome is the risk level for building design while alternative solutions are further investigated based on HOE risk-based parameters. The T-H-O-Risk methodology compensates for the weaknesses in HOE-related risks overlooked in a typical PRA. An example of BN for a building design can be found in our earlier study [17]. The BN structure has been developed by calculating the importance measures of basic events in the event/fault tree approach and multi-state BBN variables. The HOE basic events that contribute significantly to the occurrence of fire accidents have been identified from statistical data and presented in Table 1 below.
## 5 Discussion
### Formation of Laumontite  
Previous studies have demonstrated that vein-filling mineralogy is identical throughout the study area [22]. These investigations also ascertain many details regarding the formation of the early phases of the mineral sequence. Based on the reconstructed formation temperatures, the early chlorite/kaolinite, calcite1, quartz and calcite2 records represent the subsequent steps of exhumann of the metamorphic massif to the surface. Plant fragments enclosed in calcite2 crystals represent a middle Miocene terrestrial flora, indicating that by this time, the metamorphic rocks had reached the surface and probably formed islands in the Pannonian Sea [23], Figure 3). This scenario is further confirmed by the Badenian age of the conglomerate covering the uplifted metamorphic dome [10].  
The results of experimental petrology indicate that the minimal stability temperature of laumontite is above \(\sim\)125 \({}^{\circ}\)C. As this zeolite phase follows calcite2 in the vein-filling sequence, it has to crystallise during or following the subsidence phase of the basin's evolution. The facts that the veins lack all other zeolite minerals and that the composition of laumontite is rather stable along the well also indicate identical formation conditions throughout the rock body. The reasons for the slight variation in the chemical composition of laumontite are not known in detail. [PERSON] et al. [53] found that while the Ca-Na exchange is an endothermic reaction, the Ca-K exchange is exothermic. Thus, the coeval Ca \(\approx\) (Na + K) cation exchange (Figure 6) is probably caused by the changing composition of the fluid that occurs during the formation. A similar fluid-dependent mineral composition was identified in the case of other zeolites, such as in the heulandite characterised by [PERSON] et al. [54].  
In the present case, laumontite could have formed as a product of the reaction of the previous vein-filling phases according to the reaction _calcite + kaolinite + quartz + H\({}_{2}\)O = laumontite + CO\({}_{2}\)_, as shown above. Based on the classification scheme developed by [PERSON] [55; 56] and [PERSON] [57], this reaction is a mixed H\({}_{2}\)O-CO\({}_{2}\) type, where the two fluid components occur on opposite sides of the chemical equation [58]. In agreement with the THERIAK-DOMINO model shown above (Figure 7), extremely low \(X_{CO_{2}}\) levels are required to stabilise laumontite in such a system. As the reaction produces CO\({}_{2}\) continuously, its concentration can be sufficiently low only if the deliberated gas can migrate away, i.e., in the case of an open fracture system.  
As presented above, the amount of laumontite is found to increase significantly around the two highly fractured intervals, while closed veins, far from the communicating brecciated zones, are missing zeolite infill. Thus, based on the formation requirements, laumontite with extremely low \(X_{CO2}\) levels is an excellent indicator of communicating fracture systems, such as in the two modelled depth zones in the A-1 well.
# Steel Surface Defect Detection Based on YOLOv8-TLC
## 3 YOLOv8-TLC Algorithm
### Building the TSP Module  
#### 3.3.1 Triplet Attention  
To better capture the semantic information of defects on the surface of steel and to balance detection accuracy with computational cost, we adopted the Triplet Attention (TA) mechanism proposed by [PERSON] et al. [36] to extract more semantic information about defect features. The TA mechanism identifies important regions in the image through spatial attention, emphasizes specific features with channel attention, and focuses on information at different levels with depth attention. This allows the model to have a more comprehensive understanding of the image and to establish dependencies across different dimensions through rotation and residual operations, thereby more accurately identifying key features and enhancing the detection capability of steel surface defects. Additionally, the TA mechanism has a smaller number of parameters and supports cross-dimensional interaction, significantly improving training and inference speed. The implementation process of the TA attention mechanism can be represented by the following formula [37]:  
\[y_{1}=\hat{\chi_{1}}\sigma(\Psi_{1}(\hat{\chi_{1}^{*}})) \tag{9}\]  
\[y_{2}=\hat{\chi_{2}}\sigma(\Psi_{2}(\hat{\chi_{2}^{*}})) \tag{10}\]  
\[y_{3}=\chi\sigma(\Psi_{3}(\hat{\chi_{3}})) \tag{11}\]  
\[\overline{y}=\frac{1}{3}(y_{1}+y_{2}+y_{3}) \tag{12}\]  
In this process, \(\sigma\) represents the activation function, while \(\Psi_{1}\), \(\Psi_{2}\) and \(\Psi_{3}\) denote the convolution operations in the three branches. The input feature map \(\chi\) undergoes rotation operations in the top and middle branches, resulting in feature maps \(\hat{\chi_{1}}\) and \(\hat{\chi_{2}}\). These are then reduced in dimensionality through Z-Pool, yielding feature maps \(\hat{\chi_{1}^{*}}\) and \(\hat{\chi_{2}^{*}}\). In the bottom branch, the input feature map \(\chi\) is also reduced in dimensionality via Z-Pool, producing feature map \(\hat{\chi_{3}}\). Finally, the overall attention weight \(\overline{y}\) is obtained by averaging the attention weights \(y_{1}\) from the top branch, \(y_{2}\) from the middle branch, and \(y_{3}\) from the bottom branch.
# Proactive Service Caching in a MEC System by Using Spatio-Temporal Correlation among MEC Servers
## 2 Related Works
### Caching in a MEC System
Thus, there is a high possibility that learning will be delayed due to the increased load on the access network. Our method differs from these methods mainly in two ways. The popularity of a service within a MECS is determined by the set of users within its service region and the service preference of the users, both of which change over time due to users mobility and changes in their service request patterns. Therefore, during a time interval, the popularity of a service within a single MECS is influenced not only by the users that it serves but also by the users moving from its neighboring MECSs. However, previous methods consider only the temporal correlation among the service popularity within a single MECS when they predict the popularity of services. On the contrary, we consider not only the temporal correlation in a single MECS but also the spatial correlation among MECSs to predict the service popularity distribution in a MEC system. In other words, we comprehensively consider the service popularity distribution over a MEC system and predict the popularity of each service in each MECS at the same time. In terms of implementation, our method reduces the amount of storage required to predict the service popularity. Since previous methods independently predict the service popularity in each MECS, the number of predictors required in a MEC system increases as the number of services and the number of MECSs increases. However, since our method simultaneously predicts the future popularity of all services in all MECSs by utilizing the spatio-temporal correlation structure, it needs only one predictor. We summarize the related works in Table 2.
# Tracking Mobile Sinks via Analysis of Movement Angle Changes in WSNs
## 3 Details of Damst  
### Network Model  
A network with lots of sensor nodes and several mobile sinks scattered uniformly in an area is considered. The sensor nodes communicate with each other via radio links. The whole network is assumed to be fully connected. The mobiles sinks have unlimited energy, large storage space and powerful processing capability to gather data from sensor nodes of interest. The mobile sinks are capable of obtaining real-time location information via their own GPS devices. However, the sensor nodes are not necessarily supported by the GPS function. While moving, the mobile sinks appoint some sensor nodes as RAs and some other sensor nodes on or near their trajectories as footprints. Each node in the network has a unique ID which is used as the routing address. For a clear description without loss of generality, the scenario where there is only one mobile sink and one RA is mainly discussed in the following elaboration.
## 4 Conclusions  
In this study, a polymeric matrix embedded with MWCNTs has been proposed for the first time and successfully applied as a new phase for the TFME of organic pollutants. The inherent chemical characteristics of the MWCNTs made a significant contribution to the extraction process, as demonstrated by a cross-study between membrane composition and extraction properties. As a result, the presence of 10% (\(w\)/\(w\)) of MWCNTs in the film composition ensures an appropriate phase for the adsorption of CPS, TCS and AHTN. This makes the whole TFME procedure more environmentally friendly, avoiding the use of plasticizers. Additionally, no conditioning or derivatization steps are required in the proposed methodology, minimizing waste generation, and enabling high sample throughput. It is also important to note that the proposed membrane led to a rapid TFME (1 h for the extraction and 15 min for the elution). Moreover, the microextraction method developed here provides limits of detection of 1 ug L\({}^{-1}\) for CPS and AHTN and 10 ug L\({}^{-1}\) for TCS. Repeatability was good, with RSD \(\leq\) 20% when using pieces from a single film. The application at two different spiked river water samples has shown a certain matrix effect, particularly in the case of TCS.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/polym15020314/s1](https://www.mdpi.com/article/10.3390/polym15020314/s1)]([https://www.mdpi.com/article/10.3390/polym15020314/s1](https://www.mdpi.com/article/10.3390/polym15020314/s1)), Figure S1. XRD spectrum of MWCNTs from Sigma Aldrich.  
Conceptualization, E.A.; Data curation, E.A., F.M., A.P. and C.F.; Formal analysis, E.A., F.M., A.P. and C.F.; Investigation, E.A., C.P. and I.Q.; Methodology, E.A., C.P. and I.Q.; Resources, E.A. and C.F.; Supervision, E.A. and C.F.; Writing--original draft, E.A., C.P. and I.Q.; Writing--review & editing, E.A., I.Q., F.M., A.P. and C.F. All authors have read and agreed to the published version of the manuscript. Funding has been received from the Ministerio de Ciencia, Innovacion, y Universidades (MCIU), PID2019-107033 GB-C22/AEI/10.13039/501100011033.  
Figure 9: TFME response of F1 for different water matrices (\(n\) = 3). The samples were spiked at 200 ug L\({}^{-1}\) for CPS and AHTN, and at 2000 ug L\({}^{-1}\) for TCS.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **AR (\%)** & \\ \hline  & Osor River & Lélémena River \\ \hline
**CPS** & 91 (13) & 65 (11) \\
**TCS** & 70 (7) & 51 (10) \\
**AHTN** & 100 (11) & 59 (11) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Absolute recoveries for spiked Osor River and Lémena River water samples (\(n\) = 3). Standard deviation is shown in brackets.
# Artificial Intelligence Enabled Routing in Software Defined Networking
## 3 Proposed AIER Mechanism
### AIER Mechanism
#### 3.2.3 Application of the ANN Model  
Because there are 3 source-to-destination pairs and 3 available paths, there exist 3 possible path configuration outcomes. The ANN model trained in the preceding stage is employed for path configuration in the controller, and the congestion probability, which is denoted by \(C_{k}\) for each path configuration \(k\), is calculated, as summarized in Table 3. Assuming that the current path configuration is {0, 0, 0}, if any congestion probabilities lower than the current path configuration by a predefined threshold (denoted as _Th_), for example, 20%, exist, the controller will replace the current path configuration with one with the smallest congestion possibility. For instance, the new path configuration will be {2, 2, 1} in Table 3. The controller is responsible for forwarding the new path configuration through the southbound interface to the OpenFlow switches in the data plane. The predefined threshold _Th_ can avoid the so called ping-pong effect. Moreover, the AIER mechanism can periodically monitor the queuing length of each OpenFlow switch for the current path configuration to avoid potentially inaccurate output in the trained ANN model. If any queuing length of an OpenFlow switch is greater than 80%, then path reconfiguration is triggered.