esa-sceva/satcom-chunk-collection huggingface dataset:
# Triaxial Compression Performance Research of Steel Slag Concrete on the Unified Strength Theory
## 2 Materials and Experimental Procedures  
### Specim Production  
There are two types of coarse aggregates used in this true triaxial tests: Natural Coarse Aggregate (NCA) and Steel Slag Aggregate (SSA). NCA was natural gravel produced in Nanning, China. SSA was by-products from Guangxi Liuzhou Iron and Steel Group. As shown in Figure 1, NCA and SSA's diameter is in the range of 5-20 mm. Table 1 shows the material performance of the coarse aggregate. Steel slag concrete was made of P. O 42.5 Ordinary Portland cement, and the fine aggregate was the natural river sand with a fineness modulus of 2.5.  
Table 2 shows the mixing ratio of the mixture and the main parameters of SSC. The concrete mix ratio was designed according to the Specification for Mix Proportion Design of Ordinary Concrete [28]. Three kinds of SSA replacement ratios were designed: 30%, 70%, 100% by mass of NCA. SSC-30 means that the SSA with a replacement ratio of 30%.  
After mixing, the concrete was cast in a mold and then transported to a vibrating table for vibrating to remove air bubbles in the specimen. After 24 h cured in the room, the specimens were put into a standard curing room with a temperature of 20 \(\pm\) 2 \({}^{\circ}\)C and relative humidity of 95% for 28 days. Ten stress ratios (\(\sigma_{1}:\sigma_{2}:\sigma_{3}=0.00\):0.00:1, 0.00:1.00:1, 0.05:0.25:1, 0.05:0.5:1, 0.05:0.75:1, 0.05:1:1, 0.1:0.25:1, 0.1:0.5:1, 0.1:0.75:1, and 0.1:1:1) were designed for each mix of proportions. The test specimens in this test were cubes with a side length of 100 mm, and there were three directions of vertical stress on the surface of the specimens, as shown in Figure 2. Three specimens were tested for each mixture, so a total of 90 specimens were tested. The experimental results took the average of each data set after deleting the discrete ones in this study.
## 1 Introduction
Synthetic materials, like calcium phosphate ceramics, can be tailored for specific resorption rates and mechanical properties. Similarly, the choice of barrier membrane plays a crucial role in guided bone regeneration [21]. Resorbable membranes,typically made from collagen, offer ease of use and eliminate the need for a second surgery for removal [22]. Non-resorbable membranes may provide more rigid structural support but require additional procedures for retrieval [23]. The selection between resorbable and non-resorbable membranes should be based on the specific clinical scenario, considering factors like defect size and the need for space maintenance. Therefore, careful selection of appropriate graft materials and membranes, tailored to the patient's specific clinical needs, is essential for optimizing the success of dental implant procedures. In this study, xenograft was used along with collagen membrane for alveolar bone reconstruction. Although simultaneous reconstruction at the time of implant removal has been suggested, the long-term dimensional stability and comparative effectiveness of immediate versus delayed approaches remain unclear. A direct comparison regarding ridge width and height after implant removal between immediate and staged reconstruction strategies is warranted. This study aimed to evaluate whether immediate alveolar bone reconstruction at the time of implant removal provides comparable or superior dimensional stability of the alveolar ridge compared to staged reconstruction approaches. This study was conducted to determine whether or not immediate alveolar bone reconstruction can provide sufficient bone augmentation to support subsequent implant placement while maintaining dimensional stability and minimizing complications by analyzing cone-beam computed tomography (CBCT)-based morphological outcomes. The null hypothesis of this study is that there is no significant difference in alveolar bone dimensions between immediate and staged reconstructions following implant removal.
## 3 Results
### NPH vs. Multiple Conditions (AD vs. Healthy Controls)
#### 3.1.2 Segmentation and Feature Analysis  
In the study, developed an automated segmentation and connectivity analysis method for identifying brain regions relevant to NPH diagnosis [39]. Their comparative analysis of multiple algorithms revealed substantial performance differences: their proposed method achieved segmentation accuracies of 84% for ventricles, 87% for gray-white matter, and 35% for subarachnoid space, significantly outperforming traditional techniques. The notably poor performance for subarachnoid space segmentation (35% accuracy) across all methods highlights a persistent technical challenge in NPH imaging analysis, as this region is crucial for diagnosing disproportionately enlarged subarachnoid space hydrocephalus (DESH). The substantial performance gap between ventricle/gray-white matter segmentation (84-87%) and subarachnoid space segmentation (35%) indicates that future methodological development should focus specifically on improving this challenging aspect of NPH-related image analysis.  
Morphometric Parameter Evaluation  
In another study employed a RF algorithm to predict NPH and evaluate six commonly used morphometric parameters [41]. Their feature importance analysis revealed that frontal horn length alone achieved 95.0% accuracy, the highest among all individual parameters. Surprisingly, combining all six parameters decreased accuracy to 89.3%, demonstrating that more parameters do not necessarily improve performance. This counterintuitive finding challenges the common assumption that incorporating more features improves ML performance and highlights the risk of overfitting when using redundant or irrelevant parameters. Their results suggest that ML algorithms for NPH diagnosis should be trained on carefully selected informative features rather than broadly incorporating all available measurements. This principle of parsimonious feature selection could improve both model accuracy and generalizability.
## 4 Results  
### InSAR Areal Analysis  
The areal InSAR analysis was performed over the entire study area (60 km\({}^{2}\)) and covered the period from February 2016 to December 2019. Figure 5 reports an interferometric stack acquired in descending viewing geometry that spans the period 2018-2019. Almost the entire territory shows little or no movement, with displacement rates less than 20 mm/year (green pixels). Extensive decorrelated areas (gray/transparent pixels) are observed along the steep slopes exposed to the North.  
Only four slopes show consistent signs of movement during the period of our analysis (Figure 5). Three of them (Braina, Carbona and Spareda) correspond to the landslides that suffered catastrophic failures during November-December 2019; the fourth is represented by the Marano landslide, which was reactivated during March 2018. The detected interferometric signals can be interpreted as slow downslope displacements before the reactivation. In fact, interferograms related to the late 2019 reactivation events are excluded from the areal stacking (Figure 5). Therefore, the analysis refers to the years prior to the catastrophic failure.  
The analysis of the temporal evolution of deformation signals, which was developed by observing the monthly areal stacks, reveals that Braina, Carbona and Spareda were actively deforming during the entire analysis period, preceding their reactivation. The Marano landslide reactivated catastrophically in March 2018, and the deformation signal is less evident (i.e., lower average displacement rate). In this case, the deformation takes place mostly during the first months of 2018 [8], while in the post-failure phase, only small displacements are observed. According to the regional landslide database, no other landslides were exhibited high displacement rates in the study area during the observation period. Therefore, no landslides went undetected.  
These results show that the InSAR analysis can provide useful information on the landslide activity for most of the mountain territory (\(\sim\)85%) despite the scattered and sparse presence of infrastructures. The only areas without information are the decorrelated slopes characterized by dense vegetation on steep slopes exposed to the North.
# Routing Protocols Performance on 6 LoWPAN IoT Networks
## 2 Related Work
### Lightweight On-Demand Ad Hoc Distance Vector Routing Protocol--Next Generation (LOADng)  
LOADng protocol is a simplified version of AODV which focuses on 6 LoWPAN. Like AODV, LOADng builds routes solely on demand. Control messages obtained from AODV allow this route development, sometimes referred to as route discovery [25]. Since LOADng is a reactive protocol, its main method of operation is to use LOADng routers to generate Route REQuests (RREQs) to find paths that go to certain destinations. The protocol calls for receiving Route REplies (RREPs) from the destination after RREQs arrive successfully. Moreover, RREQ transfer happens at the hop-by-hop level via unicast communication [26].  
As an AODV variation, LOADng and AODV use a similar method for route building, maintenance, and discovery. However, it stands out due to its optimized flooding, adaptable packet format, ease of implementation, and removal of settings that are rarely useful [26]. The destination node is tasked with determining the optimal path, which it does after obtaining several copies of the initial RREQ message. A Route Cost (RC) is carried by every duplicate of the original RREQ message, which is determined as it travels towards the destination node [27].  
Routes are only created when there are data to be transmitted and a workable path to the destination exists, in line with reactive routing protocols. If traffic stays on the approved path, these pathways will be kept up. Even with its benefits, LOADng has drawbacks, like route discovery latency [8]. Unlike RPL, LOADng lacks a controlling node like a root, resulting in a single point of error. To mitigate control message overhead, LOADng employs a generalized message format [28].
# A Universal Privacy-Preserving Multi-Blockchain Aggregated Identity Scheme
## 1 Introduction
### Proposed Solution  
In our previous work [17], we proposed a public blockchain-enabled identity scheme based on Merkle tree and smart contract. However, when applying the solution to non-custodial wallets, we found batch identity proofs to be a necessary requirement, and the Merkle tree was inefficient for batch membership proofs. Besides, due to the large number of VASPs, it is difficult to ask all VASPs to cooperate with the investigation, and requiring VASPs to verify the users' attributes increases their cost. To address these issues, we propose a new multi-chain aggregated identity system. The scheme replaces Merkle trees with RSA accumulators to implement constant-size batch membership proofs. In addition, the solution sets up a third-party verifier to verify the user's identity witness, and provides an interface for querying the white list of verified wallet accounts, so that a large number of small VASPs do not need to repeatedly verify the user's identity witness. This not only saves costs but also facilitates the criminal investigation of the regulatory authorities. Finally, the scheme abandons the attribute proof in the previous scheme, instead, the allowed business scopes, such as participation in initial coin offering (ICO), are recorded in the identity NFT. Therefore, VASPs can decide which services are allowed to be provided to the user based on the identity NFT without checking the user's attributes. The costs of VASPs are saved and the privacy of the users is better protected.  
We will here provide a brief description of the new scheme. First, the identity provider checks the user's identity and minus an identity NFT for the user. Then, the user aggregates-wallet accounts of multiple blockchains into a cryptographic accumulator and associates the accumulator value with the identity NFT through the smart contract. Then the user can prove that these wallet accounts correspond to a verified identity. Therefore, the scheme supports the identity management of all public blockchains. The solution only needs to be deployed on one EVM-compatible blockchain instead of all blockchains, and the identity credentials are only on this blockchain. So, for other blockchains, the verification of identity is off-chain. Since only the accumulator value is stored on the blockchain, the identity provider only knows the user's real identity but not the user's wallet account, while VASPs only know the user's wallet account but not the user's identity. So this scheme protects the user's privacy. The identity provider and VASP know which identity NFT corresponds to the user identity and wallet account, respectively. Thus, regulators can trace the identity of suspicious accounts by correlating user identities held by identity providers with wallet accounts held by VASPs. Therefore, this scheme realizes the identity tracking of addresses on the chain without affecting the anonymity of the blockchain, which provides a new solution for the government to supervise the cryptocurrency.
# Feasibility of Developing Sustainable Concrete Using Environmentally Friendly Coarse Aggregate
## 4 Results and Discussion
### Overview of Long Term Durability  
The compressive strength of the combined RCA concrete achieved the design strength of 25 and 40 MPa, satisfying the minimum compressive strength stated in AS3600 standard for the design of concrete structures in exposure classes A1/A2 and B1/B2, respectively. This designates that RCA concrete is a possible alternative to quarried aggregate concretes used at present. However, the protection of reinforcing steel in concrete due to ingress of water and aggressive agents determines the long term durability and service life of reinforced concrete [73]. It is the exposure to carbonation or chloride ingress that can destroy the primary protective passive layer in steel and result in the reinforcement corrosion. Due to lower water absorption observed in combined RCA concretes the use of the RCA is expected to minimize ingress of these aggressive agents into the RCA concrete, and provide higher resistance to chloride and sulfate attack. This is further supported by low shrinkage observed in combined RCA concrete as that reduces shrinkage deformation and micro-cracking. Formation of shrinkage cracking can lead to an increase of permeability to water and aggressive agents and deteriorate concrete through freeze-thaw in cold regions. Overall, the low water absorption coupled with low drying shrinkage in RCA concrete is expected to resist chlorides and sulfate ion attack. However, to determine this more clearly, long term corrosion and sulfate testing of RCA concrete is required.  
Figure 7: Microstructure development of Basalt aggregate and combined RCA concrete.
## 4 Formal Model
The model is expressed in the following way:  
Amount of activations:  
\[Max\quad\sum_{f\in F_{K}\in I_{f}}x_{fk} \tag{2}\]  
Amount of flows:  
\[Max\quad\sum_{f\in F}w_{f} \tag{3}\]  
subject to:  
If the activation \(k\) of flow \(f\) is transmitted, it is done in \(t_{f}\) of its valid slots:  
\[\sum_{h\in H_{f}^{k}}\sum_{(\sigma_{f},j)\in L}z_{(\sigma_{f},j)hf}=t_{f}x_{fk} \forall f\in F,\ \forall k\in\ I_{f} \tag{4}\]  
For a flow to be scheduled, all its activations must be scheduled:  
\[\sum_{k\in I_{f}}x_{fk}=|I_{f}|w_{f} \forall f\in\ F \tag{5}\]  
In each slot \(h\), there is at most one path for flow \(f\):  
\[\sum_{(\sigma_{f},j)\in L}z_{(\sigma_{f},j)hf}\leq 1 \forall f\in F,\ 1\leq h\leq\ H \tag{6}\]  
Path continuity of flow \(f\) in slot \(h\):  
\[\sum_{(i,j)\in L}z_{(i,j)hf}=\sum_{(j,i)\in L}z_{(j,i)hf} \forall f\in F,\ 1\leq h\leq H,\ \forall i\in V,\ i\
eq\sigma_{f},d_{f} \tag{7}\]  
Exclusivity of link \((i,j)\) in slot \(h\):  
\[\sum_{f\in F}z_{(i,j)hf}\leq 1 \leq h\leq H,\ \forall(i,j)\in\ L \tag{8}\] \[x_{fk}\in\{0,1\} \forall f\in F,\ \forall k\in\ I_{f}\] (9) Figure 2:0 (10) Figure 2:1 (11) Figure 2:2  
Once the maximum amount of activations or flows is computed, the second objective is to schedule with the minimum energy demand. For this, the variables \(x^{\prime}s\) corresponding \begin{table}
\begin{tabular}{c c} \hline \hline  & **Decision Variables** \\ \hline \(x_{fk}\) & 1 if the activation \(k\) of flow \(f\) is transmitted; 0 otherwise \\ \hline \(w_{f}\) & 1 if every activation of flow \(f\) is transmitted; 0 otherwise \\ \hline \(z_{(i,j)hf}\) & 1 if the flow \(f\) uses link \((i,j)\) in the slot \(h\); 0 otherwise \\ \hline \(y_{ih}\) & 1 if server or switch \(i\) is active in slot \(h\); 0 otherwise \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Variables of the ILP model. to the flows sent, or variables \(w^{\prime}s\) corresponding to the activations sent, are set to value 1, and the objective function of the previous model is modified to:  
\[\min\sum_{i\in V}\sum_{h=1}^{H}y_{ih} \tag{13}\]  
In addition, constraints relative to the activation of the switch \(i\) in the slot \(h\) if it is being used in the path of some flow in that slot are added:  
\[\sum_{(i,j)\in L}\sum_{f\in F}z_{(i,j)hf}+\sum_{(j,i)\in L}\sum_{f\in F}z_{(j, i)hf}\leq\delta_{i}y_{ih}\qquad\forall i\in V,\;1\leq h\leq H \tag{14}\]  
where \(\delta_{i}=|(i,j)\in L|+|(j,i)\in L|\). Note that this formulation does not force the selected paths to be simple (they do not repeat switches) nor does it prevent the \(z\) variables from forming isolated cycles (without connection to the source and destination servers of the flow). However, if any of these situations occurs, the corresponding variables can simply be ignored.
## 3 Materials and Methods
### Human Error Analysis Process
#### 3.1.4 Step 4: Quantification of Human Error  
The quantitative analysis in this step was performed based on HEART (human error assessment and reduction technique). HEART contains three main elements, which are the following:  
* Generic task type (GTT): the analyst should specify which of the eight generic task types proposed in HEART best match the task object of analysis and determines the nominal HEP for the task based on the mean HEP value associated with the corresponding GTT.
* Error producing condition (EPC): these are conditions that may influence the human reliability, mathematically they represent modification weights for the nominal HEP. HEART proposes a table with a list of 40 EPCs and their relative maximum effect on performance in the form of a numerical value (EPCs are also known as performance shaping factors or PSFs).
* Calculation method: the method for calculation of human error probability evaluates the EPCs weights based on their relative importance to each other in the task context. In this manner, an assessed proportion of the error producing conditions (APOA) is obtained. The final HEP can be yielded, as shown in Equations (1) and (2): \[HEP=GTT_{i}\ \times\ \prod WF_{j}\] (1) \[WF_{j}=\big{[}\big{(}EPC_{j}-1\big{)}\times APOA_{j}+1\big{]}\] (2) where \(WF_{j}\) = weighting effect for the \(j\)th _EPC_; \(EPC_{j}\) = error-producing condition for the \(j\)th condition; \(APOA_{j}\) = assessed proportion of 'affect' for that condition; \(HEP\) = final human error probability; \(GTT_{i}\) = central value of the \(HEP\) task (distribution) that is associated with a task \(i\); \(GTT\) = generic task type.
# Entity Embeddings in Remote Sensing: Application to Deformation Monitoring for Infrastructure
## 3 Methods
### Entity Embedding  
The novel use of entity embeddings captures both the spatial and temporal components of the InSAR measurements. A sequence of random numbers was assigned to each category of the InSAR metadata, also known as latent factors. The randomly initialised latent factors were then modified by the DL workflow for the prediction of deformation. Therefore, the latent factors capture the relationships between different categories. The word \"embedding\" refers to the computational shortcut that links the particular entity in the InSAR metadata to its corresponding sequence of numbers.  
Based on preliminary experiments, the best performance was obtained by treating all the InSAR metadata as categorical and only one variable, \"elapsed\", as continuous. Therefore, embeddings will not be created for the continuous variable \"elapsed\". The  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Random Forest** & \\ \hline n\_estimators & 40 \\ max\_samples & 200,000 \\ min\_samples\_leaf & 5 \\ max\_features & 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Random Forest hyper-parameter setup.  
Figure 3: The importance of each InSAR metadata feature in predicting deformation, as determined by the Random Forest model.  
entities chosen as categorical variables from the InSAR metadata are in Table 2. The cardinality is the number of unique categories representing each entity, for example the cardinality of \"Month\" is 12. The embedding size is the number of values (i.e., vectors) representing each of the entities, so that entities with higher cardinality are captured by larger embeddings to help capture the subtle non-linearities. The experiments on the embedding size have been conducted within various deep learning frameworks, which is based on some fraction of the cardinality. For example, Google/TensorFlow have adopted an embedding size of \(\gamma^{0.25}\), where \(\gamma\) is the cardinality [45]. In this study, the embedding size proposed in FastAI/[31] was adopted, where the embedding size equals \(1.6\gamma^{0.52}\).