esa-sceva/satcom-chunk-collection huggingface dataset:
# Discrimination of Earthquake-Induced Building Destruction from Space Using a Pretrained CNN Model
## 4 Experimental Results and Discussion
### Performance of Pretrained VGGNet for the Detection of Collapsed Buildings  
The classification results of collapsed buildings by means of the pretrained VGGNet model with fine-tuning all layers were shown in Table 2 and Figure 6. The overall accuracy is 88.83%, which is better than the results achieved in [34]. It can be seen that the applied model performed satisfactorily for identifying collapsed and non-collapsed buildings with relatively high producer accuracy (PA) and user accuracy (UA) values, which could be utilized as a guidance for the monitoring of disaster conditions and the responding emergency rescue. The non-collapsed buildings were better classified than collapsed buildings, which is partly caused by the effect of imbalanced data in which there were more non-collapsed buildings than collapsed ones. In addition, there were many steel or wooden frame buildings with metal sheet roofs, which had no visible deformation or textural change of the metal sheet roofs on the overhead satellite imagery when the buildings had been collapsed.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Confusion Matrix**} & \multicolumn{3}{c}{**Ground Truth**} \\ \cline{2-4}  & **Building Damage Grade** & **Collapsed Non-Collapsed** & **UA (\%)** \\ \hline \multirow{2}{*}{Predicted} & Collapsed & 208 & 47 & 81.57 \\  & Non-collapsed & 33 & 428 & 92.84 \\ \cline{1-1}  & PA (\%) & 86.31 & 90.11 & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of fine-tuned pretrained VGGNet model with dataset augmentation.  
Figure 5: Fine-tuning of different layers. \({}^{1}\) Overall accuracy; \({}^{2}\) Fully-connected layer.
# A Knowledge Graph Framework to Support Life Cycle Assessment for Sustainable Decision-Making
## 3 Methodology
### KG Quality Management  
The purpose of the proposed KG is to act as a knowledge support system during the engineering phase, with a particular focus on providing early access to the LCA data. This fit-for-purpose design ensures that the KG serves as a critical tool for engineers, facilitating informed decision-making by seamlessly integrating up-to-date LCI data into the product development process. To address the challenges associated with this application while preserving simplicity, as quality control should not be more complex than the construction and application of the graph, we propose two distinct implementations to support the fitness-for-purpose quality of KGs in our methodology.  
Initially, to verify the completeness and basic accuracy of the model, the following Cypher queries will be utilized. To detect nodes with identical labels and properties, we executed the query depicted in Listing 1.  
```
MATCH(n1),(n2) WHEREid(n1)<id(n2)ANDlabels(n1)=labels(n2)ANDproperties(n1) =properties(n2) RETURNn1,n2
```  
Listing 1: Cypher Query for Identifying Duplicate Nodes.  
This query identifies all pairs of nodes with the same labels and properties. Duplicate nodes might indicate redundancies in the graph that should be reviewed for potential merging or removal. To find relationships between the same pairs of nodes with identical types and properties, we executed the query depicted in Listing 2.  
```
MATCH(n1)-[r1]->(n2),(n1)-[r2]->(n2) WHEREid(r1)<id(r2)ANDtype(r1)=type(r2)ANDproperties(r1)=properties(r2) RETURNr1,r2
```  
Listing 2: Cypher Query for Identifying Duplicate Relationships.  
```
MATCH(n1)-[r1]->(n2),(n1)-[r2]->(n2) WHEREid(r1)<id(r2)ANDtype(r1)=type(r2)ANDproperties(r1)=properties(r2) RETURNr1,r2
```  
Listing 3: Cypher Query for Finding Isolated Nodes.  
```
MATCH(n) WHERENOT(n)-() RETURNn
```  
Isolated nodes may indicate incomplete data or modeling issues that require further investigation. To ensure that all nodes have the required properties, we executed the query depicted in Listing 4.  
```
MATCH(n) WHERE NOT EXISTS(n.requiredProperty) RETURN n
```  
This query identifies nodes missing specific properties, ensuring consistency in node attributes.  
Second, regular updates of LCI data are essential to maintain the temporal integrity of the KG. This can be achieved by implementing update pipelines that periodically retrieve and integrate the latest LCI data from authoritative sources. Typically, LCI data need to be updated annually, as the information is not very granular, and only yearly averages of energy consumption are calculated for regions and various production processes [45]. Once the existing LCI information is updated with these new data, the augmented LCI integration should be repeated. This repetition is necessary because the LLM predictions can change significantly when they are provided with an updated context from the latest LCI data.
# _CoVSCode_: A Novel Real-Time Collaborative Programming Environment for Lightweight IDE
## 7 Discussion and Comparisons
_Live Share_ supports programmers to collaborate in the host-participator pattern only, with several problems and limitations (as presented in Section 2.3), whereas _CoVSCode_ supports unconstrained and flexible real-time collaboration patterns--programmers may work in the host-participator pattern (e.g., in pair programming sessions), and they may also work with precisely equal roles (i.e., everyone is the host) for meeting diverse collaboration needs (e.g., in agile software development processes, in collaborative learning, and in collaborative troubleshooting). For any software project maintained in the _CoVSCode_ environment, each collaborating programmer (with permission on the project) can flexibly work on the project at any preferred time. Whenever a programmer starts to work in a day, the latest source code copy can be fetched, regardless of other collaborators' statuses. 2. With _Live Share_, only the host has the complete source code, and a source code file will be transmitted from the host to a participator only when the participator opens that file, the design of which affects the user experience (i.e., whenever the participator opens a file, it takes a while to show). Since every site has the complete source code with _CoVSCode_, whenever a programmer opens a file, it is immediately displayed and ready for editing, the same exact experience as in traditional single-user programming. 3. With _Live Share_, the host must be online at all times, and whenever the host loses the network connection for any reason, the whole session will be terminated, with all ongoing works at the participators' sites lost. With _CoVSCode_, every site has the complete source code, and each editing operation is saved at the server before further propagation. Therefore, there is no single point of failure in the session--whenever a client temporarily loses its connection with the server, no other collaborator is affected. 4. With _Live Share_, a participator cannot use IDE features that rely on cross-file code analysis (such as the \"go to definition\" feature) since they are restricted by the incomplete and memory-residing source code files, and no independent compilation, testing or debugging can be conducted. In contrast, with _CoVSCode_, each programmer has the complete source code copy on the local disk (which is exactly the same as in single-user programming), and thus enjoys all existing IDE functionalities and features such as language support, compilation and debugging. 5. The _Live Share_ environment relies on Internet-based services hosted by Microsoft, which may not be suitable for teams with high security concerns. In contrast, the _CoVSCode_ server can be simply hosted in a private cloud or inside an organization without Internet access, which is more suitable for meeting security requirements.
# Molecular Mechanisms of Topography Sensing by Osteoblasts: An Update
## 3 Molecular Mechanisms of Transduction of the Topographical Information
Consistently, osteogenic rough surfaces also increase the expression of the Wnt ligand Wnt5a in MSC together with the expression of the integrins \(\alpha\)1, \(\alpha\)2 and \(\alpha\)V [29]. Importantly, the exogenous addition of Wnt5a and Wnt5a knock-down experiments demonstrated that the integrin upregulation was Wnt-dependent [29] and suggested a mutually reinforcing crosstalk between integrin adhesions and Wnt signaling. The aforementioned pathways (ERK, BMP, Wnt) have in common their strong ties with the remodeling of integrin-mediated adhesions in contact with topography. This suggests that the transduction of the topographical information starts at the cell-substrate interface and might come down to the question of the number, distribution and composition of the adhesion sites. However, topography can be sensed by the cell as a whole owing to the morphological deformation it imposes. MSCs growing on micrometric grooves and ridges tend to align and elongate in the direction of the grooves. This leads to a stretching and orientation of the nuclei [69]. Some studies using ordered nanodots or micrometric grooves and ridges show that nuclear deformation was associated with the reorganization of some chromosomic territories and alteration in the gene expression from the affected chromosomes [94; 95]. Interestingly, MSCs growing on surfaces with cell-scale convex or concave curvature present morphological modifications of the nuclei, where convexity leads to a flattening and bending of nuclei [96; 97]. This is accompanied with an increase in the level of lamin in the nuclear envelope and an enhanced osteogenic differentiation as illustrated by a higher production of OCN. In those cases, the entire cell is responsible for the sensing of the surrounding topographies. Given that the adhesion-mediated topography sensing involves the same signaling pathways classically associated with osteogenic differentiation, the molecular events are fairly well understood. By contrast, deformation-induced topography sensing remains unclear, especially because it involves the whole cell as a unique and complex mechanical unit and thus represents an enticing future challenge for the field.
# Resilient Green Cellular IoT for Landslide Monitoring Using Voice Channels
## 3 VRITHI: System Design and Characteristics
### Advantages of Control Channel  
Using the control channel offers many benefits, as listed below:  
* Unlike the data channel, when the communication is happening between the control channel of the sender and receiver nodes, the third node receives a busy tone and cannot initiate another communication or flood the receiver, causing a DoS attack. In contrast, during communications through the data channel, any node on the Internet can flood the receiver with IP packets that results in DoS attack.
* In the control channel, the communication happens using IMSI number, which can drastically reduce the traditional botnet attacks.
* If the botnet DoS attacks compromise the modem, the TCP/IP stack becomes non-responsive, but the GSM stack that has established voice bearer remains active. Hence, voice connections continue to operate.
* Listening in the data channel is performed only when it is required for communication (or signaled by the control channel). This helps in saving power, as shown in Section 4.5.
* The IP assigned to the IoT node by the Telecom provider keeps changing over time. The control channel takes care of such dynamic-IPs assigned to the node. This saves memory space by not having a separate Dyn-DNS client running in the main memory of the node.  
Figure 6: Communication handshake for peer-to-peer communication.  
* During disasters or emergency conditions, voice traffic has a higher priority than Internet data traffic. Hence, having a control channel in voice traffic is advantageous for signaling in such scenarios.
## 1 Introduction
The proposed method first estimates the IF of a multi-component signal by developing an efficient IF estimation algorithm for multi-sensor scenario (i.e., when the signal is received by multiple sensors). The estimated IFs are then used to separate sources. Finally, the DOA of each source is separately estimated by employing the MUSIC algorithm. The salient features of the method are:  
* The method can achieve good performance in low signal to noise ratios in both under-determined and over-determined scenario. * The method is applicable to a large class of signals and does not require signals to have a non-overlapping signature in the TF domain or follow a specific mathematical model. * The method is computationally efficient as compared to the methods of similar performance. The key contributions of this study are as follows:  
* A computationally efficient and accurate multi-sensor IF estimation algorithm is developed that achieves better performance without requiring the computation of Adaptive directional time-frequency distributions [28] or Viterbi algorithm [26] thus both reducing computational cost and resulting in improved performance. * A DOA estimation method, based on multi-sensor IF estimation, is developed that achieves better performance as compared to our existing methods, which are the Viterbi-based algorithm and ridge tracking approach, both in terms of computational cost and performance [24; 25]. The remaining paper is organized as follows. The signal model is presented in Section 2. The proposed methodology is elaborated in Section 3. The computational  
Figure 1: Time–frequency representations of (**a**) non-overlapping signal components and (**b**) intersecting components. cost comparison with other related methods is given in Section 4. Numerical results are provided in Section 5 and work is concluded in Section 6.
# Traffic-Based Heuristics for Regenerator Placement in Translucent Dynamic Optical Networks
In this paper, we propose solving the RPP using the second approach but dismissing the use of network simulations to return the related BPs during the RPP procedure. Instead, for the BP evaluation, we use approximate analytical modeling, which allows the evaluation of the impact of the inclusion of each regenerator in the overall network BP. Using the heuristic approach, [PERSON] [2] proposes a statistical framework to tackle RPP in translucent optical networks under ad-hoc traffic. The framework consists of two parts: first, some heuristic is used to evaluate the probability \(pdf_{3R}(i)\) of a node \(i\) being used as a regeneration point by some lightpath. Then, the regenerators are iteratively placed in the network, one per iteration, in the node that presents the maximum value of the following deviation: \(\Delta pdf_{3R}(i)=pdf_{3R}(i)-n_{3R}(i)/n_{3R}^{total}\), in which, \(n_{3R}(i)\) is the current number of regenerators deployed in node \(i\) and \(n_{3R}^{total}\) is the total number of regenerators to be deployed in the network. In this paper, we propose two heuristics to solve RPP. We introduce two alternative approaches based on Markov modeling to evaluate \(pdf_{3R}(i)\), and we propose that during the iterative process, the decision to place a regenerator on a node be based on the BP reduction generated by that choice. To infer the BP reduction, we used a predicted ad-hoc traffic matrix used to model the stochastic behavior of the ad-hoc traffic. Different from the algorithms presented in [2], the proposed strategies can also consider non-uniform traffic. We also introduce the concept of the essential node, which helps solve RPP. Thus, the main contribution of our method is that we approximately evaluate the reduction of the overall network blocking probability (BP) caused by the addition of a regenerator in each network node and we select, for the 3R inclusion, the node that provides the greatest reduction in BP. The previous methods, on the other hand, do not perform such evaluation: they usually place regenerators proportionally to the traffic offered to each node, which does not correspond to the solution that results in the greatest reduction of the BP because the relationship between the reduction in BP and node offered load is not linear. The paper is organized as follows: in Section 3, we establish some definitions and mathematical notation used in the paper, in Section 4, we introduce the proposed RPP heuristics, in Section 5, we state the simulation setup used in the simulations, in Section 6 we show the simulation results and in Section 7, we give our conclusions.
# Integrating Machine Learning Techniques for Enhanced Safety and Crime Analysis in Maryland
## 3 Methodology
### Model Development
**Hyperparameter Optimization and Model Integration**  
Our study employed rigorous hyperparameter tuning using GridSearchCV across multiple machine-learning models to optimize their performance for crime data analysis. Key models used included random forest, gradient boosting, XGBoost, and CatBoost, each tailored to address specific challenges in modeling crime patterns. For the random forest model, we experimented with a range of n_estimators (100, 200, and 300), max_depth (None, 10, and 20), and min_samples_split (2, 5, and 10), aiming to fine-tune the model's complexity and enhance its generalization capabilities while avoiding overfitting. Gradient boosting was optimized by adjusting n_estimators (100, 200, and 300), learning_rate (0.01, 0.05, and 0.1), and max_depth (3, 5, and 7). This setup ensured that each successive tree that was built incrementally improved upon the previous ones, thereby enhancing the model's accuracy and efficiency. XGBoost underwent detailed tuning for learning_rate (0.01 and 0.1), max_depth (3, 5, and 7), and subsample (0.8, 0.9, and 1.0), leveraging its advanced regularization to minimize overfitting and maximize performance. CatBoost was similarly fine-tuned, focusing on depth (4, 6, and 8), iterations (100, 200, and 300), and learning_rate (0.01 and 0.1) to optimize its processing of categorical data and intricate dataset interactions. Furthermore, we incorporated advanced ensemble techniques to enhance the model's predictive accuracy. The voting regressor integrated outputs from various models to stabilize predictions by reducing variance. In contrast, the stacking regressor applied a meta-model to exploit the diverse strengths of base models, significantly boosting the overall model efficacy. These strategic enhancements ensured that each model and ensemble technique not only performed optimally on its own but also contributed to a robust, comprehensive predictive framework. This approach underscores our commitment to employing advanced machine learning to generate actionable insights, thereby influencing policy and enhancing public safety effectively.
# Recent Development of Morphology Controlled Conducting Polymer Nanomaterial-Based Biosensor
## 5 Others  
As noted throughout this review, researchers have taken advantage of the unique electrical and chemical properties offered by the alternative structures of conductive polymer chains to synthesize more effective biosensor electrodes. [PERSON] et al. synthesized poly (2,5-di(furan-2-yl) thiazolo [5,4-d]thiazole) (PTZrEr)-based films using electrodeposition before introducing glucose oxidase for electrochemical glucose detection (Figure 11a) [134]. The excellent electrochromic properties of this polymer chain facilitate easy detection, as noted by the obvious color changes of the film material. One clear example of this can be seen in the study conducted by [PERSON] et al. in which colorimetric thrombin aptasensors were generated using poly [3-(3'-\(N\),\(N\),\(N\)-triethylamine-1'-propyloxy)-4-methyl-2,5-thiophene hydrochloride] (PMNT) films via intrinsic peroxidase-like activities that catalyzed the reaction of the peroxidase substrate 3,3',5,5'-tetramethylbenzamideidine (TMB) in the presence of hydrogen peroxide (H\({}_{2}\)O\({}_{2}\)). As a result, a notable blue color was observed (Figure 11b) [135]. [PERSON] et al. synthesized \"layer-by-layer\" films consisting of polythiophene/streptavidin/biotinylated captured probes to detect small RNA (e.g., miR-221 microRNA) biomarkers of lung and breast cancer cell lines [136]. The sensing performance of other conducting polymer-based biosensors are suggested in Table 4.  
Figure 11: (**a**) The PTZrEr film-based electrochemical sensors for glucose detection [134]. Copyright 2019, Elsevier. (**b**) The poly [3-(3’-\(N\),\(N\),\(N\)-triethylamine-1’-propyloxy)-4-methyl-2,5-thiophene hydrochloride] PMNT-based colorimetric thrombin aptasensors [135]. Copyright 2017, Elsevier.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Nanostructure** & **Recpero** & **Analyte** & **Mechanism** & **LOD1** & **Response Time** & **Working Temperature** & **Linearity** & **Cycle Stability** & **Reference** \\ \hline PTLF* 2 film & GO* 3 & Glucose & Electrochemic & 12.8 \(\mu\)M & \(<\)10 s & 25\({}^{\circ}\)C & 12.8-500 \(\mu\)M & - & [134] \\ PMMT* 4 film & Appearance & Throughput & Colorimetric & 4 \(\mu\)M & - & 25\({}^{\circ}\)C & 0.01-0.1 \(\mu\)M & - & [135] \\ PT-based LBU* 5 film & Botion & mNR-221 & Electrothermal & 0.7 \(\mu\)M & - & 25\({}^{\circ}\)C & 0.7-100 \(\mu\)M & - & [136] \\ \hline \hline \end{tabular} \({}^{\dagger}\) Limit of detection, \({}^{2}\) poly(2-5-difuran-2-yl) thiazol5[5,4-dithiazol), \({}^{3}\) glucose oxidase, \({}^{4}\) poly[5-(3-N,N-diethylamino-1-\({}^{2}\)-propyl)-4-methyl2-5-thiophene hydrochloride].  
\({}^{5}\) polythiophene-based layer-by-layer.  
\end{table}
Table 4: Comparative sensing performance and limit of detection of other conducting polymer nanostructure-based biosensors.
###### Abstract  
The SCADA system, which is widely used in the continuous monitoring and control of the physical process of modern critical infrastructure, relies on the feedback control loop. The remote state estimation system triggers the control algorithm or control condition of the controller according to the monitoring data returned by the sensor. The controller sends the control command to the actuator, and the actuator executes the command to control the physical process. Since SCADA system monitoring and control data are usually transmitted through unprotected wireless communication networks, attackers can use false sensor data to trigger control algorithms to make wrong decisions, disrupt the physical processing of the SCADA system, and cause huge economic losses, even casualties. We found an attack strategy based on the sequential logic of sensor data. This kind of attack changes the time logic or sequence logic of the response data, so that the false data detector can be successfully deceived. This would cause the remote state estimation system to trigger wrong control algorithms or control conditions, and eventually disrupt or destroy the physical process. This paper proposes a sequential signature scheme based on the one-time signature to secure the sequential logic and transmission of sensor data. The security analysis proves that the proposed scheme can effectively resist counterfeiting, forgery, denial, replay attacks, and selective forwarding attacks.  
SCADA system; remote state estimation; sequential logic attack; one-time signature 2022