esa-sceva/satcom-chunk-collection huggingface dataset:
# Social Media Analytics for Disaster Response: Classification and Geospatial Visualization Framework
## 5 Discussion  
### Classification and Data Source  
The classifications of social media posts in existing disaster response datasets often fall short of meeting the nuanced requirements of real-time emergency scenarios. Many existing categories are too broad or generalized, failing to capture the specific situational details necessary for facilitating effective emergency interventions. A more focused classification system, tailored to the real-world needs of disaster response, is critical for enhancing situational awareness and enabling rapid, targeted actions. In a real-time disaster response system, it is essential to prioritize categories that provide actionable intelligence for response teams, such as identifying individuals in urgent need of rescue, those requiring medical assistance, or areas affected by infrastructure damage. In the current study, we rely exclusively on a publicly available dataset composed of tweets, which inherently limits our model to the linguistic patterns and content styles typical of Twitter/X. Acknowledging this limitation, future research will focus on expanding our datasets by collecting and integrating data from diverse social media platforms, such as Facebook, Instagram, and other sources. This broader approach will enable our model to generalize better across varying linguistic contexts, content formats, and communication styles, thereby enhancing its robustness and effectiveness in real-world disaster response scenarios. In future work, we will implement advanced techniques, including similarity detection algorithms, credibility assessment tools, and machine learning-driven misinformation detection models, to proactively identify, manage, and mitigate the impact of fake or duplicate social media posts. These enhancements will significantly improve the reliability and accuracy of our disaster response system in real-world operational scenarios.
## 2 Material and Methods
### Comparison of Methods  
For assessing the performances of the different methods utilized for filling missing values in daily and monthly precipitation time series, the following metrics are used:  
* Mean Absolute Error (MAE) The MAE represents the average amplitude of the absolute error. It is represented by Equation (3): \[\text{MAE}=\frac{1}{n}\ \sum_{i=1}^{n}\left|E_{i}-O_{i}\right|\] (3)
* Root Mean Square Error (RMSE) The RMSE measures the amplitude of the average squared error; hence, it more strongly penalizes larger errors than MAE, which is useful for assessing whether the lack of fit is restricted to highest order statistics or the entire range of rainfall amounts. It is represented by Equation (4): \[\text{RMSE}=\sqrt{\frac{1}{n}\ \sum_{i=1}^{n}\left(E_{i}-O_{i}\right)^{2}}\] (4)
* Percent Bias (PBias)The percent bias is used to assess systematic errors. It can quantify tendencies of underestimation and overestimation of the estimated values with respect to the observations, as represented by Equation (5):  
\[\text{PBias}=\frac{\sum_{i=1}^{n}(E_{i}-O_{i})}{\sum_{l=1}^{n}O_{i}}\times 100 \tag{5}\]
* Correlation Coefficient (CC)  
The correlation coefficient reflects the degree of linear correlation between estimated and observed values, as represented by Equation (6):  
\[\text{CC}=\frac{\sum_{i=1}^{n}\bigl{(}O_{i}-\overline{O}\bigr{)}\left(E_{i}- \overline{E}\right)}{\sqrt{\sum_{i=1}^{n}\left(O_{i}-\overline{O}\right)^{2} \cdot}\sqrt{\sum_{i=1}^{n}\left(E_{i}-\overline{E}\right)^{2}}} \tag{6}\]  
In Equations (3)-(6), \(n\) is the number of days, months, or years of imputed values, \(O_{i}\) are the observed values, and \(E_{i}\) are the values estimated by the method for filling missing values.
## 4 Case Study
### DIPA for Hotel A  
Although the BLCCs in Figures 5 and 6 are useful in abstracting the overall history of an attribute, they are limited in their ability to reveal trends over time and predict the future status. Staged control charts can be a better solution for extracting changes over time.  
For instance, Figure 7 shows two staged charts for DF, in which stages are defined based on the review year (2018, 2019, and 2020). The staged \(p\)-chart in Figure 7a shows that the mean appearance frequency gradually decreased from 0.481 in 2018 to 0.358 in 2020 (the first quarter only). This implies that customers' interest in or the importance afforded to DF decreases over time. The staged \(g\)-chart in Figure 7b shows that the median complaint interval of DF decreased from 34.2 in 2018 through 10.0 in 2019 to 3.7 in 2020. This indicates that the complaint rate increased or the performance level decreased over time.  
As shown in the DF example, the CLs of an attribute's staged \(p\)- and \(g\)-charts (i.e., \(\text{CL}^{p}\),\({}_{is}\) and \(\text{CL}^{g}\), respectively) can be an indicator of the attribute's average importance and performance, respectively. By tracking how the CL values change over the stages through DIPA, it is possible to identify which attributes require urgent action to reduce complaints.  
The proposed DIPA generates a two-dimensional graph in which the importance and performance levels are represented on the \(y\)- and \(x\)-axes, respectively. The importance and performance index values for the individual attributes of Hotel A are listed in Tables 9 and 10, respectively. In addition, two boundary values are required to divide the low and high levels of importance and performance. In many IPA studies (e.g., [1; 4; 31; 33; 34; 63]), a commonly accepted way to set the boundary values is to use the average values of all points on the charts. For this case study, the boundary values were set at 0.4310 for the importance index and 0.8654 for the performance index, based on the average values of all points on the charts.  
Figure 7: Examples of staged control charts for the DF attribute of Hotel A.  
Figure 8 shows the final graph of the proposed DIPA. The DIPA classifies attributes into four groups and identifies attributes with a greater need for quality improvement. The attributes in Q2 require the most attention and urgent improvement. In the case of Hotel A, the DIPA suggests that FC and RC require greater efforts to reduce complaints. They are frequently mentioned in customer reviews, but their performance levels are relatively low compared to those of other attributes. The DIPA also cautions that the performance levels of DF, BC, and FA are decreasing significantly. Although immediate actions may not be required because of their low importance, further investigation of the root causes behind the performance drops is recommended.
## Appendix D Small-Signal Analysis  
To find the FN PSD, the system shown in Equation (16) is to be solved. This is done using the small-signal analysis proposed in Equation (19). Assuming a narrow-linewidth laser, i.e., a long coherence time with respect to the external cavity lenghts:  
\[t_{\text{ext}}<t_{\text{coh}}, \tag{128}\]  
the following approximation is valid:  
\[\Omega t_{\text{ext}}\ll 1. \tag{129}\]  
Simplifying this equation, and neglecting the quadratic terms yields:  
\[i\Omega^{\prime}S_{\Delta}\overset{\eqref{eq:20}}{\approx}a_{g}N_{\Delta}S_{0}- \zeta_{\mathrm{s}}S_{\Delta}-i\Omega^{\prime}\phi_{\Delta}2\mathrm{K}_{\mathrm{s }}S_{0}+i\Omega^{\prime}S_{\Delta}(\kappa_{1}^{\mathrm{c}}-\kappa_{2}^{\mathrm{ c}})+F_{\mathrm{S}}. \tag{111a}\]  
In a similar way, Equation (111b) can be rewritten using Equations (111) and (20):  
\[i\Omega^{\prime}\phi_{\Delta} \approx\ \frac{\alpha_{\mathrm{H}}}{2}\Big{(}G_{0}+a_{g}N_{\Delta}- \tau_{\mathrm{ph}}^{-1}\Big{)}-\bigg{[}\alpha_{\mathrm{H}}\frac{G_{0}-\tau_{ \mathrm{ph}}^{-1}}{2}-\kappa_{2}\sin(\phi_{2})+\kappa_{1}\sin(\phi_{1})\bigg{]}\] \[\quad-\kappa_{2}\bigg{(}1-\frac{i\Omega^{\prime}S_{\Delta}}{2S_{0 }}t_{2}\bigg{)}\big{[}\sin(\phi_{2})+i\Omega^{\prime}\phi_{\Delta}t_{2}\cos( \phi_{2})\big{]} \tag{111b}\] \[\quad+\kappa_{1}\bigg{(}1+\frac{i\Omega^{\prime}S_{\Delta}}{2S_{0 }}t_{1}\bigg{)}\big{[}\sin(\phi_{1})+i\Omega^{\prime}\phi_{\Delta}t_{1}\cos( \phi_{1})\big{]}+F_{\phi}\] \[\Leftrightarrow 2i\Omega^{\prime}\phi_{\Delta}\overset{\eqref{eq:20} \eqref{eq:20}}{=}\alpha_{\mathrm{H}}a_{g}N_{\Delta}+2(\kappa_{1}^{\mathrm{c}}- \kappa_{2}^{\mathrm{c}})i\Omega^{\prime}\phi_{\Delta}+\frac{i\Omega^{\prime}S_ {\Delta}}{S_{0}}K_{\mathrm{s}}+2F_{\phi}.\]  
Finally, Equation (111c) can be rewritten as:  
\[i\Omega^{\prime}N_{\Delta}\overset{\eqref{eq:20}\eqref{eq:20}}{=}\overset{ \eqref{eq:20}}{\approx}G_{0}S_{0}-\big{(}G_{0}+a_{g}N_{\Delta}\big{)}S_{0}- \big{(}G_{0}+a_{g}N_{\Delta}\big{)}S_{\Delta}-\tau_{\mathrm{sp}}^{-1}N_{\Delta }+F_{N}\] \[\Leftrightarrow i\Omega^{\prime}N_{\Delta}\overset{\eqref{eq:20} \eqref{eq:20}}{=}-\tau_{\mathrm{e}}^{-1}N_{\Delta}-G_{0}S_{\Delta}+F_{N}. \tag{111c}\]  
The linearized rate equations of the laser under study are thus Equations (111a)-(111c), from which the power spectral density, and subsequently the intrinsic linewidth, can be computed.
# Adversarial Hierarchical-Aware Edge Attention Learning Method for Network Intrusion Detection
## 2 Related Work  
### Application of Graph Neural Networks in NIDSs  
In recent years, GNNs have been widely used in network intrusion detection systems. [PERSON] et al. [23] introduce a GNN-based framework for end-to-end botnet detection, focusing exclusively on network topological structures rather than incorporating node or edge features. This approach enables the model to aggregate features across the graph purely based on structural relationships, formulating botnet detection as a binary classification task. Experimental results demonstrate that their GNN models significantly outperform the logistic regression method. Subsequently, traditional graph embedding approaches [24; 25] are applied to network intrusion detection by converting network flows into graphs using source-destination IP and port pairings. Methods like DeepWalk (skip-gram) are employed to generate node embeddings for anomaly detection. However, this shortcoming renders them impractical for most real-world NIDS scenarios because real network flow detection cannot only target nodes, but should target the network flow between two nodes.  
Therefore, we investigate the edge determination method. E-GraphSAGE [13] introduces an edge feature encoding mechanism using the GraphSAGE [26] framework, which achieves inductive learning of dynamic network flow by aggregating information about neighbor nodes and edges. However, this method adopts a static mean aggregation strategy and fails to distinguish the importance between key attack paths and normal flow interactions, resulting in insufficient detection sensitivity for covert attack patterns (such as APT). Similarly, GNN-IDS [27] proposes a flow-level anomaly detection framework based on graph convolution, but its homogeneous graph assumption oversimplifies the heterogeneity of network entities (such as differences between hosts, protocols, and services). Recent studies attempt to model edge attributes through GNNs [14], but they mainly focus on binary classification (malicious vs. benign flow) and ignore the need for hierarchical identification of fine-grained attack types (such as DDoS, port scanning, and malware propagation).  
In summary, the above methods generally have two key problems: firstly, the dynamic fusion mechanism of edge features and topological structures is missing, making it difficult to capture the synergy between edge semantics (such as flow statistics) and topological patterns (such as attack propagation paths) in attack behaviors; secondly, the detection granularity is single, which cannot meet the synergy requirements of coarse-grained screening and fine-grained classification in actual network environments. The AH-EAT framework proposed in this study effectively solves the above problems through attention mechanisms and hierarchical multi-grained detection.
###### Abstract  
Effective data interoperability and schedule analysis play a significant role in improving the management of prefabricated buildings. However, there is a lack of efficient strategies and comprehensive approaches for data interoperability and data-based automated schedule analysis. This paper intends to promote prefabricated buildings' management by solving these two problems via developing an IFC-based framework consisting of three parts. Firstly, this framework proposed a mechanism to establish an IFC-based 4D construction management information model of prefabricated buildings. Furthermore, a non-relational database--graph database--is introduced to twin this model into a task-centered network to realize the interoperation of construction information among different participants. Finally, graph database-based strategies to update data, automatically analyze construction schedules and visualize the 4D construction management information model are described. The proposed framework is validated in a prefabricated engineering case. In this case, an IFC-based and graph database-based 4D construction management information model is established through IFC standard's extension. The graph database-based analysis of the model automatically recognizes the engineering case's critical path information, delay analysis information, and schedule network analysis information. It is illustrated that this framework can successfully establish a unified IFC-based information model of prefabricated buildings' construction management to prompt effective data interoperability. In addition, the application of this IFC-based information model in graph database can automatically analyze the construction schedules to prevent possible delays in advance. In short, the significance of this paper is to innovatively propose an IFC-based and graph data-based information model to solve the difficulties of ineffective data interoperation and unautomated schedule analysis in prefabricated buildings' construction management. This study can be the digital foundation of further IFC-based digital twin.  
IFC standard; prefabricated buildings; construction management information; graph database; delay analysis +
Footnote †: journal: Applied Sciences
# A Review on Standardizing Electric Vehicles Community Charging Service Operator Infrastructure
## 4 Charging Type
### Wireless Charging: Charging Type
They optimized the user's utility with the help of the introduced hierarchical game-based approach. Similarly, [PERSON] et al. [93] presented cost-efficient dynamic wireless charging for EVs by optimizing the ground assemblies and considering the misalignment and cost issues. The simulation yielded a result of 96% efficiency and cost of ground assembly of USD 1004. * Quasi-Dynamic wireless charging Quasi-dynamic wireless charging can be considered as a subcategory of dynamic wireless charging. In quasi-dynamic wireless charging, EVs can charge in transit. However, the charging will be performed at a slower speed than in the case of dynamic wireless charging. EVs that are charged with quasi-dynamic charging technology can opt for CS near traffic signals or parking slots. Therefore, they have the advantage of a low charging time for dynamic wireless charging, with reduced architecture cost and increased efficiency [19]. Due to the cost-efficiency and reliability benefits, many companies have developed and introduced quasi-dynamic wireless charging to ensure the ease of EV wireless charging. For example, IPT Technology has provided wireless charging solutions for light duty and heavy duty vehicles since 1997. They developed various series of wireless charging buses that can be charged at a bus stop or if they are parked for an extended duration [94]. However, it is more difficult to manage and plan quasi-dynamic wireless charging compared to dynamic wireless charging due to the frequent requirement of charging at a parking space or traffic signal. Many researchers have provided solutions for efficient, quasi-dynamic wireless charging. For example, [PERSON] et al. [95] discussed the feasibility analysis of quasi-dynamic wireless power transfer for EV charging. They proposed an algorithm to manage the fluctuation of charging and discharging of EVs at a traffic signal which further extends the charging duration. Then, [PERSON] et al. [96] presented an analysis of quasi-dynamic wireless power transfer for EV charging considering several misalignment scenarios. They performed a simulation to achieve an optimized charging and recharging scenario by handling the air-gap conditions in EVs. Later, [PERSON] et al. [97] addressed the electromagnetic security issues in dynamic wireless EV charging. The simulation model was developed to secure the EV against the exposed magnetic field while charging in transit considering the various EV charging scenarios. Table 4 represents the analysis of various wireless charging types (static, dynamic, and quasi-dynamic) along with their advantages and disadvantages to improve wireless charging for EVs.
## 5 Conclusions  
This paper introduces the design of a rectenna at 2.45 GHz and 5.8 GHz for energy harvesting. A DRA is proposed to design the receiving antenna. To further improve the gain of the receiving antenna, a compact feed network is designed to excite a 4-element DRA array. The measured gains of the DRA array are 5.5 dBi at 2.45 GHz and 12.9 dBi at 5.8 GHz. For the rectifier, a novel IRS is proposed to significantly improve the efficiency of the rectifier circuit. The rectenna is processed and tested. The measurement results show the maximum conversion efficiency can reach 66% at 2.45 GHz and 62% at 5.8 GHz when the input power level is 10 dBm. Moreover, a DC-to-DC boost converter can be used. With its superior performance, the rectenna is suitable for various ultra-low applications.  
Conceptualization, [PERSON] and Z.L. ([PERSON]); methodology, [PERSON] and Z.L. ([PERSON]); investigation, [PERSON] ([PERSON]); resources, [PERSON] ([PERSON]); writing--original draft preparation, [PERSON] ([PERSON]); writing--review and editing, [PERSON]; visualization, [PERSON]; supervision, [PERSON]; project administration, [PERSON]; funding acquisition, [PERSON] ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Key R&D Program of China under Grant 2019 YFB2101601.  
Figure 20: The conversion efficiency measurement. (**a**) Measurement setups; (**b**) DC Voltage across the supercapacitor.
## 5 Conclusions  
An extensive analysis of HAPS massive MIMO systems employing triangular lattices has been presented within the 5G NR n257 and n258 frequency bands (24.25-29.5 GHz). The noteworthy massive MIMO performance improvement when adopting a triangular lattice arrangement of the array elements instead of a square one in a planar array with a regular lattice has been highlighted. Specifically, a triangular lattice allows for the achievement of a superior array gain and ASLL reduction, as well as greater robustness of the antenna elements' impedance variation during beam steering by exploiting lower MC levels. Moreover, the larger minimum distance between elements in the case of a triangular grid guarantees a better angular resolution that, in a massive MIMO scenario, provides superior interference robustness and, hence, higher SE. The advantages offered by the regular and periodic triangular lattice arrangement of antenna elements make it appealing for 5G massive MIMO applications.  
Conceptualization, F.A.D.; validation, F.A.D. and S.G.; investigation, F.A.D. and S.G.; writing--original draft preparation, F.A.D.; writing--review and editing, F.A.D. and S.G. All authors have read and agreed to the published version of the manuscript.  
Work partially supported by the Italian Ministry of Education and Research (MIUR) in the framework of the CrossLab project (Departments of Excellence).  
Not applicable.  
The authors declare no conflict of interest.
# BL0K: A New Stage of Privacy-Preserving Scope for Location-Based Services
## 8 Conclusions and Future Work  
This paper outlined increasingly comprehensive research on the preservation of privacy in LBS. We proposed a BLOom Filter 0 Knowledge (BL0K) framework that aims to preserve the user's privacy queries in LBS, consisting of both a bloom filter and zero-knowledge proof. We showed that BL0K enhances security and preserves privacy. BLOT is another mechanism, valuable in its ability to address the problems in LBS. However, this particular mechanism is not perfect, and may contain the potential risk of leakage. The BL0K approach is able to detect malicious servers directly, and can reduce the amount of data being exposed during an attack, thus slowing data leakage from the BF from 0.5 bit per query to 0.5 bit per N queries.  
In a worst case scenario, an unauthorized LBS provider, through multiple attacks, is able to obtain a user's complete location information history. A potential solution to this risk is to use the BL0K capability to mask any sensitive data exchanges occurring during communication of location-sharing procedures. We showed that the performance of the BL0K framework, using a dataset, is very close to the real-world queries by comparing its performance with the BLOT in terms of privacy (entropy). As shown in Figure 11, the computation performance of BL0K is the best because it requires the least time in the execution of one complete query because the data flow in BL0K is less than in BLOT and BMBbishare.  
The proposed method resulted in performance improvements up to 93% (average 25% to 30%). As a result, an untrusted participant cannot gain access to unauthorized privacy information. It follows that, when compared to BMBbishare and BLOT, BL0K is the securest and most efficient service with the best performance.  
For future work, we propose a cloaking system model called zero-knowledge cloaking (ZKC). In this model, it is possible for a malicious LBS to learn at most one bit of information about the location of the user based on a single query but can learn at most n bits about the location of the user at a past time after n consecutive queries. The ZKC system uses zero-knowledge proofs of location (ZKPL), so that the user can detect malicious LBS providers with only a few queries. This system can provide provably better anonymization than k-anonymity, and does not use non-person proxies (\"dummies\").  
Conceptualization, A.A. and V.T.; Data curation, A.A.; Formal analysis, A.A. and V.T.; Investigation, A.A. and V.T.; Methodology, A.A.; Resources, A.A. and V.T.; Software, A.A; Supervision, A.A. and V.T.; Validation, A.A.; Visualization, A.A. and V.T.; Writing-original draft, A.A.; Writing-review & editing, A.A. and V.T.  
This research received no external funding.  
The authors declare no conflict of interest.
# Security in Wireless Sensor Networks: A Cryptography Performance Analysis at MAC Layer
## 4 Implemented Attacks and Relative Mitigations
### Impersonation Attack
#### 4.2.1 Impersonation Attack: Impersonating a Sensor Node against the Legacy Server  
In our scenario, the attacker, before carrying out the attack and just before sending the packet to the server, modifies the source IP address in the packet so that the other node thinks that it is a legacy node of the WSN. This modification, made by the attacker, is done at network layer exploiting the IPv4 protocol. Particularly, the attacker forges a new packet and in the decapsulation phase, sets this packet as a source IP one that is assigned to a legacy node in the WSN, choosing it in a random way. The packet, therefore, will be forged by the attacker but will traverse the network with a source IP address which cannot be referred in any way to the malicious sensor. Considering a scenario which does not provide any cryptographic technique, there are no mechanisms which could guarantee the proper sender identification; thus, the attack is completed and the server will be fooled regarding the real identity of the sender.