esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on Energy Drainage Attacks and Countermeasures in Wireless Sensor Networks
## 4 Recent Review Based on State-of-the-Art Works for Energy Drain Attack
### Network Layer
#### 4.3.5 Sinkhole Attacks  
In sinkhole attacks, a compromised node can attract all the traffic from neighbor nodes by faking the routing metric in the routing protocol. A malicious node in this attack advertises itself as the closest node to the sink node to capture the network traffic, then drops data packets instead of forwarding them to the base station. This operation can cause an energy drain on surrounding nodes because all traffic is forwarded to them but not the sink node. [PERSON] [13] published a survey paper with existing schemes before 2019. They reviewed sinkhole attack detection, prevention strategies, and attack techniques. Furthermore, fuzzy logic-based systems are recommended as countermeasures in the intruder detection system. As a similar approach, we will review recent work on sinkhole attacks in the WSN that have been published since 2019.  
[PERSON] and [PERSON] [75] proposed a lightweight, secure method that extended existing threshold-sensitive, energy-efficient sensor network protocols for routing, as well as watermarking techniques to ensure data integrity during transmission. Homomorphic encryption was used to detect and prevent sinkhole attacks. The BS generates two keys, namely network and cluster keys. The network key is used to encrypt and decrypt node IDs between clusters. If a node becomes a sinkhole attacker, then it propagates a message to the sensor nodes with its encrypted ID. This node is detected by a cluster header. [PERSON] et al. [76] proposed sinkhole attack detection by integrating set pair analysis (SPA) and Java algorithms in WSNs. The trust value of a suspicious node is calculated by the SPA trust model and used to detect the attacker. To avoid the attacked region, the detoured path is constructed using the Java algorithm. The proposed scheme is composed of a suspiciousness detection phase, a malicious identification phase, and defense against attack phase. Abnormal changes caused by sinkhole attacks include the hop count, packet forwarding rate, energy consumption, and delay initiating the detection procedure by building a trust evaluation model by combining direct and indirect trust values. [PERSON] et al. [77] proposed a novel intrusion detection scheme called a time-synchronized multivariate regressive convolution deep neural network model (TSMR-CDNN). In this model, the sinkhole attack is identified by clock characteristics and radio details. As for data packets from the node, each node estimates the clock characteristics and radio information that is analyzed by the regression function. The estimated value is compared with a threshold value to make the decision for the attacker.
## 2 Materials and Methods
### Instrumentation and Measurement of Inputs and Response Parameters
#### 2.2.1 Soil Hardness  
A hydraulically operated cone penetrometer (strain gauge, full bridge 350 \(\Omega\), 100 kg load carrying capacity) was used to measure the cone index (CI), i.e., soil strength and hardness down to a 150 mm depth from the soil surface as per the procedures detailed in the ASABE Standards S313.3 [34]. Technically, CI is defined as the force per unit base area required to insert a cone-shaped probe into the soil at a steady rate (30 mms\({}^{-1}\)). The CI probe had a 323 mm\({}^{2}\) of base area and a 30\({}^{\circ}\) cone angle. A circular shaft with a 15.6 mm diameter was attached to the CI probe and a ring transducer of 10 kN capacity. The ring transducer converted the actual measured force into an electrical signal and then back to actual force. The ring transducer was equipped with a strain gauge Wheatstone bridge circuit with each gauge of 120 \(\Omega\) resistance. It was calibrated prior to force measurements. The depth of cone penetration was measured using a rotary potentiometer mounted on a rack-pinion arrangement. The DAS was connected to both the Wheatstone bridge circuit of the ring transducer and the potentiometer. An excitation voltage of 5-volt DC was provided by DAS to the ring transducer [35]. The ring transducer was calibrated by gradually increasing and decreasing the compressive force using dead weights and recording the corresponding output voltage. The CI was recorded before testing the R\({}_{\text{VA}}\) from 10 different locations, 1 m apart along the soil bin to ensure the soil bed uniformity. The soil preparation and compaction measurement procedure was repeated until the selected CI values were achieved for the soil bed.  
The soil was prepared down to a depth of 0.5 m and for moisture of 10-12% db. A time-domain-reflectometer moisture probe was used to measure the moisture content of the soil at 10 different locations, 1 m apart in the soil bin. The corresponding soil moisture values were recorded and converted to dry weight basis.
# Automated Tunnel Point Cloud Segmentation and Extraction Method
## 2 Materials and Methods
### Cross-Section Segmentation and Segment Extraction
#### 2.2.3 Tunnel Ring Inner Segmentation  
In tunnel construction, a segmented lining structure is often used, including capping blocks, crown blocks, side plates, and bottom plates. These lining segments are arranged and connected in a specific manner to form the overall tunnel structure, as shown in Figure 10.  
The TMatch algorithm transforms the tunnel structure into specific angular segments, as shown in Figure 11. Using the least-squares method, an overall circular fitting is performed to determine the center of the circle. Taking this center as the reference, the number of point clouds with different labels is calculated within specific angles of the tunnel point cloud data. Since point cloud data typically consist of millions of points, defining angles around the tunnel lining joints allows for high-precision segmentation. For long, regular tunnels, this method can be applied directly for efficient processing.
###### Abstract  
This paper is based on a novel approach towards clean energy production, i.e., space innovative applications toward sustainable development. Specifically, the role of Earth observation (EO) satellites in maximizing renewable energy production is considered to show the enormous potential in exploiting sustainable energy generation plants when the Earth is mapped by satellites to provide some peculiar parameters (e.g., solar irradiance, wind speed, precipitation, climate conditions, geothermal data). In this framework, RESTcreen clean energy management software can be used for numerical analysis, such as energy generation and efficiency, prices, emission reductions, financial viability and hazard of various types of renewable-energy and energy-efficient technologies (RETs), based on a large database of satellite parameters. This simplifies initial assessments and provides streamlined processes that enable funders, architects, designers, regulators, etc. to make decisions on future clean energy initiatives. After describing the logic of life cycle analysis of RESTcreen, two case studies (Mexicali and Toronto) on multiple technologies power plant are analyzed. The different results obtained, when projecting the two scenarios, showed how the software could be useful in the pre-feasibility phase to discriminate the type of installation not efficient for the selected location or not convenient in terms of internal rate of return (IRR) on equity.  
renewable energy; space industry; RESTcreen 2020; 12, 2062; doi:10.3390/su12052062
# Mitigating Class Imbalance in Network Intrusion Detection with Feature-Regularized GANs
## 3 Proposed Methodology
### Integration of SNNL into WGAN Training  
While WGAN improves training stability over standard GANs by addressing issues related to vanishing gradients, it does not inherently enforce feature-level similarity between real and generated samples. Consequently, synthetic data generated by WGAN may still suffer from feature misalignment, limiting its effectiveness in minority-class augmentation. To overcome this limitation, we incorporate SNNL as an additional regularization term into the critic loss during WGAN training.  
The training objective of WGAN consists of optimizing the critic to approximate the Wasserstein distance between real and generated data distributions. Unlike standard GANs, which use a discriminator for binary classification, WGAN replaces it with a critic \(C(x)\) that directly estimates the difference in expected values between real and generated samples. The original critic loss in WGAN is defined as  
\[\mathcal{L}_{\text{WGAN}}=\mathbb{E}_{x\sim p_{\text{real}}}[C(x)]-\mathbb{E}_ {\hat{x}\sim p_{\text{gen}}}[C(\hat{x})] \tag{2}\]  
where \(C(x)\) is the critic's score for a real sample \(x\), and \(C(\hat{x})\) is the score for a generated sample \(\hat{x}\). The critic is trained to minimize the negative of this objective, thereby approximating the Wasserstein distance between the two distributions. To ensure convergence, the critic is updated multiple times per generator update, and weight clipping is applied to satisfy the Lipschitz constraint.  
To promote feature-level alignment, we extend the critic's loss by introducing a Soft Nearest Neighbor Loss term applied to intermediate features of real samples:  
\[\mathcal{L}_{\text{WGAN+SNNL}}=-\mathcal{L}_{\text{WGAN}}+\lambda\cdot \mathcal{L}_{\text{SNNL}} \tag{3}\]  
where \(\lambda\) is a regularization coefficient balancing adversarial and alignment objectives. This combined loss is minimized during critic training. Importantly, SNNL is computed on all training data and does not alter the generator loss. As the critic learns more structured and class-aware representations, the generator indirectly benefits through improved training signals.  
To compute SNNL, we extract feature embeddings from the intermediate layers of the critic. Given a batch of real samples \(\{x_{1},x_{2},\dots,x_{N}\}\), their feature embeddings are defined as  
\[X=\{f(x_{1}),f(x_{2}),\dots,f(x_{N})\} \tag{4}\]  
where \(f(x)\) denotes the activation of an intermediate layer in the critic. Applying SNNL in this feature space encourages intra-class compactness and inter-class separation, improving generalization when incorporating synthetic samples into downstream classifiers.
## 1 Introduction
In order to understand the impressive stability of the NPs produced by nanosecond PLAL, it is important to investigate if the excess electrons that characterize the main stage of the PLAL process, can also have a role in the stability after their release in solution. Since then, there have been conflicting developments. One of the most convincing experimental pieces of evidence (the destabilization of the NP upon grounding the solution with a metallic wire) was invalidated by the effect of trace amounts of copper ions. On the other hand, theoretical modeling indicates a crucial role of \"trapped\" electrons on the NPs growth during the PLAL and predicts the eventual presence of \(\sim\)10-20 excess electrons on the NPs released in the aqueous medium, depending on the particle size [19]. It is worth considering this number accounts for the experimental \(\zeta\)-potential measured in water \(\sim\)70 mV. Thus, there is a more solid theoretical background in support of the \"extra electrons\" hypothesis, but a smaller set of proofs in favor of it. Intending to investigate the stability of PLAL-produced NPs, and of discussing the potential role of the excess of electrons on NPs surface after the nsPLAL process, a systematic study on the physical characteristics and chemical stability of the different metal NPs is presented. NPs made of gold, silver, and platinum have been synthesized by PLAL using conditions that allow the formation of different surface amounts of metal oxides. The surface composition was assessed by XPS and compared with the experimentally determined NP size and \(\zeta\)-potential. The data analysis reveals that the presence of oxidized metal is uncorrelated with the charge density of the particles. This finding is in agreement with a previous study [16], where the stability of gold NPs was investigated as a function of pH. In that work, the main effect of the solution pH was attributed to the measured \(\zeta\)-potential, which becomes more negative by roughly \(-30\) mV when passing from pH = 3 to pH = 10. In the case of AuNPs, this result excludes that the NPs are stabilized by gold oxides that, having pKas close to 4, should have a positive \(\zeta\)-potential at pH \(<\) 4. To further investigate the stability of the NPs produced by PLAL, and to support the systematic observation of the NPs characteristics, in this contribution, the effect of metal ions as oxidants in the colloidal solution is explored. The analysis of the results is consistent with the presence of electrons that can reduce metal ions.
# An Efficient Satellite Resource Cooperative Scheduling Method on Spatial Information Networks
## 2 Related Work
### Resource Scheduling Method Based on Game Theory  
Paper [41] proposes a resource allocation method based on multi-layer matching game theory, which mainly solves the problem of resource allocation in multi-user scenarios. Researchers [2] consider the service quality requirement as the constraint condition, study the access control strategy based on the penalty function, and propose a cooperative game satellite bandwidth resource allocation algorithm. Paper [42] proposes a bargaining cooperative game model based on the Nash equilibrium and proves the effectiveness of this method in time complexity. One study looked at [2] the problem of joint transmission power and bandwidth allocation and proposed a combined resource management method based on game theory. Researchers [43] analyzed the similarities between mobile and complex satellite services based on the main characteristics of the millimeter-wave spectrum and proposed a new cooperative scheduling algorithm based on a game-theoretic framework.  
In summary, the approaches based on machine learning, reinforcement learning, or game theory ensure the maximization of resource utilization. However, the time complexity of the algorithms is slightly higher, which increases the occupation of valuable satellite computing resources and needs to be calculated on ground-based base stations. The establishment of communication links between ground-based stations and satellites will consume a large amount of time. The resource scheduling algorithms start from the perspective of task execution gain, thus ignoring the time consumption of the algorithms, and the efficiency of resource scheduling still needs further research.  
Therefore, we propose a coordinated resource scheduling method from the perspective of efficient scheduling of spatial information network resources to solve these problems quickly and efficiently.
# Health Misinformation in Social Networks: A Survey of Information Technology Approaches
## 4 Content-Based Methods
### Emotional Features
\begin{table}
\begin{tabular}{l r} \hline \hline \multicolumn{2}{c}{**Textual Representations**} \\ \hline BoW & [30,87] \\ TF-IDF & [30,87,88,90,105] \\ n-grams & [89] \\ Non-contextual embeddings & [30,83,89,90,104,121,127] \\ Contextual embeddings & [53,80,89,90,103,105,106,107,108,127,129] \\ Knowledge embeddings & [73,115,116] \\ \hline \hline \multicolumn{2}{c}{**Linguistic**} \\ \hline PoS & [30,89,117,130] \\ Uncertainty constructs & [88,131] \\ Certainty constructs & [88] \\ Passive voice & [119] \\ Headline length & [88] \\ \hline \hline \multicolumn{2}{c}{**Emotional**} \\ \hline Sentiment analysis & [30,122,123,124,125,126] \\ Subjectivity & [121] \\ Polarity & [122,124] \\ \hline \hline \multicolumn{2}{c}{**Enities**} \\ \hline NER & [30,130] \\ Medical terms & [30] \\ Commercial terms & [30] \\ Hyperlinks & [88] \\ Hashtags & [132] \\ Direct quotes & [88] \\ Visual and textual entity inconsistencies & [133] \\ \hline \hline \multicolumn{2}{c}{**Stylistic**} \\ \hline Complexity, readability, specificity, & [87,121,131,134] \\ informality, non-immediacy, diversity & [87] \\ Misspellings & [87] \\ \hline \hline \multicolumn{2}{c}{**Topic Extraction**} \\ \hline LDA & [135] \\ Markov chains & [136] \\ \hline \hline \multicolumn{2}{c}{**Image**} \\ \hline Visual features & [133,137,138] \\ \hline \hline \multicolumn{2}{c}{**User Profiles**} \\ \hline User description, timeline, & [117,128] \\ friends profile & \\ \hline \hline \multicolumn{2}{c}{**External**} \\ \hline Fact-verification score & [128] \\ Credibility score & [139] \\ Stance detection & [140, 141, 142] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Features used in content-based approaches.
# Time-Frequency Analysis of Daily Activities for Fall Detection
## 3 Results
### Fall Algorithm Testing & Validation  
To evaluate the algorithm's performance, a confusion matrix is used to summarize the results. Table 2 provides a confusion matrix for half the dataset use for optimizing the algorithm (i.e., determining key thresholds, etc.). Falls were accurately detected with  
Figure 4: Display of Acc (row 1), Acc\({}_{yz}\) (row 2), spectrogram of Acc (row 3), and power spectral density (PSD) of Acc (row 4). Each column represents different rigorous activities such as (**a**) running, (**b**) falling, (**c**) jumping forward, and (**d**) jumping up/down.  
only seven falls incorrectly classified out of 261 fall events (sensitivity = 97.3%). Similarly, daily activities (i.e., standing, sitting, stepping, running, jumping or lying) were accurately recognized with only six misclassified as fall out of 357 daily activities (specificity = 98.3%).  
Once critical thresholds for the algorithm were optimized, the remaining half of the dataset was used to validate the developed algorithm. Table 3 provides a summary of this validation analysis. Only four out of 254 falls were not detected (sensitivity = 98.4%) and only five out of 355 daily activities were identified as falls (specificity = 98.6%).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & \multicolumn{2}{c}{**Detected**} \\ \cline{3-4}  & & **Fall** & **Daily Activities** \\ \hline \multirow{3}{*}{Actual} & Fall (n = 261) & 254 & 7 \\ \cline{2-4}  & Daily activities & 6 & 351 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Confusion matrix for algorithm development and optimization dataset.  
Figure 5: Fall detection algorithm. Stage A is performed in the time domain with Stage B in the frequency domain.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & \multicolumn{2}{c}{**Detected**} \\ \cline{3-4}  & & **Fall** & **Daily Activities** \\ \hline \multirow{3}{*}{Actual} & Fall (n = 254) & 250 & 4 \\ \cline{2-4}  & Daily activities & 5 & 350 \\ \cline{1-1} \cline{2-4}  & & & \\ \cline{1-1} \cline{2-4}  & & & \\ \cline{1-1} \cline{2-4}  & & & \\ \cline{1-1} \cline{2-4}  & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Confusion matrix for validation dataset.
## 4 Experimental Validation and Result Analysis
### Processing of Experimental Data
#### 4.1.3 Feature Fusion and Failure Threshold  
As shown in Table 5, the Bearing failure times detected using the 3\(\sigma\) threshold setting method and the RMS threshold setting method [8], respectively, are compared with the actual operational times. It can be observed from Table 5 that the failure points (The unit is \"min\") detected by the 3\(\sigma\) method for the three bearing datasets are 1330, 673, and 323, respectively, the RMS method for the three bearing datasets are 1414, 688, and 328, respectively, while the actual operating times of the bearings are 2311, 701, and 434. The detected failure times are earlier than the actual operating times of the bearings; however, the 3\(\sigma\) method detects bearing failure earlier than the RMS method. This detection method can identify the degradation state of the bearings in advance, avoid missed detections, and guide staff to prepare preventive measures in advance.  
Figure 10: Ranking of weighted averages corresponding to different features.  
Figure 12 shows the HI curves and failure thresholds for the three types of data, the unit of the time step is minute. It can be observed that, although the operating conditions in Figure 12a,b are the same, their HI curves and failure thresholds are different. This is due to the fact that, despite using the same model of bearings for the same operating conditions, the manufacturing and processing of individual bearings are different, which leads to different results. The operating condition presented in Figure 12c is different from those presented in Figure 12a,b. This results in different operating times, HI curves, and failure thresholds.  
Figure 11: Selected feature indicators.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Bearings**} & **3\(\sigma\) (Failure Points** & **RMS (Failure Points** & **Actual Operating** \\  & **(min)**) & **(min)** & **(min)** \\ \hline
1-3 & 1330 & 1414 & 2311 \\
2-6 & 673 & 688 & 701 \\
3-3 & 323 & 328 & 434 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Detection of failure points by the 3\(\sigma\) and actual failure points.
## 2 Residential BESS without RES in ToU Tariff  
The residential BESS to be considered consists of 15 lithium iron phosphate battery cells (Model: GBS-LFP100 Ah-A, Zhejiang GBS Energy Co., Ltd., Shanghai, China) connected in series. The nominal voltage of the GBS-LFP100 Ah-A battery is 3.2-3.4 V and the nominal capacity 100 Ah. Consequently, the nominal voltage of the 15-lithium iron phosphate battery-stack is 48-51 V, the capacity is 100 Ah and the overall power capacity is 4.8-5.1 kW.  
The application which is going to be explored considers the operation of the aforementioned BESS without RES at a household in Greece where the energy pricing environment is ToU. The performance of the BESS is explored by taking into account different household power consumption profiles according to the four seasons of the year. Moreover, it is taken under consideration that the ToU tariff in Greece is not identical through all the year. During spring and summer, the tariff is 0.078 \(\upxi\)/kWh from 11 p.m. to 7 a.m. and 0.11 \(\upxi\)/kWh from 7 a.m. to 11 p.m. (Figure 1a). On the contrary, during autumn and winter the tariff is 0.078 \(\upxi\)/kWh from 2 a.m. to 8 a.m. and from 3 p.m. to 5 p.m. while the tariff is 0.11 \(\upxi\)/kWh from 8 a.m. to 3 p.m. and from 5 p.m. to 2 a.m. (Figure 1b).  
Owing to the absence of RES, the BESS is charged from the grid when the tariff is 0.078 \(\upxi\)/kWh and discharges to the house when the tariff is 0.11 \(\upxi\)/kWh. BESS supplies power to the household in parallel operation with the grid in order to guarantee uninterrupted supply and ensure the absence of dead time during transition (Figure 2). Furthermore, the parallel operation of the BESS with the grid secures that the excessively high instantaneous loads which cannot be covered by the BESS itself due to current limitations will be covered.  
The BESS comprises the 15 lithium-ion battery stack and the battery management system (BMS) which contains the EMSs. BMS retrieves day-ahead power consumption profile and ToU tariffs from the data repository. Thereafter, predicts and applies the optimal charging/discharging operation schedule to the battery stack in order to prevent premature degradation of the batteries and secure high economic profit. Over and above, it supervises the whole operation of the system, repossesses updated power consumption profiles and updates the optimal charging/discharging operation schedule.  
Figure 1: ToU tariffs: (**a**) spring and summer tariff; (**b**) autumn and winter tariff.  
Figure 2: Residential BESS without RES.