esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Simulation Results
### Simulation Results of the Voltage Drop at the 60 kV Bus  
The main purpose of this test is to study how a remote grid fault from the PCC may affect the operation of a wind farm; this fault affects the source of 60 kV, which is far from the PCC where the wind farm based on the DFIGs is connected to the grid. Hence, a temporary voltage drop of 50% is  
Figure 15: (**a**) Reactive power at the PCC during line-to-line electrical grid fault; (**b**) zoom of reactive power at the PCC during line-to-line electrical grid fault.  
applied to the source for the duration of 500 ms at \(t=10\) s, and then the same grid fault for 1000 ms of duration.  
Figure 16 shows that during this type of grid fault and without the presence of D-FACTS, the voltage at the PCC drops to 0.44. Therefore, the protection system will be triggered and the wind farm will be disconnected if the fault duration exceeds the electrical interconnection grid code for the wind turbine systems (see Figure 2). However, in the same figure, it is shown that during this grid fault and with the presence of D-FACTS systems, the voltage at the PCC is maintained around 0.88 pu with a transient peak without triggering the protection system. Thus, by using the D-STATCOM, transient peaks are reduced and the time response is faster than by using the D-SVC.  
The active powers at the PCC are presented in Figure 17.  
Figure 17 shows that, when the D-FACTS devices are not installed at the PCC, the wind farm cannot maintain its connection to the grid during the grid fault that lasts 1000 ms because the protection systems are triggered and the wind farm is disconnected. However, after the installation of D-FACTS devices and with the same grid fault type and the same duration of grid fault, the wind farm can return to the steady state and inject the active power of 10.6 MW to the grid.  
From the results shown in Figure 18, it is noticed that without the presence of a compensation system, the wind farm is operating in a weak electrical grid due to its normal behavior and there is no reactive power exchange with the electrical grid. However, in the same situation with the D-FACTS devices, the necessary reactive power is provided to the grid of 12.6 MVAr with D-SVC and 9.7 MVAr with the D-STATCOM. Thus, a very fast and significant fluctuation is observed with the use of the D-SVC compared to the use of the D-STATCOM.  
Figure 16: Voltages at the PCC during the voltage drop at the 60 kV bus.
## 4 Experiments
### Experimental Results
#### 4.3.5 Error Analysis of DeepSeek-R1  
As highlighted in Section 4.3.1, most LLM-based vulnerability detectors, including DeepSeek-R1 and O3-Mini, show relatively weak performance on certain categories such as CWE-22 and CWE-79. To further investigate the limitations of these models, a detailed error analysis is conducted using DeepSeek-R1 as the representative reasoning model.  
One of the most illustrative failure cases is observed in CWE-22. A representative vulnerable sample _path_basic_bad.c_, is shown in Figure 14.  
This simple C program reads a file path from user input and attempts to open the specified file without any path validation or sanitization. Such code is inherently vulnerable to path traversal attacks, where malicious input (e.g., _../etc/passwd_) can be used to access unauthorized system files. This type of vulnerability typically occurs when a program allows users to influence file paths but fails to restrict the path scope appropriately.  
Figure 13: Representative reasoning trace by DeepSeek-R1 on CWE-787 (ID: 231378).  
while DeepSeek-R1 can simulate a structured analysis process, it may lack domain-specific knowledge for certain vulnerability types.  
A similar trend is observed with CWE-79, where the model struggles to detect vulnerabilities involving improper input neutralization during web page generation. These issues often result in unauthorized exposure of sensitive data or script injection risks. For example, if user input is directly embedded into HTML content without unauthorized content, attackers may gain access to restricted resources. This issue is related to the exposure of the resource to the wrong sphere.  
This failure may be attributed to two main factors: (1) a possible lack of domain-specific training examples in the model's training steps. (2) Insufficient emphasis on secure coding practices during the instruction tuning process. In particular, there is a lack of security risk awareness and adversarial robustness training, both of which are critical for equipping models with the ability to recognize subtle yet dangerous input-driven vulnerabilities in real-world applications.  
Overall, these findings suggest that while models like DeepSeek-R1 exhibit general reasoning abilities, they still struggle with subtle, domain-specific security issues. Enhancing detection performance in these cases may require task-specific fine-tuning, enriched datasets, and integration of security design patterns into the model's training framework.
# Blockchain-Based Federated Learning: A Survey and New Perspectives
## 3 Blockchain vs. Federated Learning
### Drawbacks of Federated Learning
#### 3.2.3 Robustness and Efficiency  
* **Single-point failure**: FL relies on a central server that is vulnerable to malicious activity, causing global model updates to fail [50]. Moreover, if the central server is compromised, the entire system faces a collapse.
* **Barrier of defense**: Due to the lack of clear attack standards, FL frameworks lack defensive capability and are vulnerable to attacks, resulting in model updates being tampered by malicious agents. Likewise, FL lacks the ability to backtrack malicious clients, and the existence of malicious clients can also lead to model performance degradation and even training failure.
* **Not censoring**: Most existing federated learning systems are combined with centralized coordinators without providing any clear transparency and source mechanism for the generated models.
* **Robust performance**: Malicious or lazy devices in FL may migrate fake models or refuse to share models for profit, reducing the efficiency and reliability required for federated learning systems.
* **Network overload**: Federated learning (FL) is a decentralized learning method that breaks away from the traditional centralized learning. FL learns locally on each device and incrementally improves the learning model through interaction with a central server. However, it causes network overload due to the limited communication bandwidth and the participation of a large number of users [51].
# RCM: A Remote Cache Management Framework for Spark
## 5 Experimental Results and Analysis
### Experimental Setup  
In order to validate the effectiveness of RCM during jobs execution, RCM is implemented on Reids as a service of remote cache. The Spark platform will run benchmark jobs to test performance of RCM. The experiment starts eight computing nodes and four cache servers. The configuration of the experiment environment is shown in Table 1. HDFS is chosen as the database service. The benchmark jobs include PageRank, K-means and WordCount. The benchmark workloads of jobs are shown in Table 2. The main metric of the experiment is the job execution time. Provided by SNAP [34], three standard datasets, Web-BerkStan, Web-Google and Cit-Patents, are selected. To increase the pressure on cache resources, Web-BerkStan, Web-Google and Cit-Patents are expanded to 500 GB, 1 TB and 2 TB, respectively.
## 3 Results and Discussion
### Microbial Analysis
#### 3.3.2 Microbial Composition  
The Venn diagrams at the OUT level and abundance plots of microbial composition at the genus level are presented in Figure 2. It was evident that the bacterial OTUs in the Q2 samples were much greater than those in the other two samples, whereas the difference in fungal OTUs among all samples was minor, ranging from 110 to 135, which was in line with [PERSON]'s research results [13]. The microbial composition and abundance of all Xiaoqu samples were distinct at the genus level. The microbial species composition of Q1 and Q3 starter (0 d) was similar, with _Saccharomycescoposis_ and _Rhizopus_ being the predominant fungal species, and _Pediococcus_, _Weissella_, and _Enterobacter_ being the primary bacterial species. During the fermentation process, the diversity of bacteria decreased, with _Pediococcus_ being absolutely dominant, while the diversity of fungi increased (Q1: _Saccharomycescoposis_ and _Wickerhamomyces_); Q3 also showed the same trend. It is evident that _Pediococcus_ was more suitable for growth and reproduction in black rice, as it utilized the nutrients in black rice to produce a large number of organic acids and bacteriocins, thereby inhibiting the growth of other bacteria and achieving \"self-purification\" [22; 23]. Compared to the other two starters, the microbial species composition of Jiangxi Yingtan Xiaoqu was notably dissimilar, mainly composed of _Rhizopus_, _Wallemia_, _Clacispora_, _Bacillus_, and _Enterobacter_. Interestingly, the fermentation process was mainly characterized by three yeasts: unclassified_f_Metschnikowiaceae, _Clacispora_, and _Wickerhamomyces_, which mainly proliferated during the alcohol fermentation stage. Meanwhile, _Enterobacter_, _Bacillus_, _Achromobacter_, and _Pediococcus_ were the bacteria most commonly found during this process. First observed in Chinese rice wine, unclassified_f_Metschnikowiaceae is a type of non-Saccharomyces yeast that can effectively enhance the flavor of red wine [24]. Additionally, it had a notable advantage over other yeasts when it came to the brewing of BRW.  
Dominant microorganisms with high abundance are believed to be major parts of food fermentation which are essential to the food fermentation process [25]; they are usually identified by having an average abundance of over 1% and existing in 50% of samples [16]. Table S3 shows that the dominant microbial communities of different product groups were not identical, yet there were similarities--_Pediococcus_, _Saccharomycescoposis_, and _Wickerhamomyces_ were present in the dominant microorganisms of both Q1 and Q3, while two dominant microbes of Q3 were _Weissella_ and _Cyberlindera_. Additionally, two dominant microorganisms, _Pediococcus_ and _Wickerhamomyces_, were present among the three Xiaoqu samples.
# Corner Centrality of Nodes in Multilayer Networks: A Case Study in the Network Analysis of Keywords
## Appendix A Computational Time  
The analysis carried out below assumes that the information on the neighbors of a particular node is obtained in lineal time \(n\). \(n\) is the number of nodes in each layer.  
The information _about_ the node's neighbors is needed in Equations (1), (2), and (10).  
To obtain the computational time of Algorithm 1, we analyzed the computation time in every step.  
```
1:for every node \(u\) in \(L_{*}\), do
2:for every layer \(L\) in G, do
3:if\(u\) is in \(L\), then
4: obtain \(I_{L}(u)\) by using Equation (1) \(\rhd\) by using intralinks within L.
5:else, if\(u\) is not in \(L\), then
6: obtain \(I_{L}(u)\) by using Equation (2) \(\rhd\)by using interlinks between \(L_{*}\) and \(L\) layers.
7:endif
8:endfor
9:endfor
10:for every layer \(L\) in G, do
11: Normalize \(I_{L}\)
12:endfor
13:for every node \(u\) in \(L_{*}\), do
14: obtain the matrix \(M(u)\) by using Equation (9). \(\rhd\)with only two layers by using Equation (10)
15: obtain the score of the node \(S_{L*}(u)\rhd\)by using Equation (12)
16:endfor
17: Obtain the rank of every node in \(L_{*}\) by sorting \(S_{L*}\)
```  
* In steps 1-8, the computational time is calculated by:  
\[\sum_{i=1}^{n}\sum_{j=1}^{M}n=n^{2}\times M\]  
where \(n\) is the number of nodes in the layer \(L_{*}\), and \(M\) is the number of layers. We have assumed that each layer has the same number of nodes n. Within the second summation, we have \(n\), which is the time consumed by the search for the neighbors of a node in the jth layer:
* In steps 10-12, the computational time is \(n\times M\).
* In steps 13-16, the computational time is \(n\times(M^{2}+n+M^{2.373}+M)\). \(M^{2}+n\) is the time to create the matrix in step 14. \(M^{2.373}\) is the computational time to apply the determinant for a matrix \(M\times M\)[31]. Additionally, M is the computational time to obtain the trace for a matrix \(M\times M\).
* Additionally, in step 17, the computational time is \(n\times\log_{2}n\).  
Adding all the terms  
\[n^{2}\times M+2\times n\times M+\ n^{2}+n\times M^{2}+n\times M^{2.373}+\ n \times\log_{2}n \tag{1}\]  
As can be seen in Equation (1), the computational time is a function of the number of nodes in each layer (n) and the number of layers (M).  
In the case that \(M\ll n\), the computational time in the worst case is \(\mathrm{O}\big{(}n^{2}\times M\big{)}\). However, if \(n\ll M\), the computational time in the worst case is \(\mathrm{O}\big{(}n\times M^{2.273}\big{)}\). Usually, the number of layers is much lower than the number of nodes; therefore, the computational time is set according to the number of nodes.
# Forecasting Seasonal Sales with Many Drivers: Shrinkage or Dimensionality Reduction?
## 3 Methods
### Regression with AutoRegressive Integrated Moving Average Errors
#### 3.1.1 Univariate AutoRegressive Integrated Moving Average Models  
The ARIMA model family represents one the most widely used approaches to univariate forecasting, having demonstrated both flexibility in modelling a variety of data generating processes, and accurate forecasting. With ARIMA models time series are considered as three components, an AutoRegressive (AR) process, the Integration, and a Moving Average (MA) process, each responsible for capturing different aspects of the time series. First, the integration is responsible for ensuring that the time series is stationary, that is necessary for the AR and MA parts. This takes the form of differencing the time series as needed. Once the time series has become stationary, the rest of the modelling proceeds. The AR part models the target variable using lags of itself, and intuitively, in the context of retailing, handles habitual consumption. The MA part introduces lags of the errors of the model, which acts as self-correcting the predictions of the model. With ARIMA we can include no or several lags of either the target series or the errors, and similarly multiple differences, which are the orders of the model. Seasonal ARIMA models can be created by introducing seasonal lags for the AR and MA terms, and seasonal differencing for the integration. The reader can find details about the model at [57], Chapter 6, or [58].  
There are many alternative approaches to identify the orders of ARIMA models. We follow the procedure by [59], who have proposed an automatic procedure that has been shown to perform well for a variety of forecasting tasks. In brief, the order of differencing is identified using statistical testing, while the orders of AR and MA are identified using the Akaike Information Criterion corrected (AICc) for small sample sizes in a stepwise fashion. Although ARIMA models can become rather complex with several AR and MA terms, the use of information criteria in their specification helps to reject superfluous terms. Nonetheless, in modelling seasonality substantial data may be lost. For instance, if we introduce a seasonal AR lag, then we need to use \(m\) observations to construct this lag. A second lag doubles the amount of observations needed, and so on. The same applies for seasonal differences. Therefore, even though a well specified ARIMA may be economical in terms of coefficients to be estimated, it still requires a substantial sample size when seasonality is introduced. For weekly data, relevant to our case, \(m=52\), highlighting the potentially substantial reduction in sample.
## 5 Conclusions  
In this paper, we propose a multi-agent framework to address hallucinations in large language models. Our approach combines repetitive inquiries, adversarial debates, error logs, and voting mechanisms to reduce hallucinations and improve accuracy. We also introduce entropy compression techniques to optimize communication efficiency, minimizing both dialogue rounds and response time.  
The results demonstrate that our system significantly reduces hallucinations and enhances the reliability and efficiency of LLMs. The combination of self-correction mechanisms and cross-validation between multiple models improves consistency and robustness.  
While our experimental results demonstrate the effectiveness of the proposed approach, several limitations should be noted:  
1. Lack of multi-machine communication scenarios: Our experiments were conducted in a single-machine environment, and we did not account for the complexitiesof multi-machine communication, such as network latency, synchronization issues, or bandwidth constraints. These factors could significantly impact performance in real-world distributed systems.  
2. Failure and downtime scenarios: The current framework does not consider potential failures or the downtime of individual models or agents. In practical applications, fault tolerance and recovery mechanisms would be essential to ensure robustness and continuity.  
3. Limited dataset and task coverage: Due to time and resource constraints (27 h per batch), our evaluation was primarily focused on a subset of datasets and tasks (e.g., MMLU). A more comprehensive analysis across diverse datasets and tasks would strengthen the generalizability of our findings.  
4. Scalability concerns: The scalability of the proposed approach to larger-scale systems with hundreds or thousands of agents remains untested. Future work should explore the performance and efficiency of the framework in such scenarios.  
Addressing these limitations in future research will be critical to advancing the practical applicability and robustness of the proposed approach. Future work could explore incorporating more diverse models and datasets, as well as integrating human evaluators for further validation. Overall, our approach lays the foundation for building more trustworthy and efficient LLM-based systems in critical applications.  
Conceptualization, Y.Y., Z.H. and H.F.; methodology, Y.Y., Y.M. and H.F.; software, Y.M.; validation, Y.Y., H.F., Y.C. and Y.M.; formal analysis, H.F. and Y.M.; investigation, Y.M. and Y.C.; resources, Y.M.; data curation, Y.M.; writing--original draft preparation, H.F., Y.C. and Y.M.; writing--review and editing, Y.Y., H.F., Y.C. and Y.M.; visualization, Y.M.; supervision, Y.Y. and Z.H.; project administration, Y.Y. and Z.H.; funding acquisition, Y.M. and Y.Y. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflict of interest.
## 5 Results
### Placement Accuracy
To summarize, we can conclude that in an indoor environment, \"SiteVision (in Manuel placement), GAMMA AR, ARcore, and marker-less methods (model-based) reach an accuracy of less than 10 cm, which degrades to 19 cm using Vuforia or marker-based (no model-based) methods. The two commercial solutions \"SiteVision\" with its manual placement mode and \"GAMMA AR\" come out on top as being the most accurate placement methods. This degree of accuracy is explained by the fact that these two methods use quality manual placement to match the virtual model to reality. The ARcore marker-based method is more accurate than the other methods (8 cm). In outdoor environment, we note three classes of RMSE values. A first class (RMSE less than 10 cm) is obtained by commercial solutions: \"SiteVision\" with its manual placement mode and \"GAMMA AR\". The degree of accuracy achieved by these two methods is explained by the use of an exact manual adjustment like the indoor. The second class of RMSE of 10 cm to 40 cm is obtained from marker-based methods (ARcore and Vuforia) and marker-less methods (based or not on a 3D model). A third class with RMSE less than 10 cm combines two methods: SiteVision with its automatic placement mode and inertial sensors based on consecutive RMSE values of 3.47 m and 7.67 m. The placement accuracy of 7.67 m obtained by the inertial sensor-based method is due to the low nominal accuracy of the smartphone's inertial sensors. This RMSE value is somewhat improved with the use of SiteVision with its automatic placement mode. An RMSE of around 3 m with automatic placement by SiteVision is explained by using a medium-sized positioning receiver (Catalyst Trimble) and the low coverage of the RTX network (Figure 18). According to the results reported in Table 2 and Figure 18, we can conclude that the knowledge of the environment is a determining factor for the accuracy of placement of marker-less methods because computer vision algorithms in this category are easily able to accurately overlay and match pre-existing 3D models with reality.
## 3 Numerical Results  
The performance comparison is made between the drone VLC systems based on the above Lambertian and the non-Lambertian beams in this section, for the terrestrial user and the aerial user. Specifically, one typical medium-size target area, i.e., 5 m \(\times\) 5 m, is considered for drone-assisted VLC coverage. Additionally, by mainly inheriting from the published drone VLC work of ref. [13], the main simulation parameters are given in Table 1.  
In the following numerical analysis of the envisioned drone wireless coverage scenario, one ground corner is set as the origin of coordinates. And the representative center position with coordinates (2.5, 2.5, 0.85) m, the side position with coordinates (2.5, 0.5, 0.85) m, and the corner position with coordinates (0.5, 0.5, 0.85) m are chosen as the candidate terrestrial locations to configure different drone VLC conditions.  
Due to the rotational asymmetry described in Figure 3 for the NSPW LED beam, the additional side location 2, i.e., (0.5, 2.5, 0.85) m, is introduced for the following investigation  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Values** \\ \hline Target area size (W \(\times\) L) & \(5\times 5\) m\({}^{2}\) \\ Total available signal power of transmitter & 1 W \\ Number of transmitters & 1 \\ Location of transmitter & (2.5, 2.5, 3) m \\ LED Lambertian semiangle & 60\({}^{\circ}\) \\ Receiver field of view & 90\({}^{\circ}\) \\ Height of receiving plane & 0.85 m \\ Physical area of PD & 1.0 cm\({}^{2}\) \\ Responsively of PD & 0.8 A/W \\ Concentrator refractive index & 1.5 \\ Optical filter gain & 1 \\ Signal bandwidth & 10 MHz \\ VLC noise power density & \(1\times 10^{-22}\) A\({}^{2}\)/Hz \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main parameters configuration.  
of drone VLC based on the NSPW beam configuration. Similarly, for the aerial user of drone VLC, the two-dimensional coordinates of the aerial side position, the aerial side position 2, and the corner position are (2.5, 0.5) m, (0.5, 2.5) m, and (0.5, 2.5) m, accordingly. Since the envisioned aerial user is based on a drone platform as well, the relevant altitude, i.e., height coordinate for the above aerial receiver positions, could be varied between the height of the terrestrial receiver and the height of the transmitter drone.  
Moreover, following the previous system models for the distinct drone VLC systems, both the upward orientation receiver and the horizontal orientation receiver are considered in the investigated aerial user for drone VLC links. For clear comparison and helping readers better understand the differences, the spatial radiation characteristics and application domains of Z-Power and NSPW LED beams are presented in Table 2 as follows: