esa-sceva/satcom-chunk-collection huggingface dataset:
# Intersection Routing Based on Fuzzy Multi-Factor Decision for VANETs
## 3 Intersection Routing Based on Fuzzy Multi-Factor Decision  
In this section, the proposed routing protocol is discussed in detail. The proposed protocol is further divided into two parts: (1) vehicle decision and (2) intersection decision. In the vehicle decision, the routing is performed based on the vehicle routing between two adjacent SNs. However, in the intersection decision, the routing is decided based on the SN routing.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Routing Protocol** & **Type** & **Sub-Type** & **Map-Based** & **Intersection Based** & **Traffic Aware** \\ \hline AODV & Topological & Reactive & No & No & No \\ GPSR & Geographic & Non-DTN & No & No & No \\ GeOpps & Geographic & DTN & Yes & No & No \\ iCAR & Geographic & Non-DTN & Yes & Yes & Yes \\ iCAR-II & Geographic & Non-DTN & Yes & Yes & Yes \\ TAROC & Geographic & Non-DTN & Yes & Yes & Yes \\ AFMADR & Geographic & Non-DTN & Yes & Yes & Yes \\ FL-DGR & Geographic & DTN & Yes & No & Yes \\ IRFMFD & Geographic & Non-DTN & Yes & Yes & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of related routing protocols.
## 3 Materials and Methods
### Cognitive Workload Monitoring System  
The DGNN model is deployed on an Ubuntu server (version 20.04) as part of the cognitive workload monitoring experiment (CWME). The experiment involves four virtual users, represented by laptops, whose EEG data is transmitted wirelessly to Raspberry PiZero W modules. These modules, selected for their affordability, compactness, and wireless capabilities, establish connections via Wi-Fi to receive real-time EEG data. Each virtual user sends their EEG data to their designated Raspberry Pi module, which functions as a gateway. Operating on a legacy 32-bit OS installed on a 32 GB memory card, these modules transmit live data to the Ubuntu server. While these modules can serve multiple virtual users simultaneously, in this scenario, each module is dedicated to serving a single virtual user. The DGNN model undertakes critical classification tasks, leveraging its advanced neural network architecture for efficient analysis.  
The Ubuntu server takes responsibility for processing cognitive workload data and transmit it to Things Board for live monitoring. As a monitoring platform, Things Board presents various parameters such as latency, DGNN classification time, and workload type. To efficiently manage historical data, integration with a PostgreSQL database is employed due to its support for unstructured information. Figure 8 illustrates this four-layer process within a unified framework, showcasing the seamless flow of data from virtual users to the monitoring interface.  
Communication across the layers is facilitated by the constrained application protocol (CoAP). It follows a pattern where data move from virtual users (Layer 1) to Raspberry Pi modules (Layer 2) in a one-to-one manner. Then, the data move from the Raspberry Pi modules to the Ubuntu server with the deployed DGNN model (Layer 3), which operates in a many-to-one fashion. Finally, the data are sent from the Ubuntu server to the Things Board module (Layer 4) in a one-to-one manner.
## 5 Conclusions  
To summarize, we introduce a data-based filter, namely the basic SSA filter, to femtosecond laser absolute range finders for significant reduction of measurement uncertainty in the conditions of irregular moving targets. We verify the effectiveness of the basic SSA filter both in numerical simulation and in an absolute ranging experiment based on a pair of free-running femtosecond lasers. The simulation shows that the basic SSA filter is capable of reproducing a target trajectory that is significantly degraded by a white Gaussian noise. The priori knowledge on the dynamic behavior and the noise characteristics is not required. In the experiment, we successfully separate the signal and noise components by applying the basic SSA filter. We show that Gaussian white noise is the dominant noise process in an absolute range finder based on two free-running femtosecond lasers. By eliminating the noise components, the range finder shows 110-nm measurement uncertainty at a 200-Hz update rate for an irregularly-moving state. This method can be potentially used in a number of ASOPS-based high precision measurement fields. Firstly, the capability of separating various noise and signal components allows basic research on the noise characters in the femtosecond laser ranging system and shines light on the improved femtosecond laser range finder design. Secondly, this method enables a variety of industrial dynamic and vibrational measurements based on femtosecond laser  
Figure 6: Experimental noise extracted by the basic SSA filter (**a**) and its PSD (power spectral density) (**b**).  
Figure 7: Spectral power density (**a**) and Allan deviation (**b**) of the distance data.  
range finders. Finally, this method can also be used for LiDAR and remote sensing applications, where the basic SSA filter can be used for target identification from point clouds obtained by scanning femtosecond LiDAR.  
Conceptualization, Y.S. and H.C.; Methodology, Y.S. and H.C.; Software, H.C.; Validation, R.L., Y.L., H.S. and J.Y.; Writing--Original Draft Preparation, Y.S. and H.C.; Writing--Review & Editing, Y.S. and H.C; Supervision, Y.S. and C.W.; Funding Acquisition, Y.S. and M.H.  
This research was funded by Tianjin Natural Science Foundation, Grant Number 18 JCYBJC16900; the National Natural Science Foundation of China (NSFC), Grant Numbers 61475162, 61675150, 61535009; the Tianjin Research Program of Application Foundation and Advanced Technology, Grant Number 17 JCJQJC43500.  
The authors declare no conflict of interest.
# Data Anonymization for Hiding Personal Tendency in Set-Valued Database Publication
## 4 Proposed Method
### Procedure of Full Swapping  
A record in set-valued databases may contain arbitrary item \(i\in I\). Therefore it is necessary to consider a case if individuals have several items from one category only, indicating that the data subject has a very strong tendency to one specific thing. In such a situation we consider to devise an additional algorithm to hide the individual tendency. Still, we emphasize to avoid excessive record distortion.  
Before executing Algorithm 4, a set \(I_{swap}\) of items are selected as \(I_{swap}=\{t_{k}\in\mathcal{D}\}\). Next, a destination record selection is performed by Algorithm 4 as follows. To achieve the goal, we initially calculate the length of each \(t_{k}\). For each \(t_{k}\) satisfying \(|t_{k}|>1\) and containing only one item category \(\#g=1\), append the \(t_{k}\) as \(ct_{k}\) in a bucket \(Ct\). The next step is for each \(ct_{k}\in Ct\) compute \(jc\) between the \(t_{k}\) and \(ct_{k}\). Select the \(ct_{k}\) as the selected destination record \(t_{k}^{\prime}\) which results in the minimum \(jc\). Once the \(t_{k}^{\prime}\) is determined, swap all the items in \(t_{k}\) to \(t_{k}^{\prime}\). When the \(|Ct|=0\) which means there are no records satisfying the selection condition of record, full swapping is not possible.  
```
INPUT : \(\mathcal{D}\), \(P\), \(I_{swap}\) // P is a set of pairs (\(t_{k},t_{k}^{\prime}\)) OUTPUT : \(\widehat{\mathcal{D}}\);  create Buffer \(B_{t_{k}}\) and \(B_{t_{k}^{\prime}}\); foreach\(P\)do \(B_{t_{k}}\leftarrow\{i\in g_{j}^{\prime}|g_{j}^{\prime}\in t_{k}\}\); \(B_{t_{k}^{\prime}}\leftarrow\{i\in g_{j}^{\prime}|g_{j}^{\prime}\in t_{k}^{ \prime}\}\);  append \(B_{t_{k}^{\prime}}\) to \(t_{k}\);  append \(B_{t_{k}}\) to \(t_{k}^{\prime}\);  save \(t_{k}^{\prime}\) and \(t_{k}\) to \(\widehat{\mathcal{D}}\);  end foreach end
```  
**Algorithm 3**Procedure of items swapping.  
```
INPUT : \(\mathcal{D}\); OUTPUT : \(P\) // P is a set of pairs (\(t_{k},t_{k}^{\prime}\)) Read \(\mathcal{D}\);  Set \(C_{t}\) as an empty set; forall\(t_{k}\in\mathcal{D}\)do  compute the \(|t_{k}|\); if\(|t_{k}|>1\)and\(\#_{\mathcal{G}}\in t_{k}=1\)then \(ct_{k}\gets t_{k}\);  append \(ct_{k}\) to \(Ct\);  end foreach\(ct_{k}\in Ct\)do if\(ct_{k}\) and \(t_{k}\) have the same tendencythen  remove \(ct_{k}\) from \(Ct\);  calculate \(jc(t_{k},ct_{k})\);  end if  end for  select \(ct_{k}\) which has the minimum \(jc\) out of \(C_{t}\); \(t_{k}^{\prime}\gets ct_{k}\);  end for
```  
**Algorithm 4**Procedure of destination record selection for full swapping.
###### Abstract  
Developing simple and cheap electrocatalysts or photocatalysts for cathodes to increase the oxygen reduction process is a key factor for better utilization of microbial fuel cells (MFCs). Here, we report the investigation of natural wolframite employed as a low-cost cathode photocatalyst to improve the performance of MFCs. The semiconducting wolframite was characterized by scanning electron microscopy (SEM), X-ray diffraction (XRD), and Raman spectroscopy. The band gap and photo respond activities were determined by UV-vis spectroscopy and linear sweep voltammetry (LSV), respectively. Compared with the normal graphite cathode, when MFCs were equipped with a wolframite-coated cathode, the maximum power density was increased from 41.47 mW\(\cdot\)m\({}^{-2}\) to 95.51 mW\(\cdot\)m\({}^{-2}\). Notably, the maximum power density further improved to 135.57 mW\(\cdot\)m\({}^{-2}\) under light irradiation, which was 2.4 times higher than with a graphite cathode. Our research demonstrated that natural wolframite, a low-cost and abundant natural semiconducting mineral, showed promise as an effective photocathode catalyst which has great potential applications related to utilizing natural minerals in MFCs and for environmental remediation by MFCs in the future.  
microbial fuel cells (MFCs); natural wolframite; photocatalyst; wastewater treatment +
Footnote †: journal: Applied Sciences
## 3 Sensorless FOC with Luenberger-Type Back-EMF Observer
### Sensorless FOC  
FOC is a common form of vector control. It is a cascade control structure with an inner current and outer speed loop. The control loops placed in series represent feedback in a closed-loop system. However, a transformation of the controllable motor quantities, such as current and voltage, from the stationary _abc_ to the rotational reference system _dq_ must be conducted. In the _dq_ coordinates, these variables become DC values. Thus, simple controllers can control such quantities, as shown in Figure 4.  
PI controllers are commonly used to maintain demanded behavior by minimizing the error between required and feedback variables. The parameters of these controllers, more specifically proportional and integral gain, have to be correctly calculated to adjust the dynamics of the whole system.
## 3 Long-Wave Infrared Optical System Design  
In order to meet the requirements of real-time dynamic imaging recognition of natural objects and artifacts, metal and non-metal targets in physical evidence searches, and battlefield camouflage target recognition, this paper designed a long-wave infrared imaging optical system based on micropolarization, and the imaging resolution was determined as 0.25m @ 1 km.  
The uncooled long-wave infrared focal plane detector from China Yantai Airway Optoelectronics Co., Ltd. (Yantai, China) was selected. The specific parameters are shown in Table 1.  
Aiming at the imaging requirement of 0.25 m target size at 1 km of infrared imaging, according to Johnson criteria, the target should occupy at least 2 \(\times\) 2 pixels on the target surface of the detector, then:  
\[\frac{2\mathrm{d}}{f}=\frac{\Delta}{L} \tag{9}\]  
Object size is \(\Delta=0.25\) m, the imaging distance is \(L=1\) km, and the detector image size is \(\mathrm{d}=12\)\(\upmu\)m. Substituting it into Formula (9), the system's focal length \(f=96\) mm.  
To ensure good imaging quality, the \(F\)-number is set to 1.2. According to Formula (10), the aperture of the optical system is 80 mm.  
\[D=\frac{f}{F} \tag{10}\]  
\(x\) is the diagonal length of the target surface of the detector, and \(f\) is the system's focal length. According to Formula (11), the field angle of the optical system is 9.17\({}^{\circ}\).  
\[\theta=2\mathrm{arctan}(\frac{x/2}{f}) \tag{11}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Numerical Value** \\ \hline resolution ratio & 1024 \(\times\) 768 \\ pixel size & 12 \(\upmu\)m \(\times\) 12 \(\upmu\)m \\ spectral response range & 8–14 \(\upmu\)m \\ heat sensitivity & \(<\)50 mk \\ operating temperature range & \(-\)40–85 \({}^{\circ}\)C \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specific parameters of the detector.  
The optical system indicators obtained by calculation are shown in Table 2.  
The optical transmission structure is adopted according to the imaging requirements. The two-dimensional structure of the optical system is shown in Figure 1a. As shown in Figure 1b, the MTF (Modular Transfer Function) curve of the system indicates that even at 30 line pairs (corresponding to a pixel size of 12 \(\upmu\)m), the image quality of each zoom position is close. The MTF value of each position is more significant than 0.1, and the central field of view is about 0.5-0.6, which proves that the system has excellent image quality and an image with high resolution.
# The Domain Mismatch Problem in the Broadcast Speaker Attribution Task
## 4 Methodology
### The Direct Assignment Approach  
The first proposal follows a traditional out-of-the box speaker recognition architecture. This architecture fits the diagram block from Figure 2 except for the clustering stage, which is not applied. Additionally, the identity assignment block follows the previously described speaker recognition philosophy. In order to explain it in detail we present Figure 3, which illustrates its functionality. Given the analysis audio, we individually assign an identity, enrolled or generic unknown, to each embedding \(\mathbf{\phi}_{i}\in\mathbf{\Phi}\) representing an audio segment. During the assignment process, each score \(s_{ij}\) stands for the similarity between the individual embedding \(j\) from \(\mathbf{\Phi}\) compared to the enrollment speaker \(i\). Moreover, the role of the calibration subset \(\mathbf{\Phi}_{\text{calib}}\) is played by those embeddings from the development subset. This same calibration is also applied to the test subset. The detailed flowchart is illustrated in Figure 4. Given the audio under analysis \(\Omega\), we perform its feature extraction, segment the estimated information (VAD and SCPD stages are involved) and extract the set of embeddings per segment (red box). These embeddings are compared to those obtained from the enrollment audios \(\Omega_{entroll}\) (yellow box), processing the features for the whole recording. Both sets of embeddings are fed into the identity assignment block to obtain the final labels.  
Figure 3: Diagram of the direct assignment approach. The embeddings obtained from the different parts of the given audio are independently assigned to the identities. These assignments can be done to enrolled identities or the generic unknown one (red arrow).
## 3 Data Set  
### Normalized Difference Vegetation Index (NDVI)  
NDVI provides an index of ecosystem function since it has proven to be a proxy for the status of the aboveground biomass at the landscape level due to the high correlation with green-leaf density, net primary production and the fraction of photosynthetically active radiation absorbed by green biomass and CO\({}_{2}\) fluxes [46].  
The 16-day Terra MODIS (MOD13A2) NDVI product at 1 km spatial resolution has been used. MOD13A2 ingest level two daily surface reflectance product (MOD09 series), which provides red and near-infrared surface reflectance corrected by the effect of atmospheric gases, thin cirrus clouds and aerosol [47, 48]. The MOD13A2 vegetation product contains a data quality assessment (QA data) product holding information on overall usefulness and cloud conditions on a per-pixel basis [49].
# An Efficient Satellite Resource Cooperative Scheduling Method on Spatial Information Networks
## 5 Numerical Experiment  
### Algorithm Complexity Analysis  
The resource coordination scheduling method of a spatial information network is mainly divided into two parts: the dynamic allocation of network resources and the maximum flow allocation of inter-satellite link resources. The time complexity of adding super source nodes and super sink nodes in the process of network resource dynamic allocation is \(O(s_{source}+s_{target})\), and the time complexity of the Ford-Fulkerson algorithm is \(O(Ef)\). We use approximate algorithms to solve the minimum cost calculation results  
Figure 6: Satellite resource request queue reconstruction and inter-satellite resource coordination.  
of multiple resource types under multiple sinks and multiple source conditions. The time complexity is only related to the scale n of the input example, and it varies with the parameters \(\varepsilon\). Therefore, the time complexity of \(PTAS\) ranges from \(O(n^{1/\varepsilon})\) to O(\(n^{exp(1/\varepsilon)}\)). According to the above analysis, the time complexity of Algorithm 1 is \(O(s_{source}+s_{target}+Ef+n^{1/\varepsilon})\) or \(O(s_{source}+s_{target}+VE^{2}+n^{exp(1/\varepsilon)})\), and the final time complexity is \(O(Ef+n^{1/\varepsilon})\) or \(O(Ef+n^{exp(1/\varepsilon)})\). As with Algorithm 1, Algorithm 2 first adds virtual nodes for the source and sink points with the time complexity of \(O(s_{source}+s_{target})\) and then solves the maximum flow of the network. The time complexity of the algorithm Ford-Fulkerson is \(O(Ef)\). Finally, satellite resources are allocated according to the traffic on each edge. We call the algorithm proposed in this paper \(RQRCS\), then the best time complexity of \(RQRCS\) is \(O(Ef)\), and the worst-case time complexity is \(O(Ef+n^{exp(1/\varepsilon)})\).  
\(RQRCS\) proposes the algorithm with the smallest time-complexity among all algorithms when the maximum flow \(f\) is small in the paper. When the maximum flow value \(f\) is relatively large, then the advantages of this method will be significantly reduced. However, influenced by the spatial information network, the number of satellites that can establish a network with adjacent satellites at a specific moment is small, and the amount of data transmitted by the inter-satellite link is also minimal. Therefore, we do not need to consider the high time complexity caused by the large scale of the spatial information network. We address the problem of the local small satellite network in resource request queue reconstruction and satellite coordinated allocation.
# Coordination of Congestion and Awareness Control in Vehicular Networks
## 4 Evaluation
### Congestion and Awareness Control
The variability shown in Figures 7 and 8 for the packet  
Figure 6: Time evolution of the \(CBR\) and the \(D_{p}\) metric during a simulation run. The results are shown for two vehicles that implement INTERN (integrating A-DCC and MINT), and COMPASS (coordinating A-DCC and MINT), and a traffic density of 75 veh/km/lane. transmission frequency and power utilized when implementing INTERN, MINT or COMPASS is due to the fact that the application requirements for each vehicle have been randomly selected so that the study is not restricted to a particular application. In this case, INTERN, MINT and COMPASS adapt the configuration of the communication parameters to the application requirements, which explains the variability observed. However, it is important remembering (Figure 6) that the _CBR_ and _Dp_ metrics converge when implementing COMPASS and INTERN. A very different behavior is observed for A-DCC. First of all, A-DCC does not adapt the transmission power and all vehicles use the same power level (Figure 7a). A-DCC adapts the packet transmission frequency based on the channel load and not on the application requirements; in particular, the packet transmission frequency is adapted so that similar values are utilized by neighboring vehicles. This explains why we do not observe a scattered plot in Figure 8a even though the application requirements were also randomly selected with A-DCC.
## 1 Introduction
The distance algorithm in the traditional TOPSIS method is improved, and Mahalanobis distance is adopted to replace Euclidean distance, which can avoid the juxtaposition of evaluation results and effectively overcome the misjudgment caused by the correlation between PQ indices. Due to the weighting being the precondition of PQ evaluation, this paper also proposes a new AHP-EW weighting method based on the combination of improved analytic hierarchy process (AHP) and entropy weight (EW) method to calculate the subjective and objective weights of indices. Compared with the traditional subjective and objective weighting methods, this method can consider the subjective and objective factors at the same time, and greatly reduce the amount of calculation, with strong operability. Finally, the statistical values of PQ monitoring data are evaluated by an example, and the results are compared with the traditional methods to verify the validity and correctness of the proposed algorithm. Figure 1 shows the improved process of the TOPSIS method. It can be seen that the EW-AHP is used to replace the existing single weighting method, and the objective weight of the original data is obtained by EW. The subjective weight can be obtained by AHP. The combination of the two can take into account the subjective and objective weights at the same time. The Mahalanobis distance replaces the Euclidean distance and eliminates the impact of the correlation between the evaluation indices on the evaluation results. It should be noted that the evaluation method proposed in this paper is universal. It can be widely used in the evaluation of multiple targets with multiple indices, as shown in Figure 2, including energy efficiency evaluation, service satisfaction evaluation, risk evaluation and so on. Next, the paper will apply the proposed method to PQ evaluation to verify its effectiveness.
# Wireless May Benefit Blockchain +
### Contributions  
As has been articulated in Section 1.2, most of the previous work in the literature on blockchain and distributed systems presents experimental results, which can only be applied to certain practical scenarios with a set of specific parameter settings. Moreover, no prior work directly shed light on the fundamental impacts of uncertainty induced by intermittent connections, which becomes the central challenge in a blockchain network formed among wireless nodes. Therefore, this paper attempts to provide a more general framework that can be applied to any other blockchain for the measurement of their consensus performance.  
Addressing the limitations mentioned above in the existing literature, this paper is dedicated to answering the following research question: How will wireless connectivity affect the performance of blockchain consensus? It aims at accurately evaluating the performance of consensus mechanisms when the nodes are wirelessly connected. We also claim that this work is an extension of the author's previous work [34]. The specific technical contributions are summarized as follows:  
* It provides an analytical framework for calculation of scalability and decentralization of a blockchain consensus process.
* It quantifies the impacts of wireless connections on the scalability and decentralization by adopting a probabilistic analysis.
* It presents a comparative study among PoW, PoS, vs. PoC with respect to scalability and decentralization.