esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Multi-Source Anonymity for Aggregated Internet of Vehicles Datasets
###### Abstract
Methods that address location privacy preservation include enforcing anonymized data identities [17], location obfuscation [18], space transformation [19], and spatial anonymization [20]. [PERSON] et al. [21] presented the PrivacyGrid framework. It anonymizes location-based queries and gives effective algorithms. [PERSON] et al. [22] proposed ICIiqueCloak for location k-anonymity. However, protecting private data from multi-source aggregated data is complex. On the one hand, there are different types of private information, such as location, identity, and privacy information. In addition, data from many sensors are included, such as global positioning systems, radar, and cameras. Hence, many aggregated datasets have been generated from multi-source data. When information is aggregated, it is easy to leak the linked relationships. For example, an attacker can access the database of a vehicle administration office. Thus, he knows a vehicle's registration date, license plate, and color. Figure 1: Internet of Vehicles data interaction model. By observing the values in an anonymized aggregation dataset, an attacker can obtain accurate QI values from another data source. This creates new privacy disclosures in multi-source scenarios. However, current techniques must address this problem. After \(k\)-anonymity, some anonymized QIs of the data remain unchanged, as \(k\)-anonymity only entails the records in the QI group. This privacy disclosure differs from identity disclosure and attribute disclosure because it leaks more information about the attack objective, which is harmful. Considering the aggregation of datasets, it may differ from general privacy-preserving data publishing or location privacy. Therefore, we exploit this new attack and propose countermeasures. The main contributions of this study are as follows. * We propose a multi-source linkability attack by analyzing the problem of the aggregated dataset in IoV. * We proposed multi-source (\(k\)_d_)-anonymity and multi-source (\(k\)_l,d_)-diversity to protect privacy disclosure in IoV. The former prevents the acquisition of accurate quasi-identifier values when the attacker possesses background knowledge. The latter has a similar privacy capability and can protect sensitive attributes. In addition, we provide heuristically efficient algorithms. * We experimentally evaluated our algorithms using real datasets. The experimental results demonstrate that our algorithms perform well regarding privacy disclosure, information loss, and efficiency. In Section 2, we describe related works, including the k-anonymity model and data utility of anonymity. Section 3 provides an introduction to the aggregated dataset and new attacks, elucidating the motivation behind our research. Our novel algorithms are presented in Section 4, and Section 5 showcases the results with regard to privacy, utility, and efficiency. Finally, Sections 6 and 7 discuss and conclude the study.
###### Abstract  
The performance of the underwater acoustic (UWA) orthogonal frequency division multiplexing (OFDM) system is often restrained by time-varying channels with large delays. The existing frequency domain equalizers do not work well because of the high complexity and difficulty of finding the real-time signal-to-noise ratio. To solve these problems, we propose a low-complexity neural network (NN)-based scheme for joint equalization and detection. A simple NN structure is built to yield the detected symbols with the joint input of the segmented channel response and received symbol. The coherence bandwidth is investigated to find the optimal hyperparameters. By being completely trained offline with real channels, the proposed detector is applied independently in both simulations and sea trials. The results show that the proposed detector outperforms the ZF and MMSE equalizers and extreme learning machine (ELM)-based detectors in both the strongly reflected channels of the pool and time-variant channels of the shallow sea. The complexity of the proposed network is lower than the MMSE and ELM-based receiver.  
underwater acoustic communication; subcarrier multiplexing; neural networks; coherence bandwidth; equalizers; detectors +
Footnote †: journal:  
Article
## 3 Fabrication and 2 x 2 Array Structure  
The proposed antenna was manufactured using bulk silicon micromachining and wafer-level assembly processes. Specifically, the bulk silicon process refers to the process of obtaining a certain shape of through groove, blind groove, or through vias on a certain thickness of silicon wafer using etching technologies. After etching, a gold layer with a thickness of 3 \(\upmu\)m was electroplated on each side of a wafer, and the process flow is shown in Figure 14. Then, high-precision wafer-level bonding was achieved between each layer of silicon wafers using gold, and there is a 10 \(\upmu\)m gold layer between the formed silicon wafers.  
To validate the antenna performance, a 2 x 2 array is designed with the feed network on PCB. Due to the layout of other active chips and the scanning performance of phased arrays, the unit spacing is designed to be 5 mm (0.47\(\lambda_{0}\)). The PCB contains two layers of substrate (Arlon AD300A, \(\epsilon_{r}=3\), \(tan\delta=0.002\)) and three layers of metal. In order to achieve equal amplitude feeding of units while ensuring signal transmission quality, a strip-line Wilkinson power division network was designed. Figure 15 shows a cross-section view of the whole 2 x 2 array structure for measurement. The antenna fabricated on silicon is bonded to the PCB through BGA which provides reliable interconnection.  
Figure 14: Flow of the silicon bulk micromachining process.  
Figure 13: Comparison of the antenna’s performance with/without strips on M2.
## 3 Results and Discussion
### Gene Essential Assessment: A Tool for Drug Target Discovery?
\begin{table}
\begin{tabular}{l l c c c c c c} \hline \hline
**Systematic Name** & **Standard Name** & **EC Number** & **Organism** & **Drug** & **PDB Entry** & **Similarity** & **Coverage** \\ \hline C1\_08590C\_A & ERG1 & 1.14.14.17 & \begin{tabular}{c} _Candida albicans_ \\ _Candida albicans_ \\ _Candida albicans_ \\ \end{tabular} & \begin{tabular}{c} Terbinafine - \\ Tolnafate \\ \end{tabular} & - & - & - \\ \hline C1\_09720W\_A & URA1 & 1.3.5.2 & _Plasmodium falciparum_ & Atovaquone & 5 DEL & 37\% & 81\% \\ \hline C2\_02460W\_A & ERG7 & 5.49.97 & _Candida albicans_ & Oxiconazole & - & - & - \\ \hline C5\_00190C\_A & FAS1 & 1.3.19 & \
\begin{tabular}{c} _Mycobacterium tuberculosis_ \\ _Mycobacterium tuberculosis_ \\ \end{tabular} & Ethionamide & 4V8W & 30\% & 45\% \\ \hline C5\_00770C\_A & FOL1 & 4.1.2.25 & _Saccharomyces cerevisiae_ & Sulfacetamide & 2 BMB & 42\% & 65\% \\ \hline C5\_02710W\_A & TRR1 & 1.8.1.9 & _Staplyococcus aureus_ & Azelaic acid & 4 GCM & 42\% & 98\% \\ \hline C7\_03130C\_A & DFR1 & 1.5.1.3 & _Escherichia coli_ & Trimethoprim & 4 GH8 & 35\% & 77\% \\ \hline C5\_00770C\_A & FOL1 & 2.5.1.15 &
\begin{tabular}{c} _Escherichia coli_ \\ _P. falciparum_ \\ \end{tabular} & Sulfonamides and sulfones & 1 AJ2 & 36\% & 40\% \\ \hline C1\_0240C\_A & GSC1 & & _Candida albicans_ & Anidualfungin & - & - & - \\ C1\_05600W\_A & GSL1 & 2.4.1.34 & _Candida albicans_ & Casopfungin & - & - & - \\ CR\_00850C\_A & GSL2 & & _Candida albicans_ & Micatrungin & - & - & - \\ \hline C3\_04830C\_A & FAS2 & 2.3.1.41 & _Escherichia coli_ & Cerulenin & 2 BVX & 31\% & 8\% \\ \hline CR\_00850C\_A & ERG11 & 1.14.14.154 & _Candida albicans_ & Azoles & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Drug targets evaluated for gene essentiality prediction in RPMI medium, as identified by the iRV781. Data retrieved from DrugBank database; only drugs with known pharmacological action were selected.
# A Novel Approach to Enhance the Energy Efficiency of a NOMA Network
## 3 System Model  
We consider that a wireless network consists of many base stations (BSs), where each BS transmits information to \(U\) users, where each user is \(u\in\{u_{1},u_{2},\ldots,u_{U}\}\), through \(V\) sub-channels, where each sub-channel is \(v\in\{v_{1},v_{2},\ldots,v_{V}\}\). Furthermore, each receiver is equipped with SIC and a single antenna. Assuming that the system bandwidth (BW) is \(B\), then the BW of every sub-channel is:  
\[B_{v}=B/V \tag{1}\]  
By expressing the total transmitted power of the BS as \(P_{BS}\), and the power of the \(i_{th}\) user of the sub-channel \(v\) as \(P_{i,v}\), then, the signal sent via the BS for \(n\) users per sub-channel can be expressed as follows:  
\[x_{v}=\sum_{i=1}^{n}\sqrt{P_{i,v}}s_{i} \tag{2}\]  
where \(s_{i}\) represents the symbol of the \(i\)th user. According to the principle of NOMA, each piece of user equipment (UE) receives a superposition of the correct and interfering signals simultaneously. Thus, the received signal for the \(j\)th user can be represented as:  
\[y_{j,v}=x_{v}h_{j,v}+w_{j,v} \tag{3}\] \[=(\sum_{i=1}^{n}\sqrt{P_{i,v}}s_{i})h_{j,v}+w_{j,v}\] (4) \[=\sqrt{P_{j,v}}s_{j}h_{j,v}+\sum_{i=1,i\
eq j}^{n}\sqrt{P_{i,v}}s _{i}h_{j,v}+w_{j,v} \tag{5}\]  
where \(h_{j,v}\) is the sub-channel coefficient of the \(j\)th user and \(w_{j,v}\) is the additive white Gaussian noise (AWGN) with \(w_{j,v}\sim\mathcal{N}_{\mathbb{C}}(0,\,\sigma_{v}^{2})\). Then, SINR (without SIC) received by the \(j\)th user can be expressed as:  
\[\begin{split}\text{SINR}_{j,v}&=\frac{P_{j,v}|h_{j,v }|^{2}}{\sigma_{v}^{2}+\sum_{i=1,i\
eq j}^{n}P_{i,v}|h_{j,v}|^{2}},\\ &=\frac{P_{j,v}H_{j,v}}{1+\sum_{i=1,i\
eq j}^{n}P_{i,v}H_{j,v}} \end{split} \tag{6}\]  
where \(H_{j,v}=|h_{j,v}|^{2}/\sigma_{v}^{2}\) represents the channel response normalized by noise for the \(j\)th user at sub-channel \(v\). For the SIC optimal decoding order, the channel conditions for user superpositions per sub-channel should be sorted by the channel responses normalized by noise as follows:  
\[H_{i,v}\leqslant H_{j,v}\ \,\forall\,(i<j)\in\{1,\ldots,n\} \tag{7}\]  
Consequently, the power assigned to users per sub-channel will be according to the following order:  
\[|P_{i,v}|\geqslant|P_{j,v}|\ \,\forall\,(i<j)\in\{1,\ldots,n\} \tag{8}\]  
Then, after removal of the interference of users with poorer channel conditions, the estimated SINR for the \(j\)th user can be expressed as:  
\[\widetilde{\text{SINR}_{j,v}}=\frac{P_{j,v}H_{j,v}}{1+\sum_{i=j+1}^{n}P_{i,v} H_{j,v}}\ \ \ \,j=1,\ldots,\ n-1. \tag{9}\]  
Based on [PERSON]'s capacity formula, the data rate for the \(j\)th user is given by:  
\[R_{j,v}=B_{v}\,log_{2}(1+\frac{P_{j,v}H_{j,v}}{1+\sum_{i=j+1}^{n}P_{i,v}H_{j,v }}) \tag{10}\]  
The sum rate of the sub-channel \(v\) is given by:  
\[R_{v}=\sum_{j=1}^{n}B_{v}log_{2}(1+\frac{P_{j,v}H_{j,v}}{1+\sum_{i=j+1}^{n}P_ {i,v}H_{j,v}}) \tag{11}\]Accordingly, the total sum rate of the system is:  
\[R=\sum_{v=1}^{V}R_{v} \tag{12}\]
## 3 Quantified Damage Analysis of Train Gearbox
### Revolution Counting Method for Evaluating the Relative Fatigue Damage of Train Reduction Gears  
To calculate fatigue damage, the first step is to simplify the measured load data, with the chosen data classification method tailored to suit the analysis objectives. This classification process has the advantage of effectively reducing vast amounts of data while characterizing meaningful trends. The ASTM (American Society for Testing and Materials) E 1049 standard recommends various effective signal classification methods, such as the Revolution Counting Method, Level Crossing Counting Method, Simple-Range Counting Method, Peak Counting Method, and Rainflow Counting Method [2]. Among these cycle-counting techniques, the Rainflow Counting Method is one of the most widely used, as it only considers the amplitude and range of loads. However, the Revolution Counting Method includes the concept of rotation amount under specific load levels. This concept is illustrated in Figure 5, which demonstrates how to calculate the cumulative number of revolutions within a specified torque range using motor torque and motor rotational speed (RPM) data.  
Figure 4: Powertrain system structure.  
Figure 5: Demonstration of the torque\(-\)revolution counting. The yellow color represents the torque range during a specific operating period in the time-based torque plot, while the blue color indicates the rotational speed range within a specific operating period in the time-based RPM plot.  
In this study, the Revolution Counting Method shown in Figure 5 is utilized to analyze the relative damage on the input and output shafts of the train reduction gear, as depicted in Figure 4.
## 3 Results
### Analysis of the Global Scientific Collaboration Network: Formation of a Dual-Core Structure  
#### 3.2.1 Global Distribution of National/Regional Research Capacities  
Based on the 4985 articles retrieved and screened from the Web of Science database, this study conducted a statistical analysis of publication volume by country/region. These publication counts were assigned to corresponding geographic vector in ArcGIS 10.2. For visual clarity and representativeness, only countries/regions with more than 10 publications  
Figure 2: UAV-RS research quantity distribution. were included in the map. An analysis of national publication statistics and collaboration networks in the field of UAV-RS reveals a marked geographic concentration and high degree of cooperative activity (Figure 3). Researchers from over 40 countries and regions have contributed to this field; however, the top 15 countries account for nearly 85% of the total global publications. This indicates a highly concentrated academic output, suggesting the presence of significant entry barriers or a strong concentration of research resources in this domain. China ranks first, with 1657 publications, accounting for 33.2% of the total (Table 1). Its research spans various themes such as agricultural remote sensing [44; 45], ecological monitoring [46], urban management [47], and disaster response [48]. The United States ranks second, with 1094 publications (21.9%), with a focus on ecological environments, disaster assessment, and urban infrastructure monitoring [49; 50; 51; 52; 53; 54; 55; 56; 57], reflecting a strong orientation toward applied technologies. Together, China and the U.S. contribute more than half of the global academic output in the UAV-RS field, forming a dominant first-tier group. China's research agenda is distinctly \"application-driven,\" emphasizing precision agriculture, environmental monitoring, and infrastructure inspection, closely aligned with national strategic priorities. While the U.S. is also application-focused, it demonstrates a deep foundation in ecosystem monitoring, disaster evaluation, and fundamental remote sensing methodologies. European countries such as the UK, Italy, and Germany--traditionally strong in remote sensing--comprise the second tier, with research that emphasizes regional characteristics (e.g., archaeology, volcanic monitoring) and the refinement of specific technologies such as LiDAR. Figure 3: Countries/regions with more than 10 publications.
# Research of Tri-State Optical Signal Detectors for Ternary Optical Computers
## 3 Results
### Experimental Results  
According to the above experimental steps, the results of the random output of the optical processor were tested repeatedly to obtain the data in Table 3.  
The minimum and maximum resistance values recorded in Table 3 are related to the brightness and darkness of the LCD pixels. Low resistance means stronger light intensity and large resistance means stronger light intensity.  
Taking into account the existence of errors and according to the data in the table, we reduced the fixed resistance of each area of the voltage divider resistor layer by a certain percentage and enlarged the adjustable resistor resistance by a certain percentage, then determined the resistance value in each area as follows:  
* In the corner of the 3, 7, 9 test area, the weakest light intensity can be set to R1 by a 100 K fixed resistance, R2 is set to a 1 M adjustable resistance, and R3 is set to a 500 K fixed resistance;  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Test Area** & **Maximum Resistance Value of R1** & **Minimum Resistance Value of R1** & **Maximum Resistance Value of R2 + R3** & **Maximum Maximum Resistance Value of R2 + R3** \\ \hline
1 & 180 2 K & 390 K & 400 K & 750 K \\
2 & 100 K & 250 K & 500 K & 800 K \\
3 & 160 K & 370 K & 430 K & 750 K \\
4 & 100 K & 250 K & 500 K & 800 K \\
5 & 80 K & 180 K & 300 K & 700 K \\
6 & 80 K & 250 K & 470 K & 790 K \\
7 & 180 K & 390 K & 400 K & 750 K \\
8 & 100 K & 250 K & 490 K & 800 K \\
9 & 180 K & 390 K & 400 K & 750 K \\ \hline \end{tabular} \({}^{\dagger}\) The unit is \(\Omega\). \({}^{2}\) Zeroed out the last digit of the data.  
\end{table}
Table 3: Voltage division resistance table.  
* In the edge of the 2, 4, 6, 8 test area, the light intensity is slightly stronger, R1 can be set to a 50 K fixed resistor, R2 is set to a 1 M adjustable resistor, and R3 is set to a 400 K fixed resistor;
* Near the light source in the area of 1, 5, the liquid crystal light intensity is the strongest, R1 can be set to a 10 K fixed resistor, R2 is set to a 1 M adjustable resistor, and R3 is set to a 100 K fixed resistor.  
We can determine the voltage division resistance value within each division, and we can solder according to the determined resistance value during the actual board-making process. Therefore, after installation, only minor adjustments are required to achieve the correct photoconversion of the 576 LCD pixels.  
In addition, the \"white noise\" factor in the experiment will also affect the experimental results; that is, the light intensity of other liquid crystals around a liquid crystal pixel will interfere with the detection of that liquid crystal pixel by the photosensitive sensor. By comparison, it was found that the light source around the sensor tube head can avoid the influence of \"white noise\" in the case of complete isolation, so it is necessary to do a good job of isolation measures to avoid light leakage.
# Energy-Efficient Hosting Rich Content from Mobile Platforms with Relative Proximity Sensing
## 7 Evaluation of _T-Wing_
### Uplink Bandwidth Requirement  
Figure 6 shows the time to upload different file types through the cellular data network. In order to measure the time to upload different file types through the cellular data network, we performed this experiment on an embedded platform, Beagleboard-xm [17] equipped with an AM37\(\times\) 1 GHz (ARM Cortex-A8 compatible) processor, a Wi-Fi and a 3G/4G LTE dongle (HuaweiTM E3276) [43] which is connected over the 3G network system though it supports GSM/GPRS/EDGE communication modes. We measured the total time to upload different file types from a mobile device participating in _T-Wing_ to the _T-Wing_ proxy server through the cellular data network.  
In our experiment, we transferred our benchmark files through the cellular data network for each scenario. We then measured the improvement in transfer performance enabled by the compression strategy described in Table 1. Since the cellular uplink bandwidth is a severe constraint for mobile platforms, the total uploading time is much faster compared to the original file even  
Figure 5: Overall architecture of _T-Wing_ that consists of _T-Wing_ proxy server and _T-Wing_ client.  
when compression/decompression overhead is included. On average, we found that performance is improved by 4\(\times\) for the content we measured.
# Cross-View Outdoor Localization in Augmented Reality by Fusing Map and Satellite Data
## 3 Methods  
In this section, we provide a detailed description of the proposed method. First, we discuss the details of the dataset and how the top-down images are extracted, including the OpenStreetMap data. Afterwards, we discuss the details of the model.
## 2 Prior and Related Work
The most widely used RNN configuration is the long short-term memory (LSTM) [60]. Another popular RNN configuration is the gated recurrent unit (GRU) [61]. It is similar to LSTM but has fewer parameters because it lacks an output gate. GRU is mainly applied in natural language analysis and translation. DL methods for intrusion detection started being using in 2014, when a deep belief network (DBN) was used as a classifier with the combination of restricted Boltzmann machine (RBM) for training and backpropagation for the fine-tuning process of KDD Cup 1999 [62]. Later, when computer processing (especially graphics processing unit (GPU)) capabilities were improved, more complex DL configurations emerged. RNNs were the most apparent methods to adapt for intrusion detection due to the ability to work with time-series data [63; 64; 65]. CNN networks for malware classification started being used in 2016 [66; 67], while the first concept of CNN usage for intrusion detection was proposed in Reference [68] and was later successfully applied by Reference [69; 70], with the results up to 99% of network anomaly detection accuracy. Later, DL methods were widely used for the datasets mention above. CNN for HIDS was tested with various sliding window sizes on ADFA-IDS and NGIDS-DS [27]. The results were somehow better than the counterpart of the work in Reference [49], where classical ML methods were applied, with a detection rate (DR) of 60% and False Alarm Rate (FAR; also known as FPR) of 20%. To achieve these results, the input layer of 191 and 151 system calls was used. Other authors have used deep CNNs (dCNNs) to extract useful features from the data and the Softmax classifier to generate the final detection result for the ADFA-IDS dataset. This method enhanced the real-time processing and detection efficiency of intrusion detection systems by 7.3% [71]. In our previous research conducted with the AWSTD dataset, CNN, LSTM, and GRU methods were evaluated. The maximum achieved accuracy was equal to 94.5% and 99.3% for 100 and 1000 first system calls, respectively, with a simple CNN configuration [14]. In recent years, research on the combination of CNNs and RNNs into one model has shown promising results [72; 73; 74] and was applied from image recognition to Bitcoin price prediction tasks. This encouraged many researchers to apply similar methods in the IDS research field. The combination of CNN and RNN was applied in NIDS and HIDS: it was tested with the KDD Cup 1999 dataset [75] and Australian Defence Force Academy Linux Dataset (ADFA-LD) [76], and comparable results were achieved. The motivation for the research described below was to evaluate intrusion detection accuracy and other critical parameters with more complex CNN, LSTM, and GRU configurations applied for the AWSTD dataset since, as already stated, more complex DL models typically provide better results in certain tasks, such as natural language processing and image recognition.