esa-sceva/satcom-chunk-collection huggingface dataset:
# Anomaly Detection in Blockchain Networks Using Unsupervised Learning: A Survey
## 4 Anomalies and Anomaly Detection in Blockchain
### Anomalies in Blockchain Networks
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Anomaly** & **Description** & **Occurrence** \\ \hline Sybil Attacks & Creation of multiple fake identities or nodes to gain control over a significant portion of the network, often disrupting consensus and influencing transaction validation. & They have been observed in public or consortium blockchains, e.g., in consortium blockchain used by a group of financial institutions [94]. \\ \hline Phishing Attacks & Malicious attempts to deceive individuals into revealing sensitive information, such as passwords or financial details, by impersonating trustworthy entities through emails, websites, or messages. & They are prevalent across the cryptocurrency space, e.g., according to a report by CipherTrace, phishing attacks accounted for millions of dollars in losses in 2023 alone [95]. \\ \hline Ponzi Schemes & Fraudulent investment operations where early investors are paid with funds from later investors, creating an illusion of profitability until the scheme collapsed, and causing financial losses for participants. & They plagued the cryptocurrency industry (e.g., the BitConnect), causing billions of dollars in damage before collapsing in 2018 (their frequency has decreased, but they still are a threat to decentralized finance). \\ \hline Double-Spending Attacks & Attempts to spend the same digital asset more than once, exploiting the delay in transaction validation to deceive the network. & Although less common in established cryptocurrencies like Bitcoin, they can occur in smaller networks or lesser-known altcoins. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Brief description of standard blockchain anomalies and their occurrences. Over time, several attacks have been reported on the Bitcoin and Ethereum networks. For example, a worthwhile attack occurred in 2017 on the Bitcoin network, where a large number of spam transactions flooded the network, causing delays and stalling transaction verifications [1; 93]. This, in turn, increased the mining process fee for Bitcoin, resulting in delayed payments of approximately USD 700 million [101]. Figure 2 visualizes the attack, where each bar corresponds to a specific attack's feature. The negative impact of spam transactions is evident, as illustrated by the leftmost bar. The subsequent bars depict the market value volatility, mining process fee increase, and delayed payments, all contributing to an overall negative effect on the blockchain system. The values atop each bar indicate the severity of these impacts, with negative values reflecting adverse consequences. The legend clarifies the color coding, associating green with positive impacts and red with
## 5 III-V-on-Silicon Devices for Optical Sensing Applications
### III-V-on-Silicon Spectrometers for Optical Sensing Applications
#### 5.2.2 GaSb-Based Photodetector Integration  
The antimony-based material system can be used for photodetection beyond the telecommunication wavelength window. This material system can make use of three group III elements (Al, Ga, In) and two group V elements (As, Sb) to cover a wide wavelength range. Epitaxial layers of this material system can be grown lattice matched to GaSb or InAs substrates. Solid-source Molecular Beam Epitaxy (MBE) is used by the University of Montpellier to grow GaInAsSb and InAsSb based epitaxial layer stacks on on-axis (100) oriented n-type GaSb substrates. The MBE reactor is equipped with both As- and Sb-valved cracker cells and conventional element-III cells. Be and Te are used as p- and n-type dopants. Ga\({}_{0.79}\)In\({}_{0.21}\)As\({}_{0.19}\)Sb\({}_{0.81}\) based PIN diode structures have been grown to reach a cut-off wavelength of 2.65 \(\upmu\)m whereas InAs\({}_{0.91}\)Sb\({}_{0.09}\) based PIN diode structures are used to cover the 3 \(\upmu\)m wavelength range. Details about epitaxial layer stacks and their growth can be found in [13, 15, 85, 86]. The integration process starts with the fabrication of the SOI spectrometers in imec's CMOS pilot line. Then DVS-BCB based adhesive bonding is used to bond the III-V die to the SOI chip. After bonding, the GaSb substrate is removed using mechanical grinding and wet etching with a HF/CrO\({}_{3}\) solution, until the etch stop layer is reached. After removing the etch stop layers by wet etching, the photodetector mesa is fabricated lithographically aligned to the underlying output grating couplers of the SOI spectrometer. Ti (2 nm)/Pt (35 nm)/Au (100 nm) contacts are then deposited using e-beam evaporation. To passivate the device, DVS-BCB is then spin coated on the sample and cured at 250 \(\,\mathrm{\SIUnitSymbolCelsius}\) for 1 h. Finally, deposition of Ti/Au contact pads is carried out after etching the DVS-BCB to access the top and bottom contacts. Figure 25a shows a microscope image of a fabricated silicon photonic AWG with integrated photodetectors.  
We have demonstrated evanescently coupled GaInAsSb based photodetectors in [85] and [13] with a responsivity as high as 1.4 A/W at room temperature. Grating-coupled GaInAsSb based photodetectors are demonstrated in [13, 14, 15] with an on-chip responsivity as high as 0.6 A/W. In [14] an array of 46 detectors was integrated onto a spectrometer chip, showing the scalability of the integration process. Recently, we have also successfully integrated grating-assisted InAsSb-based photodetectors on an AWG operating at wavelength of 3.8 \(\upmu\)m. A responsivity of 0.3 A/W at room temperature was obtained [86].  
Figure 24: (**a**) Measured transmission spectra of short-wave infrared AWG [82]. (**b**) Measured transmission spectra from a mid-infrared PCG [83].
## 2 Results and Discussion
### Volatile Organic Compounds (VOCs)  
Despite strawberry volatile compounds having been studied extensively [29], the information published to date on the volatiles of strawberry wine is still in short supply. In this study, a total of 63 VOCs were identified and quantified in strawberry wines by using headspace HS-SPME-GC-MS, which was divided into two kinds: the varietal aroma included two C6 compounds (A1-A2), one lactone (B1), two furans (C1-C2), eight terpenoids (D1-D8), and one phenolic acid ester (E1), while the fermentative aroma included four higher alcohols (F1-F4), five acetates (G1-G5), fourteen fatty acid ethyl esters (H1-H14),  
Figure 1: Total phenolic (**A**), total flavonoid (**B**), total anthocyanin (**C**), and scavenging ability of ABTS (**D**) and DPPH (**E**) free radicals of 6 strawberry wines fermented by different methods. Values identified by the same letters were not significantly different at the 0.05 level (one-way ANOVA–Dunace). six other esters (I1-I6), three fatty acids (J1-J3), three aldehydes and ketones (K1-K3), eleven phenylethyls (L1-L11), and three others (M1-M3) (Table S1). The differences in the VOCs in strawberry wines fermented using various methods are indicated using a heatmap (Figure 2), and the relative concentration histograms of various kinds of VOCs in different samples are presented in Figure 3A-C. The total concentrations of detected volatile aroma components for the six strawberry wines fell between 22.49 and 35.17 mg/L (Figure 3C), mainly including phenyl ethyl (5.18-16.28%), isobutanol (1-Butanol, 3-methyl-) (29.96-63.45%), isobutanol acetate (3.23-14.06%), 2-phenyl ethyl acetate (1.09-6.44%), ethyl caproate (hexanoic acid ethyl ester, 5.40-12.94%), and ethyl caprylate (octanoic acid ethyl ester, 2.17-9.99%), as shown near the red blocks (Figure 2, Table S1). However, compounds with high content may not necessarily make a significant contribution to wines, which cannot be detected by people with high olfactory thresholds, so the odor activity values (OAVs) of aroma compounds were calculated in this research. Aroma compounds with OAVs greater than 1 directly contribute to the aroma of wine, and the coordinating contribution of compounds with low OAVs between 0.1 and 1 to aroma quality has been confirmed by researchers [30]. Table 3 shows 33 probable odor-active ingredients with OAVs greater than 0.1. The higher the values, the more significant the contribution of the t substance to the aroma. \begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline
**No.
## 3 Scalable On-line Anomaly Detection System (SOADS)
### Implementation
#### 3.2.3 Relational Dependency Updater  
The logical flow for implementing the Relational Dependency Updater is illustrated in Figure 10.  
Figure 10: Process of Relational Dependency Updater.  
Figure 9: Spark DAG of Sequence Extractor.  
The Relational Dependency Updater uses the latest read point hash map, which has the object ID as key and the read points as values, as an internal storage space. First, to examine (A) which is the situation in which the first event occurred in the system, OID1 was captured at read point A and time T1. Because the latest read point hash map does not contain a key with the object ID, the object ID is inserted in the key and the read point A is inserted in the value. No insert occurs in the _RELATIONAL DEPENCENCY TABLE_ because this is before the discovery of relational dependency between read points. Next, in part (B), OID 1 was recognized at read point C and time T2. Because OID1 already exists in the latest relation hash map, read point C is updated in the value of OID1. At this time, A and C are in successive relation for OID1, and (AC, 1) is inserted in the _RELATIONAL DEPENDENCY TABLE_. Another ID, OID 2, is located at read point B at time T3, but there is no information about OID2 in the latest relation hash map; thus, (OID 2, B) is inserted. The same process is repeated for part (C) to find the relational dependency between the read points of OIDs and these are inserted in the _RELATIONAL DEPENDENCY TABLE_. If the same relation already exists in the _RELATIONAL DEPENDENCY TABLE_, the frequency is increased by 1.
# State of the Art of Telecommunication Systems in Isolated and Constrained Areas
## 2 Telecommunication Systems in an Isolated and Constrained Environment
### Telecommunication Towers
The scope of the \(Dmax\) of the telecommunication system is:  
\[Dmax=\frac{\sqrt{(PT\times GT\times GR)\times\lambda^{2}}}{4\times\pi\times \sqrt{Sensitivity}} \tag{1}\]  
where _Sensitivity_ is the received power, \(GT\) is the antenna gain for the transmitter, \(GR\) is the antenna gain for the receiver, and \(\lambda\) the signal wavelength  
Figure 2: Different telecommunication towers set. Some important parameters appear in this expression in terms of telecommunications [18]. It is easy to see that the scope increases by increasing the signal strength at the transmitter level. Similarly, by increasing the carrier's frequency, which conveys the information, the wavelength decreases and the scope declines. For example, by transmitting at the frequency of the UHF band, 915 MHz for the Internet of Things technology LoRa [19] (in America, 868 MHz in Europe), the transmitter and the receiver can be more distant than by transmitting by Wi-Fi at 5 GHz with equivalent transmitter power. However, another critical premise in telecommunications is that the data rate is lower when the frequency diminishes. Thus, with LoRa modulation [20], we are limited to low speed, 50 kbits/s. On the other hand, with 5 GHz Wi-Fi, we can easily transmit images with data rates of up to 2 Gbits/s. The Internet of Things for connected sensor-type objects requires the transfer of relatively small information for the moment. This bit rate of 50 kbits/s is, therefore, quite sufficient [21].
###### Abstract  
Reliable prediction of remaining useful life (RUL) plays an indispensable role in prognostics and health management (PHM) by reason of the increasing safety requirements of industrial equipment. Meanwhile, data-driven methods in RUL prognostics have attracted widespread interest. Deep learning as a promising data-driven method has been developed to predict RUL due to its ability to deal with abundant complex data. In this paper, a novel scheme based on a health indicator (HI) and a hybrid deep neural network (DNN) model is proposed to predict RUL by analyzing equipment degradation. Explicitly, HI obtained by polynomial regression is combined with a convolutional neural network (CNN) and long short-term memory (LSTM) neural network to extract spatial and temporal features for efficacious prognostics. More specifically, valid data selected from the raw sensor data are transformed into a one-dimensional HI at first. Next, both the preselected data and HI are sequentially fed into the CNN layer and LSTM layer in order to extract high-level spatial features and long-term temporal dependency features. Furthermore, a fully connected neural network is employed to achieve a regression model of RUL prognostics. Lastly, validated with the aid of numerical and graphic results by an equipment RUL dataset from the Commercial Modular Aero-Propulsion System Simulation(C-MAPSS), the proposed scheme turns out to be superior to four existing models regarding accuracy and effectiveness.  
remaining useful life prognostics; convolutional neural network; long short-term memory; health indicator; time series; data-driven 2019 9 4156 30 August 2019; Accepted: 29 September 2019; Published: 3 October 2019
# Monolitic Hybrid Transmitter-Receiver Lens for Rotary On-Axis Communications
## 4 Discussion  
### TX Performance  
Figure 7 proves the suitability of the ray mapping method based on curl-free mapping for the TX part. In order to assess the performance of the design, it is meaningful to have a closer look at the loss mechanism: the main loss is represented by \(\zeta_{\text{TX M}}=24.7\,\%\). This magnitude is quite common. It results from the extended source effect from the LD with regards to the TX lens part and the remaining curl in \(\mathbf{N}\). Furthermore, the overall ray mapping performance depends on the extent of the TX output aperture and the distance to the target plane [41]. The reason for this is that ray mapping is an optical far-field design method that neglects the rays' position vector on the output aperture.  
The inhomogeneity within the FOV is with \(\zeta_{\text{TX H}}=5.6\,\%\) very low. Generally, a \(\zeta_{\text{TX H}}\) below 10 % is a good result. The major part of the remaining inhomogeneity results from the FOV edge, where the irradiance starts to decrease. The Fresnel-loss \(\zeta_{\text{TX F}}\) is with 12.1 % in a common region for two material interfaces. Although an anti-reflection (AR) coating might reduce this effect by a factor of 3 to 4, it is challenging to homogeneously coat the nonplanar TX surfaces.
# Method of Calculating Desynchronization of DVB-T Transmitters Working in SFN for PCL Applications
## 5 TDoA Geometry  
TDoA is a technique used in multilateration localization. Multiple receiver stations gather signals from a single emitter. Considering two-dimensional space, the distance between the \(n\)th station position \(Rx_{n}\) (\(x_{Rxm}\), \(y_{Rxm}\)) and the emitter position (\(x_{Tx},y_{Tx}\)) can be described as  
\[D_{n}=\sqrt{\left(x_{Rxm}-x_{Tx}\right)^{2}+\left(y_{Rxm}-y_{Tx}\right)^{2}} \tag{1}\]  
Measuring the TDoA between two stations gives information about the difference of distances between the emitter and corresponding stations:  
\[t=\frac{D_{n}-D_{m}}{c}, \tag{2}\]  
where \(c\) is the speed of light.  
The TDoA from single-pair receivers forms a hyperbola on which the source of emission is located (see Figure 3a). By adding more receivers, a set of non-linear equations is created. The solution of this set is all the possible locations of the emitter. The problem of solving this equation is well known, and multiple approaches have been proposed [18].  
For any number of receivers, N, greater than one, it is possible to create a set of N-1 equations and form N-1 hyperbolae (Figure 3b).  
The exact value of the TDoA between two stations (\(Rx_{n}\) and \(Rx_{m}\)) is calculated by cross-correlating the acquired continuous signals (\(s_{n}\) and \(s_{m}\), respectively). Cross-correlation is a mathematical tool used in signal processing to analyze similarities between two signals in the function of time delay (\(\tau\)). For continuous signals, cross-correlation can be written as  
\[C_{nm}(\tau)=\int\limits_{-\infty}^{\infty}s_{n}^{*}(t)\;s_{m}(t+\tau)\;\mathrm{ d}t, \tag{3}\]  
where * denotes complex conjugation. The expected result of cross-correlation in a bistatic configuration should consist of a distinct peak corresponding to the TDoA value, \(t\).
# Pharmaceuticals Removal from Wastewater with Microalgae: A Pilot Study
## 3 Results and Discussion
### Correlation between Different Factors  
To better assess the impact of nutrient concentration and biomass productivity on pharmaceutical removal, a correlation analysis between all parameters was performed (Figure 6). Some positive correlations, already expected, are evident as those between the \"Average removal\" and the removal of individual pharmaceutical groups. Interestingly, a negative significant correlation was observed between the sum of all pharmaceuticals' concentrations and the average pharmaceutical removal or the removal of anti-hypertensives, suggesting that the removal of pharmaceuticals by microalgae may be compromised at high concentrations.  
As already expected, a significant positive correlation exists between nitrates and biomass concentration. However, unexpectedly, no correlation was observed between biomass concentration and the removal of any of the pharmaceutical groups. Nonetheless, some significant positive correlations were observed between anti-hypertensive drugs' removal and ammonium concentration (\(r=0.53\); \(p<0.05\)) and between TN and total pharmaceutical removal (\(r=0.46\); \(p<0.05\)) and antipsychotic drugs' removal (\(r=0.47\); \(p<0.05\)). Usually, higher concentrations of nutrients in the inflow of the photobioreactors would be linked with higher microalgal biomass productivities, which would explain the positive correlations obtained. However, no correlation between TN and NH\({}_{4}\) concentrations and  
Figure 5: Concentration of nitrogen (total nitrogen, ammonium, and nitrates) and phosphorus (total P) nutrients and chemical oxygen demand (COD) in the inflow water of the GreenDune photobioreactors during the 5 weeks of both experiments.  
biomass concentration was observed. Nonetheless, the link between nitrogenated nutrient concentration and the removal rates of some pharmaceutical groups may open new avenues for research with the supplementation of wastewater with other nitrogen-rich wastes as agricultural wastes [24].  
Most studies involving the removal of pharmaceuticals from wastewater using microalgae are performed under carefully controlled conditions in the laboratory and not many outdoor pilot studies exist in the literature. Hence, conditions affecting pharmaceutical removal by these systems are still poorly understood. In this case, it would appear that nutrient concentrations, particularly TN and NH4 concentrations, can promote better removal of pharmaceuticals.
## 3 Materials and Methods
### Anomaly Detection Techniques  
#### 3.4.1 Median Absolute Deviation (MAD)  
This is a statistical technique that uses the median (in contrast to many others, which use the average) generally in situations where the data are unbalanced or are known to contain outliers because it is less sensitive to extreme values [39].  
The first step is to calculate the median of the data. Once the median has been obtained, as a second step, for each observation in the data, the absolute deviation is calculated, which is given by the difference between each observation and the median calculated in step 1. Finally, the MAD value is obtained by obtaining the median of the absolute deviations from step 2.  
The calculation of the MAD is given by Equation (2):  
\[MAD=\text{median}(|X_{i}-\text{median}(X)|) \tag{2}\]  
To use the MAD in the anomaly-detection task, it is necessary to set a limit or threshold. This limit is a multiple of the calculated MAD. To determine whether an observation is an anomaly, the absolute deviation of the observation is compared with the limit; if it exceeds it, it will be considered an anomalous observation. The above is shown in Equation (3):  
\[\text{If }|X_{i}-\text{median}(X)|>\text{limit} \tag{3}\] \[\text{then, }X_{i}\text{ is an anomaly.}\]
# Recent Advances on Machine Learning Applications in
## 2 Machine Learning Paradigm in Machining Application
### Tool Condition Monitoring
Despite the ability of this method in sensor choices, positioning and acquisition; it still showed a few practical problems due to the high variability in chips and coolant applications. The literature shows interesting applications of the ANN method. [PERSON] et al. [109] used the WPT of the sound input signal and a Collinearity Diagnostic with a Stepwise Regression for feature selection. The authors obtained an overall error of 7.20%. Differently, [PERSON] et al. [25] applied the statistical features extracted from the WPT of Force, AE and Vibrations. A feature selection step was implemented based on the PCC obtaining a MAPE of 5.17%. The multi-sensor scheme was applied in further applications, for example, with tree-based structures. [PERSON] et al. [110] considered time-domain features with a RF model, obtaining an MSE of 10.156 \(\upmu\)m. [PERSON] et al. [111] introduced an ensemble conceptual design based on several ML methods (e.g., RF, Gradient Boosting Regression, ANN, Linear Regression and SVM), with a final phase of dynamically smoothing to predict the tool wear size. As in other applications, the PCC was applied for feature selection, including the process parameters, obtaining a final RMSE of 0.00834. In this context, Deep Learning applications may improve the prediction model, as demonstrated in different studies. In Reference [112], the authors applied a LeNet-WSRMC network for tool state recognition and classification, employing vibration and current signals and obtaining an average testing accuracy of 96%. Similarly, [PERSON] et al. [38] obtained an accuracy of 97.44% in worn grinding wheel recognition, evaluating the sound signal and using the FFT and the retransformation into the time domain through iFFT for signal processing. A further application of the CNN method was proposed by [PERSON] et al. [113]. They considered the GASF method to obtain an image representation of the force's signal input. [PERSON] et al. [14] applied the DWF and the Hilbert Envelope Demodulation Spectra (HEDS) approach to the vibration signals with a 2D input for the CNN, resulting in a final classification accuracy (for five categories) of 98.7%. Finally, the combination of LSTM and CNN improves the feature extraction step and the ability of time-dependency feature extraction. An example was presented by [PERSON] et al. [33], where the vibrations and PLC controller signals (axes position and spindle power) are analysed with a SBULSTM (Stacked Bi-Directional and Uni-Directional LSTM Network) with a fully connected layer at the top, adding nonlinearity to the output and one regression layer to generate the target RUL. The results indicated an accuracy of 90%. Several other applications of both ML and DL schemes, such as SSAE [27] or DBN [114], are classified and summarized in Table 1.
## 4 Experimental Results and Analysis
### Summary of Experimental Results  
To help readers intuitively feel the advantages of the algorithm in this paper, the index improvement of the algorithm compared with the SIFT algorithm and the adaptive SIFT algorithm is summarized, as shown in Table 6.