esa-sceva/satcom-chunk-collection huggingface dataset:
# Sustainable Removal of Cr(VI) by Line Peel and Pineapple Core Wastes
## Materials and Methods
### Cr Analysis  
The concentrations of Cr(VI) and total Cr were determined in the liquid by the colorimetric 1,5-diphenylcarbazide method at 540 nm using a spectrophotometer JASCO V-630 (Jasco Inc., Easton, MD, USA) [15]. The determination of Cr(III) was obtained from the difference between total Cr and Cr(VI).  
Cr(VI) and total Cr removal efficiencies reflect the decrease of Cr(VI) in the solution and the reduction of the Cr(VI) concentration due to the generation of Cr(III) for the oxidation process and their adsorption, respectively. They were expressed in percentages terms using the Equation (3).  
\[\mathrm{Removal\ efficiency}\ (\%)\ =\ 100\ \times\ (C_{0}-C_{t})/C_{0} \tag{3}\]  
where the concentration of Cr(VI) or total Cr in the solution after 24 hours is \(C_{t}\) and the initial Cr(VI) concentration is \(C_{0}\).  
In the isotherm analysis, the amount of Cr adsorbed per gram of adsorbent was expressed applying the equation below:  
\[q_{e}=(C_{0}-C_{e})\times V/m \tag{4}\]  
where \(q_{e}\) is the equilibrium contaminant adsorption (mg/g), \(C_{0}\) and \(C_{e}\) are the initial and equilibrium Cr concentrations (mg/L), respectively, \(V\) the solution volume (L) and \(m\) the biosorbent dosage (g).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Run** & **pH, \(\mathbf{x_{1}}\)** & **PP Dosage** & **Temperature** & **Cr(VI)** & **Total Cr** & **Uptake** & \(\mathbf{K_{D}}\) \\  & & **(g), \(\mathbf{x_{2}}\)** & **(\({}^{\circ}\)C), \(\mathbf{x_{3}}\)** & **Removal (\%)** & **Removal (\%)** & **(mg/g)** & **(mL/g)** \\ \hline  
5 & 1 & 27.5 & 65.89 & 24.95 & 1.71 & 16.62 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Box–Behnken experimental design matrix and responses in terms of real factors and calculated uptake and \(K_{D}\).  
The uptake distribution coefficient is defined as the ratio of the quantity of the adsorbate adsorbed per mass of solid to the amount of the adsorbate remaining in solution at equilibrium and related to the partitioning of a contaminant between the solid and aqueous phases [16]. In this coefficient, the adsorbed species in the adsorbent were divided by their concentration in the aqueous phase, and it was expressed according to the equation shown below [17]:  
\[K_{D}=(C_{0}-C_{e})\times V/(m\times C_{e})(\mathrm{mL/g}) \tag{5}\]
## 3 Broadcast Ephemeris Performance
### Signal-in-Space Ranging Errors  
To comprehensively assess the accuracy of GNSS broadcast ephemeris orbits and clock offset, the SISRE of all systems was investigated [27]. The SISRE for GPS/BDS-2/BDS-3/GLONASS/Galileo/QZSS satellites from 283 to 289 days in 2021 is given in Figure 8. The mean and RMS of SISRE are given in Table 5. It can be found that the SISRE of GPS and Galileo show the best performance among the five systems, and its fluctuation is between 0 m and 2 m. However, the SISRE of Galileo is larger at some epochs. The rank of SISRE from best to poorest is QZSS, BDS-3, GLONASS, and BDS-2. For the BDS MEO satellites, the SISRE value of the BDS-2 MEO satellites (C11, C12) and the BDS-3 MEO satellites (C25, C30) are from 0 m to 7 m and 0 m to 4 m, respectively. In terms of BDS IGSO satellites, the fluctuation of BDS-2 IGSO satellites (C13, C16) and BDS-3 IGSO satellites (C39, C40) are between 0 m and 6 m, and 0 m and 4 m, respectively. The SISRE of BDS-3 is better than BDS-2 for both MEO and IGSO satellites. Since SISRE can reflect the combined error of orbit and clock offset, and the accuracy of broadcast ephemeris orbit and clock offset are calculated using the observations from ground stations, there is a certain relationship between the observation data quality and SISRE. By comparing the observation data quality and SISRE, it can be found that when the observation data quality is better, the broadcast ephemeris SISRE is also better, and vice versa.  
Figure 8: SISRE of GPS/BDS-2/BDS-3/GLONASS/Galileo/QZSS.
# A Rectangular Notch-Band UWB Antenna with Controllable Notched Bandwidth and Centre Frequency
###### Abstract  
This paper presents the design and realization of a compact ultra-wideband (UWB) antenna with a rectangular notch wireless area network (WLAN) band that has controllable notched bandwidth and center frequency. The UWB characteristics of the antenna are achieved by truncating the lower ends of the rectangular microstrip patch, and the notch characteristics are obtained by using electromagnetic bandgap (EBG) structures. EBGs consist of two rectangular metallic conductors loaded on the back of the radiator, which is connected to the patch by shorting pins. A rectangular notch at the WLAN band with high selectivity is realized by tuning the individual resonant frequencies of the EBGs and merging them. Furthermore, the results show that the bandwidth and frequency of the rectangular notch band could be controlled according to the on-demand rejection band applications. In the demonstration, the rectangular notch band was shifted to X-band satellite communication by tuning the EBG parameters. The simulated and measured results show that the proposed antenna has an operational bandwidth from 3.1-12.5 GHz for \(|\)S\({}_{11}|\) \(<\) -10 with a rectangular notch band from 5-6 GHz, thus rejecting WLAN band signals. The antenna also has additional advantages: the overall size of the compact antenna is 16 \(\times\) 25 \(\times\) 1.52 mm\({}^{3}\) and it has stable gain and radiation patterns.  
UWB antenna; rectangular notch; electromagnetic bandgap (EBG); notch-band antenna +
Footnote †: journal: Sensors
# EconLedger: A Proof-of-ENF Consensus Based Lightweight Distributed Ledger for IoVT Networks
## 5 Experiment and Evaluation
### Performance Evaluation
#### 5.2.3 Data Throughput  
In order to evaluate overhead of running EconLedger on communication channel, we considered volumes of message propagation and data throughput during key steps of the PoENF consensus protocol. Figure 6 demonstrates data transmission for different stages of PoENF consensus with varying committee size. In our EconLedger prototype, each ENF transaction has fixed size \(d_{tx}\) = 430 Bytes, and a vote has fixed size \(d_{vt}\) = 589 Bytes. Given the total communication complexity of \(\mathcal{O}(K^{2})\) in both ENF collection and chain finality, data transmission of ENF collection is \(\mathcal{D}_{cc}=d_{tx}\times K^{2}\), and the data transmission of chain finality is \(\mathcal{D}_{cf}=d_{vt}\times K^{2}\). Thus, communication overheads incurred by ENF collection and chain finality are linearly scaled to \(K^{2}\).  
Each block has a fixed header \(d_{head}\) = 613 Bytes along with a transactions list with size \(d_{txs}=d_{tx}\times K\), and we can obtain block size \(d_{B}=d_{head}+d_{txs}K\). Therefore, the block size \(d_{B}\) is linearly scaled to \(K\). Assuming an ideal case that only one valid block is proposed during each epoch cycle, data transmission of block proposal is \(\mathcal{D}_{bp}=d_{B}\times K=d_{head}K+d_{txs}K^{2}\) such that communication overhead is almost scaled to \(K^{2}\). On the other hand, for the worst case that every validator proposed a candidate block such that \(\mathcal{D}_{bp}=d_{B}\times K^{2}\), huge communication cost scaling up \(K\) can be introduced.  
The data throughput could be specified as \(Th=\frac{\mathcal{D}_{cc}+\mathcal{D}_{bp}+\mathcal{D}_{cf}}{\mathcal{D}_{cc }+\mathcal{D}_{bp}+\mathcal{D}_{cf}}\) (KB/s), where KB/s means KBytes per second. With variant committee sizes, the corresponding block size and data throughput are calculated as shown in Table 3. Given a fixed ENF transaction size, increasing the committee size allows committing more ENF proofs and, therefore, reach a higher data throughput at the cost of latency. In the test case of \(K=20\), EconLedger implies a theoretical maximum data rate of 283 KB/s, which can meet bandwidth conditions in a majority of LAN-based IoVT systems.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Committee Size** & 5 & 8 & 12 & 16 & 20 \\ \hline
**Block Size (KB)** & 2.7 & 3.9 & 5.6 & 7.3 & 8.9 \\ \hline
**Throughput (KB/s)** & 61.9 & 108.3 & 159.8 & 220.1 & 283.3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Data throughputs vs. committee sizes.  
Figure 6: Communication overhead of running an epoch round of PoENF consensus. Comparative evaluations on different committee size.
# Hybrid FSK-FDM Scheme for Data Rate Enhancement in Dual-Function Radar and Communication
## 5 Simulation Results
### Example 3: Security of Communication Process  
In this section, we discuss the concerns related to the security in communication. We calculated the SNR for all angles used for transmission. It is clear from Figure 12 that we have minimum interference at the desired angle, i.e., \(\theta_{c}=-50^{\circ}\) while, the rest of the angles suffer very high levels of interference. The total number of bits transmitted was \(10^{6}\). The SNR was fixed at 5 dB. The results are compared with beam pattern ASK- and beam pattern PSK-based approaches. It is concluded that the performance of the proposed scheme is better than these methods at the same SNR.  
Figure 10: The performance comparison of the transmitted and received FDM signal and their frequency response.  
Figure 9: The performance comparison of the proposed scheme with existing schemes.
## 3 Full Description of the Approach
### Segmentation into Structural Elements  
The aim of the first part of the approach, structural element point clouds are segmented. It begins with the segmentation of the building point clouds into sub-spaces to move from scan point clouds to room point clouds. Then, based on each room point cloud, several plane segmentations are performed and points are classified as walls, ceilings or grounds. Finally, wall point clouds are obtained by assembling planes. This part of the approach was already described in [58; 59].
###### Abstract  
The out-of-plane bending problems of functionally graded thin plates with a circular hole are studied for two-dimensional deformations. The thin plates have arbitrary variations of elastic properties along the radial direction. The general solutions of the stresses and moments are presented for the plates subjected to remote bending moments based on the theory of complex variable functions. Two different cases--a whole functionally graded plate with a circular hole and a functionally graded ring reinforced in a homogeneous perforated plate--are considered by numerical examples. The influence of parameters like Young's modulus and Poisson's ratio, function types of these elastic properties, and width of the reinforcing ring on the moments around the hole is presented. It is shown that the moment concentration, caused by the geometric discontinuity of the hole in the traditional homogeneous plate, can be well relieved or even eliminated by careful selection of the above parameters. The results for some special cases are compared with previous literatures and are found in good agreement.  
out-of-plane bending; circular hole; moment concentration; graded thin plate; complex variable method +
Footnote †: journal: Applied Physics Letters
## 6 Discussion
### Calculation of RORC Severity Change Threshold  
To reduce the severity of RORC, we aimed to derive threshold values that serve as criteria for the safety process. The threshold refers to the size of a variable at which the RORC severity increases sharply when the size of the variable is above or below a certain level. The CoO method was used to calculate these thresholds. CoO is analyzed based on the Kullback\(-\)Leibler divergence, which is used in information theory and probability theory. The Kullback\(-\)Leibler divergence is used to find the distance between two probability distributions and is expressed by Equation (3) below [50]:  
\[D_{KL}(P||Q)=H(P,Q)-H(P)=\left(-\sum\limits_{i=1}^{n}p(x)\log_{2}q(x)\right)- \left(-\sum\limits_{i=1}^{n}p(x)\log_{2}p(x)\right)=\sum\limits_{i=1}^{n}p_{i }\log_{2}\frac{p_{i}}{q_{i}} \tag{3}\]  
where  
\(H(P,Q)\): The cross-entropy of \(P\) and \(Q\) which expresses the overall difference between two distributions  
\(H(P)\): The entropy of \(P\), which is a measure of how much information \(P\) carries (0 to \(\infty\)).  
Here, CoO is the measurement that replaces \(P\) with \(P(X|\varepsilon)\) and \(Q\) with \(P(X|\varepsilon/\varepsilon_{i})\). Using this measurement, we can analyze the impact of the omitted evidence, \(\varepsilon_{i}\), on the problem variable \(X\). Let \(X=\{x_{1},\ x_{2},\cdots,x_{n}\}\) be a problem variable and let \(\varepsilon=\{\varepsilon_{1},\ \varepsilon_{2},\cdots,\varepsilon_{n}\}\) be a set of evidence. Given this, the CoO of \(\varepsilon_{i}\) is expressed by Equation (4):  
\[\begin{array}{rl}c(\varepsilon_{i})&=c(P(X|\varepsilon),\ P(X|\varepsilon/ \varepsilon_{i})\\ &=(-\sum\limits_{i=1}^{n}p(x)\log_{2}q(x))-(-\sum\limits_{i=1}^{n}p(x)\log_{2} p(x))\\ &=\sum\limits_{i=1}^{n}p_{i}\log_{2}\frac{p_{i}}{q_{i}}\end{array} \tag{4}\]  
where  
\(c(\varepsilon_{i})\): CoO(Cost-of-Omission) of \(\varepsilon_{i}\).  
If the calculated probabilities are the same with or without evidence, the evidence is considered to be insignificant. On the contrary, if the calculated probabilities are significantly affected by the omission of evidence, the evidence is considered significant [51].
## 3 Compositional Data on Date Seeds and Date Seed Coffee Products
### Plant Secondary Metabolites
#### 3.4.2 Phytosterols  
The analysis showed that date seed oil (_P. canariensis_) contained 336 mg/100 g of various phytosterols. The predominant sterol present was \(\beta\)-sitosterol, accounting for over 76% of the total, with around a 20% split between campesterol, \(\Delta^{5}\)-avenasterol, \(\Delta^{5,24}\)-stigmastadienol (2.73%), and a small amount of cholesterol (0.42%), stigmasterol (1.09%), \(\Delta^{7}\)-stigmastenol (0.79%), and \(\Delta^{7}\)-avenasterol (1.18%) [23]. Date seeds contained moderate quantities (7.2%-9.6%) of sterols and triterpenes, with only \(\beta\)-sitosterol and cycloartenol being identified. This substance class was not detected in coffee beans [41].
# A Feedback Control Method to Maintain the Amplitude of the RF Signal Applied to Ion Traps
## 3 FPGA-Based Feedback Control
### FPGA-Based Feedback Controller
#### 3.3.1 Finite-State Machine  
Figure 4 presents the conceptual diagram of the finite-state machine (FSM) for controlling an ADC, a DAC, and a PI controller (or a lag compensator). The FSM initially starts with **WAIT**. When we enter the command _UPDATE start_ into an FPGA using the serial communications of a personal computer, the state of the FSM switches from **WAIT** to **UPDATE**. Then, we can enter the parameter values, such as the proportional gain and integral gain of a PI controller, the pole and zero of a lag compensator, the sampling period, and the set-point voltage into the FPGA. When we enter the command _LOAD start_, the FPGA enters the information in the range of the input and the channel to accept the signals into the ADC. When the operation is complete, **UPDATE** and **LOAD** are automatically switched to **WAIT**. When the commands are entered, such as _ADC on_, _Controller on_, and _DAC on_, the state of the FSM switches to the corresponding state. The commands such as _ADC off_, _Controller off_, and _DAC off_ return the state to **WAIT**. In **ADC**, an analog data entered into the ADC is converted into digital data and stored in the FPGA. In **Controller**, the output of the feedback controller is calculated to reduce the difference between the  
Figure 2: A block diagram of the closed-loop feedback control system for reducing the fluctuation of the amplitude of the RF signal, using a commercial universal proportional-plus-integral (PI) controller.  
digital data and set-point voltage. In **DAC**, the calculated data is converted to analog data using the DAC. The output of the DAC changes to a new value every 7.5 \(\upmu\)s, and the cycle is repeated. The DAC continues to supply the voltage of the previous cycle until it receives the new value.
# Deep Learning for Anomaly Detection in CNC Machine Vibration Data: A RoughLSTM-Based Approach
## 4 Experimental Results and Discussion
### Dataset  
The dataset presented in this study is a comprehensive collection of vibration data acquired from CNC milling machines in a real-world production environment. The data were collected over a two-year period (October 2018 to August 2021) from three different CNC machines (M01, M02, and M03) operating in a production plant [57]. The dataset is designed to address the challenges of industrial machine learning applications, particularly in the context of process monitoring and anomaly detection. The dataset consists of vibration data collected during various tool operations (OPs) performed by the CNC machines. Each operation represents a specific machining process with unique parameters, such as spindle speed, feed rate, and duration. The dataset includes 15 different tool operations, each characterized by its own set of parameters, as summarized in Table 1.  
Tool operations: Each operation (OP00 to OP14) is defined by its spindle speed (RPM), feed rate (mm/s), and duration (seconds). For example, OP00 is a step drill operation with a spindle speed of 250 RPM, a feed rate of 100 mm/s, and a duration of 132 s.  
Data segmentation: The raw vibration data were manually segmented into individual tool operations. Each segment corresponds to a specific operation performed by the machine. The dataset includes labeled data, where each tool operation is annotated as either \"OK\" (normal) or \"NOK\" (abnormal). The annotations were provided by production experts based on the quality control checks performed after each batch of workpieces. The abnormal cases (NOK) include process failures such as tool breakage, improper tool clamping, and chip jamming [57].
# Multi-Domain Communication Systems and Networks: A Tensor-Based Approach
## 4 Harnessing the Domain Trade-Off
### Numerical Examples
The tensor framework gives us the capacity in bits/tensor symbol, where in this case a tensor symbol contains elements across all the sub-carriers, sub-symbols, and antennas. Hence, we normalize the capacity of the MIMO GFDM channel by the number of sub-carriers and sub-symbols as in [25,44]. Figure 10 shows the normalized capacity against SNR for different values of \(S,K\), and \(M\). It can be seen that for a fixed value of \(KM\), as \(S\) increases we get higher capacity. In addition, for a fixed \(S\), choosing different configurations of \(K\) and \(M\), such that the product \(KM\) is constant, leads to almost similar capacity results. The capacity when \(K=4,M=10\) is almost the same as the capacity with \(K=8,M=5\), and slightly lower than with \(K=2,M=20\). This exhibits the latent trade-off between the sub-carrier and sub-symbol domains, which can be harnessed using the tensor framework. A domain trade-off can exist in many multi-domain communication systems. Another specific example of domain trade-off is presented in [45] which considers a layered maximum likelihood detection scheme for MIMO systems. Assuming a frequency selective fading channel, [45] considers a multipath component in every transmit-receive antenna pair as a virtual transmit antenna. Hence, the word 'layer' is used for the group of virtual  
Figure 10: Capacity vs. SNR for MIMO GFDM. antennas transmitting the same stream of data in its actual and delayed versions. The transmit signal is represented using a matrix (order 2 tensor) with domains corresponding to antennas and delay. Further, [45] uses over-sampling at the receiver which increases the dimension of the received signal to compensate for the lack of antennas, and thereby represents the output also as a matrix (order 2 tensor) corresponding to time and space domains. Thus, by harnessing the trade-off between the space and time domains at both the input and the output, [45] develops a maximum likelihood receiver for a MIMO OFDM system, which leads to an improved error rate performance.
## 3 Results
### Aerosol Contamination Protection
Additionally, it highlights the potential of using satellite data to control the surface measurements of aerosol contamination concentration. The satellite data can confirm surface measurements or raise questions about other factors. It is shown that the AI distribution is close to normal, so Pearson's correlation coefficients were calculated to explore the correlation between each Sentinel 5p product and PM\({}_{10}\) particle concentration. The statistical analysis showed that the correlation between surface measurements and satellite observations is weak for the whole Rijeka-Kvarner-Istria area. The area that includes several stations closest to the locations of faults on the 25 kV contact network showed better results. Hence, the correlation coefficient shows a weak-to-moderate correlation (up to \(\pm\)0.45). Analyzing correlations in various buffers around each air-quality station indicates that the AI and PM\({}_{10}\) particle concentrations have a generally negative correlation, up to minus 0.7 (Figure 14), which is expected because AI values were mostly negative. In the case of faults on the 25 kV overhead contact network, results are similar, but different air-quality stations show the strongest correlations. Calculating the correlation between PM\({}_{10}\) particle concentrations and positive and negative AI values separately, it was observed that there was a moderate-to-strong correlation between positive AI values and PM\({}_{10}\) particle concentrations (\(\pm\)0.3-0.7). The correlation of negative AI values showed a weak-to-moderate correlation (up to \(\pm\)0.45). This indicates that further analysis and modeling should explore smaller AI intervals to find better correlations and conclusions that lead to adequate operational model development, which could serve in the power network protection against aerosol contamination. The intervals should correlate with the prevailing aerosol type at the area of interest. Presented correlations have a statistical significance of 95% or more. Figure 14: Correlations between the AI\({}_{340-380}\) and PM\({}_{10}\) particle concentration.
# Network Location-Aware Service Recommendation with Random Walk in Cyber-Physical Systems
## 6 Experiment and Evaluation
### Discussion on the Network Location  
In this paper, we compute the similarity of two users or services, partly based on the inference of similar relation from the network locations of users or services. This inference is further based on the observation that services in the same autonomous system (AS) are likely to have a similar network environment and thus tend to have similar QoS.  
We leverage IP addresses and WSDL addresses, which are two types of network context information, to infer and build potential network relations among users and services. However, in the same autonomous system, in some cases, different services may be in heterogeneous network environments, because of the potentially diverse access technologies. So we propose a filtering strategy to filter fake neighbors. In detail, we propose a similar neighbor selection algorithm, which utilizes both the network information and QoS records to filter fake neighbors with abnormal QoS values. Thus in the case that the two users in the same AS are not neighbors, our proposed neighbor selection algorithm has the capability of identifying such fake neighbors.  
We conducted comprehensive experiments on two datasets over different data densities, and we can see that our models achieve superior results in all cases. The high prediction accuracy can also indicate the effectiveness of the utilization of network location information.