esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Description of the Dataset Used  
As previously stated, the study is based on a set of data collected by sensors linked to the compressor machine that records hourly voltage, pressure, flow, etc. These data were gathered in csv files composed of several rows and indexed by the column \"Date\" which contains dates with consecutive hours.  
Here, we merged all the variables in a single file which contains, at the beginning, a column reserved for the dates and the rest for our attributes. Then, this file was divided into two sections: one that contained only the variables whose output is a continuous value and the second that contained only the attributes of discrete types. The data were then processed by normalizing and cleaning them as explained above. After merging all the variables and distinguishing the discrete and continuous attributes, we defined the target variable to be predicted later. Indeed, in our dataset we did not find an exact variable that provides the state of operation and which stops the direct compressor. Consequently, we thought of creating this variable, which takes 0 if the compressor is in a working state and 1 in case of stoppage or failure. This variable has the abbreviation CP01. Thus, the target variable was created thanks to two variables: VAR 1, which calculates the flow of the compressor and which must be lower than 2, i.e., so it does not deliver any more air, and VAR 2, which must be on a stop or failure. If both conditions are met, a failure occurs, i.e., CP01 =1, and otherwise CP01 = 0.  
The problem of failure prediction is a classification problem since the type of the target variable is a qualitative variable. As a solution, the approach consists of predicting the case where there occur failures at the compressor level. The variable that corresponds to column CP01 is the y of our data set. The following figure (Figure 7) shows the variable of CP01 indexed by dates. We found 119 failures out of more than 3000 records, the first of which was in 22 August 2010 and lasted almost 8 h.  
To evaluate the performance of machine learning algorithms when used to make predictions, the dataset was divided into training and test sets. In this way, the results  
Figure 7: Compressor status: running and down.  
allow for a comparison of the effectiveness of the machine learning algorithms for the given predictive modeling problem. After its designation as a target variable, the two data sets--80% for the training set and 20% for the test set--were constructed. The division of the dataset into training and evaluation is presented in the following figure (Figure 8).
## 4 Hierarchical Structuring of CSFs
### Partitioning the Reachability Matrix (R) of CSFs  
Factors set \(R(S_{i})\) and \(A(S_{i})\) are calculated to partition the reachability matrix. \(R(S_{i})\) refers to a set of CSFs on which the CSF \(i\) has an impact. \(A(S_{i})\) refers a set of CSFs, which impact CSF \(i\). The Common Set \(C(S_{i})\) is obtained by Equation (1).  
\[C(S_{i})=R(S_{i})\cap A(S_{i}) \tag{1}\]  
If \(C(S_{i})=R(S_{i})\), then the CSFs that are in a common \(L_{i}\), will be located on the same level of the ISM (Equation (2)), and will be taken out from the set before the next partition.  
\[L_{i}=\{S_{i}\,|\,C(S_{i})\,=R\,(S_{i})\} \tag{2}\]  
The process is repeatedly completed to order the CSFs at different levels. The detailed calculating process is presented in Table 4.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**CSF (\(i\))** & \(R(S_{i})\) & \(A(S_{i})\) & \(C(S_{i})\) & \(L_{i}\) \\ \hline \multicolumn{5}{c}{\(L1=\{7,11,15\}\)} \\ \hline
1 & 1,6,7,10,11,15,16, & 1 & 1 & \\
2 & 2,6,7 & 2 & 2 & \\
3 & 3,4,5,7 & 3 & 3 & \\
4 & 4,7 & 3,4,5,14,17 & 4 & \\
5 & 4,5,7 & 3,5 & 5 & \\
6 & 6,7 & 1,2,6 & 6 & \\
7 & 7 & 1–7,14,17 & 7 & 7 \\
8 & 8,10,11 & 8,9 & 8 & \\
9 & 8,9,10,11 & 9 & 9 & \\
10 & 10,11 & 1,8,9,10,13,14,16,17,18 & 10 & \\
11 & 11 & 1,8–14,16,17,18 & 11 & 11 \\
12 & 11,12 & 12 & 12 & 12 \\
13 & 10,11,13,15,16 & 13,18 & 13 & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Decomposition of reachability matrix (\(R\)).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c} \hline \hline
** CSFs (\(ii\))** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** & **12** & **13** & **14** & **15** & **16** & **17** & **18** & **Driver Power** \\ \hline  
1 & & 1 & & 1 & 1 & 1 & & 1
# Vulnerability of Satellite Quantum Key Distribution to Disruption from Ground-Based Lasers
## 2 Methods  
The operation of a QKD link is highly susceptible to attenuation of the transmitted signal, and to the addition of due to because of the extreme sensitivity of the QKD receiver. Because of this sensitivity, the QKD link from _Micius_ is unable to operate during the day due to background light, and _Micius_ experienced a reduced key rate (increase in quantum bit error rate, QBER, from approximately 0.01 to 0.03) as a result of light pollution from Beijing 110 km away [7]. We show that the extreme sensitivity of the QKD receiver to background noise (excess photons) means that photons from a ground-based laser, scattered from the transmitting satellite, can be detected by the QKD receiver on the ground and reduce, or completely disrupt, the generation of the quantum key.  
Because of the large parameter space for future satellite QKD architectures (e.g., orbital altitudes, QKD protocol and future technology, and satellite geometry), we use the _Micius_ satellite as a case study to determine what effect optical power levels of 100 W and 1 kW launched from ground-based laser terminals have on reducing, or completely disrupting, the QKD.  
We semi-empirically develop a scattering model for the QKD satellite. We then use the orbital dynamics of the satellite and atmospheric free-space link loss calculations to estimate the fraction of photons launched from a ground based laser, reflected off the satellite, that are received at the QKD ground station. We then analyse what effect this excess photon rate has on reducing the generation of a secure quantum key.
# Visible Light Communication: An Investigation of LED Non-Linearity Effects on VLC Utilising C-OFDM
## 2 Test Setup  
On the transmitter side, a block diagram for each of the OFDM systems tested is shown in Figure 2. Firstly, the architectures were very similar and the C-OFDM (blue) and F-OFDM (red) variations were slightly different from the conventional OFDM approach (green). An independent data stream (d) was mapped onto the modulation alphabet for all systems, which was selected specifically for the format. For the F-OFDM and C-OFDM systems, a real-valued modulation format, such as a pulse amplitude modulation (PAM), had to be considered because the cosine transform accepted a real input only and produced a real output. For the conventional OFDM system, quadrature amplitude modulation (QAM) could be utilised. The number of bits-per-symbol was, therefore, set to kPAM = kQAM/2 to preserve the equivalent bit rate. Moreover, the DCT transform was spacing the subcarriers by df = 1/2T [6; 19; 21], although in IFFT, the spacing between the subcarriers was (1/T). Furthermore, all the subcarriers N carried the information using PAM in terms of C-OFDM  
Figure 2: System block diagram.  
and F-OFDM. However, in OFDM, N/2 subcarriers carried information, and N/2 was conjugated to obtain the real output of IFFT using QAM. It is worth mentioning that a fair comparison was performed with F-OFDM; the OFDM was added as a reference.  
For each scheme, the data were subsequently passed through a serial-to-parallel converter (\(c\)) prior to the first scheme, the inverse C-transform in the case of C-OFDM, or in the second one IDCT was applied in the case of F-OFDM (X). Finally, Hermitian symmetry (HS) generation and transformation through IFFT in the case of OFDM formulated the output of the IFFT (\(X^{\star}\)), a real time-domain signal. HS was considering an additional stage compared to C-OFDM and F-OFDM. The HS concept was defined as follows:  
\[S_{m}\left\{\begin{array}{ccc}0&m=0\\ X_{m}&0<m<N/2\\ 0&m=N/2\\ X_{m}^{\star}&N/2<m<N-1\end{array}\right.. \tag{1}\]  
where the \(S_{m}\) is the HS output, \(X_{m}\) is the \(m\)th parallel data stream and \(N\) is the number of subcarriers (see Figure 3), then zero-padding (ZP) was proposed as an interesting alternative to cyclic prefix (CP), as presented in [22; 23].  
The proposed system C-OFDM was utilising the C-transform instead of using FFT. The C-transform is a unitary transform which combines the effect of these two transforms, namely, WHT and DCT.
## 2 Materials and Methods
### Microbial Community Analysis  
The anammox granules were collected at the end of the ANAMMOX activity test. The DNA of all samples was extracted using DNeasy PowerSoil Kit (DNeasy Kits, DEV) on a clean bench to prevent contamination from outside at room temperature. The extracted DNA concentration and purity of the extracted DNA were obtained from the absorbance using a NanoDrop spectrometer (ND-1000, Thermo Fisher Scientific, Waltham, MA, USA). The A260/A280 ratio was a good value between 1.8 and 2.1.  
PCR for identifying the microbial community was carried out using amplified 16S rRNA by requesting a combination of the primer set Amx338F (5\({}^{\prime}\)-ACT CCT ACG GGC AG3\({}^{\prime}\)) and Amx806R (5\({}^{\prime}\)-GAC TAC HCH CGT WCT AT-3\({}^{\prime}\)). The PCR conditions were as follows: 95 \({}^{\circ}\)C for 3 min, followed by 30 cycles of denaturation at 95 \({}^{\circ}\)C for 30 s, 55 \({}^{\circ}\)C annealing for 30 s and 72 \({}^{\circ}\)C for 45s extension, followed by a final extension at 72 \({}^{\circ}\)C for 10 min. The samples were implemented in accordance with the Illumina 16S rRNA sequencing library protocol (Macrogen Co., Seoul, Korea). Input gDNA is amplified with 16S Amx338F-Amx806R primers and a subsequent limited-cycle amplification step is performed to add multiplexing indices and Illumina sequencing adapters. The final products are normalized and pooled using the PicoGreen and the size of libraries are verified using the TapeStation DNA screentape D1000 (Agilent). The samples were then sequenced using the MiSeq\({}^{\text{TM}}\) platform (Illumina, San Diego, CA, USA). After the sequencing is completed, MiSeq raw data is classified by sample using an index sequence. Sequences less than 400 bp in length were removed. The obtained sequence is CD-HIT-EST based on the operational taxonomic units (OTUs) analysis program CD-HIT-OUT. Low quality sequences and ambiguous sequences were considered as sequencing errors. After removing the chimera sequence, and the like, clustering sequences having more than 97% sequence similarity to the species level OTU was formed.
# Analytical Model to Calculate Radial Forces in Permanent-Magnet Synchronous Machines
## 4 Validation  
The analytical model was validated by both FEM simulations and experimental tests performed over two prototypes. The configurations of the prototypes were the ones listed in Table 1.
# Spatial Data-Based Automatic and Quantitative Approach in Analyzing Maintenance Reachability
## 3 Materials and Methods
### Generation of the Arm's Maximum Solution Set
#### 3.1.3 Discretization of the Continuous Arm Posture Space  
According to the analysis, \(\theta_{5}\), \(\theta_{6}\), \(\theta_{7}\) do not appear in the formula. The changes of \(\theta_{5}\), \(\theta_{6}\), \(\theta_{7}\) will not affect the positions of the shoulder joint and elbow joint once the hand is fixed. Only \(\theta_{1}\), \(\theta_{2}\), \(\theta_{3}\), \(\theta_{4}\) can affect the positions of the shoulder joint and elbow joint.  
Four sets can be obtained by taking the values of these four angles from the upper limit to the lower limit every n-degrees. These four sets are the values of \(\theta_{1}\), \(\theta_{2}\), \(\theta_{3}\), \(\theta_{4}\), which include \(90/n\), \(160/n\), \(186/n\), \(142/n\) angle values, respectively.  
Taking the values from the four sets can obtain a set containing four angle values. Iterating through all the value combinations can obtain \(\text{N}=\frac{90\times 160\times 186\times 142}{n!}\) different value sets. By placing these value sets into the expressions for shoulder joint coordinates and elbow joint coordinates, the elbow joint and shoulder joint positions that correspond to the value sets can be obtained. The arm pose can be expressed by the elbow joint and shoulder joint positions. In this way, the continuous arm posture change space can be discretized. These discrete solutions constitute the initial solution set of the arm pose that can move freely in the barrier-free space when the hand is fixed.
# Design of Cislunar Navigation Constellation via Orbits with a Resonant Period
## 4 Simulation Analysis
### DOP Value Distribution Analysis of Resonant Constellations
#### 4.2.2 Performance Distribution Analysis in the Lunar Region  
An analysis of the DOP performance for the lunar region reveals the constellation's trajectory and mean DOP distribution, as illustrated in Figure 11. The spherical distribution at a radius of 2000 km is shown in Figure 12.  
Figure 11: Variation in near-Earth region’s mean DOP value with a spherical radius.  
Figure 12: Variation in near-Earth region’s mean DOP value with a spherical radius.  
Figure 10: Variation in near-Earth region’s mean DOP value with a spherical radius.  
From the figures, the 2000 km radius sphere exhibits dual symmetry characteristics, but its distribution pattern is opposite to that of the near-Earth region: the DOP values within the 100\({}^{\circ}\)-300\({}^{\circ}\) longitude range are notably lower.  
Similarly, calculating the mean DOP values for each spherical layer and analyzing their relationship with the radius yields Figure 13. The results indicate that the mean DOP values generally increase with spherical radius, although anomalies occur at 4000 km, 7000 km, and 10,000 km radii. Overall, allt he mean DOP values remain below 5.5, demonstrating that this configuration provides high-precision navigation and positioning services across the entire lunar region.
# Concrete Dam Deformation Prediction Model Research Based on SSA-LSTM
## 4 Case Study  
### Project Overview  
The object of this study is a 112 m high crushed concrete gravity dam with a crest elevation of 153.00 m, a crest width of 6 m, and a total installed capacity of 193.2 MW. The reservoir has a certified flood level of 151.88 m, a normal storage level of 150.00 m, and a dead water level of 130.00 m. It has a total storage capacity of 717.3 million m\({}^{3}\) and consists of 10 dam sections. The main project started in September 2007 and was completed in November 2011. The dam is equipped with tension lines for monitoring downstream deformation at elevations of 153 m, 120 m, and 88 m in the corridor. Additionally, eight inverted plumb line measurement points were installed at elevations of 153 m, 120 m, 88 m, 60 m, and 43 m on both the left and right banks of the dam. Tie lines were placed in  
Figure 3: SSA–LSTM model prediction process.  
the corridor at 153 m, 120 m, and 88 m for monitoring downstream deformation as well. The tie line measurement points were numbered EX3-1a to EX3-5a in the corridor at 88 m and EX2-1a to EX2-8a in the corridor at 120 m, as shown in Figure 4. Based on preliminary observation of the monitoring data, it was noted that most of the instruments exhibited larger displacements in the downstream direction. Therefore, the training and prediction of the monitoring model focused mainly on downstream displacement deformation. For this study, data from 5 May 2018 to 6 January 2022 were selected. Four measurement points, namely EX3-4a, EX3-3a, EX2-7a, and EX2-6a, were chosen, each with approximately 180 actual raw data points. Data were measured approximately four times per month. The training set comprised the first 80% of the data, while the remaining 20% was used as the test set. The water level change process line during the selected time period and the actual measured deformation change process line of the EX3-4 measurement point are presented in Figure 5.
## 4 Result and Discussion
### UAV Movement Pattern  
Two UAV movement patterns were evaluated in this study. They are the circular and random patterns. For circular movement, the UAV continuously moves within the designated coverage area in a circular path, transmitting data packets to the ground station and recording the QoS parameters for the transmission. The circular and random movement patterns of UAVs are illustrated in Figure 5.  
In the real world, UAVs do not always follow a predetermined or linear path. External factors such as wind, changes in mission objectives, or obstacles may cause unpredictable changes in UAV trajectories. Random movement helps to simulate this kind of uncertainty. It helps to test how the system performs under random, often challenging movement conditions. This is important for ensuring that the communication network can handle a variety of situations, not just the ideal ones.  
The UAV is set to operate at speeds ranging from 1 to 15 m per second, covering an area with a size of 400 m \(\times\) 400 m. The analysis shows that these UAV movement patterns effectively cover almost the entire designated area and maintain communication with the ground station. Circular and random patterns are selected in this study since they are commonly employed for communication between UAVs and ground stations.  
A circular path allows the UAV to maintain a constant distance from a central point where the ground station is located, ensuring consistent signal strength and coverage. This can be advantageous for maintaining strong and stable communication links, especially in areas where the central location needs uninterrupted connectivity. Random UAV movement enables the testing of the robustness of communication protocols, coverage, and network design. It helps to evaluate how effectively the communication system handles variable distances, interference, and fluctuations in signal strength caused by changing UAV positions. This approach enables the simulation of how effectively the network maintains QoS despite constant changes in UAV positions.
## 4 Nanobielectronics  
Current developments in nanoelectronics [56] and nanobiotechnology [57] are providing feasible development pathways to enable molecular machine manufacturing, including embedded and integrated devices, which can comprise the main sensing, actuation, data transmission, remote control uploading, and coupling power supply subsystems, addressing the basics for operation of medical nanorobots.  
A recent actuator with biologically-based components has been proposed [58]. This actuator has a mobile member that moves substantially linearly as a result of a biomolecular interaction between biologically-based components within the actuator. Such actuators can be utilized in nanoscale mechanical devices to pump fluids, open and close valves, or to provide translational movement.  
To help control nanorobot position, a system for tracking an object in space can comprise a transponder device connectable to the object. The transponder device has one or several transponder antennas through which a transponder circuit receives an RF (radio frequency) signal. The transponder device adds a known delay to the RF signal, thereby producing RF response for transmitting through the transponder antenna [59]. A series of several transmitters and antennas allow a position calculator, associated with the transmitters and receivers, to calculate the position of the object as a function of the known delay, and the time period between the emission of the RF signal and the reception of the RF response from the first, second and third antennas.  
Nanotechnology is moving fast towards nanoelectronics fabrication. Chemically assembled electronic nanotechnology provides an alternative to using complementary metal oxide semiconductor (CMOS) for constructing circuits with feature sizes in the tens of nanometers [60]. A CMOS component can be configured in a semiconductor substrate as part of the circuit assembly [24]. An insulating layer is configured on the semiconductor substrate, which covers the CMOS component. A nanoelectronic component can be configured above an insulating layer. If several nanoelectronic components are provided, they are preferably grouped in nanocircuit blocks [24].  
Biosensors are currently used to incorporate living components, including tissues or cells which are electrically excitable or are capable of differentiating into electrically excitable cells, and which can be used to monitor the presence or level of a molecule in a physiological fluid [61]. CNTs (carbon nanotubes) and DNA (deoxyribonucleic acid) are recent candidates for new forms of nanoelectronics [62]. These are combined to create new genetically programmed self-assembling materials for facilitating the selective placement of CNTs on a substrate by functionalizing CNTs with DNA. Through recombinant DNA technology, targets labeled with distinct detectable biomarkers can be defined, such as fluorescent labels, enzyme labels, or radioactive patterns, and employed as suitable protein transducers [63].
# Online Streaming Feature Selection via Conditional Independence
## 3 Framework for Streaming Features Filtering
### Framework for Filtering Conditional Independence
#### 3.2.3 Filtering of Multi-Conditional Independence  
In filtering of single-conditional independence, if a feature is still not redundant, it is retained in the \(CFS\). Therefore, we use the filtering of multi-conditional independence to identify surplus parts of redundant features in the \(CFS\). After filtering of multi-conditional independence, the remaining features in the \(CFS\) become strongly relevant or non-redundant.  
**Filtering of multi-conditional independence:** When a new feature is merged into the \(CFS\), filtering of multi-conditional independence is started. The filtering steps are as follows:  
Step (1): \(C\) is a class attribute; for each \(f\in CFS\), \(S_{i}\subseteq CFS/\{f\}\),  
_if_\(\exists\)\(S_{i}\), \(s.t.\)\(f\bot C\)\(|S_{i}\), then \(CFS=CFS/\{f\}\).  
Step (2): If \(\forall f\in CFS\), \(S_{i}\subseteq CFS/\{f\}\), \(s.t.\)\(f\bot C\)\(|S_{i}\), return \(CFS\).  
Figure 5: Filtering of single-conditional independence 2.  
**Proposition 4**.: _For a class attribute, \(C\), the candidate feature set CFS goes through filtering of multi-conditional independence. If \(\forall f\in CFS\), \(\forall S_{i}\subseteq CFS/\{f\}\), s.t. \(f\bot C|S_{i}\), then, \(f\in MB(C)\)._  
Proof.: Suppose \(CFS\) has already been filtered through the filtering of multi-conditional independence. \(\forall f\in CFS\), \(\forall S_{i}\subseteq CFS/\{f\}\), s.t. \(f\bot C|S_{i}\). Because the \(MB\) (\(C\)) is a subset of \(CFS\), \(\exists S_{i}=MB\) (\(C\)), s.t. \(f\bot C|MB(C)\). According to **Definition 2**, if \(f\bot C|MB(C)\), \(f\
otin MB\) (\(C\)). Therefore, \(f\in MB(C)\).
## 4 Discussion
### Comparison of the Results  
A summary of key parameter estimation, knowledge-based, and optimization methods used in fault detection, isolation, and identification (FDII) is presented in Table 21, highlighting their real-time applicability, computational complexity, robustness to noise and transients, and overall FDII capability.  
As shown in Table 21, each method presents a trade-off between real-time capability, complexity, and fault coverage. The proposed SWA and SIA approaches demonstrate a favorable balance, offering full diagnostic functionality with low-to-moderate complexity and strong robustness to noise.