esa-sceva/satcom-chunk-collection huggingface dataset:
# Using Statistical Test Method to Establish a Decision Model of Performance Evaluation Matrix
## 2 Unbiased Estimators and Statistical Hypothesis Test
### Satisfaction Index  
First, we assume that the satisfaction sample data of the customer's \(i\)th service item is \(\left(X_{i,1},\cdots,\,X_{i,j},\cdots,\,X_{i,n}\right)\), \(i=1,2,\ldots,q\). Then, the estimator of the satisfaction index for service item \(i\) is expressed as follows:  
\[\hat{\theta}_{i}^{S}=\frac{1}{n}\times\sum_{j=1}^{n}X_{i,j}. \tag{3}\]  
The expected value of \(\hat{\theta}_{i}^{S}\) is  
\[\begin{array}{rl}E[\hat{\theta}_{i}^{S}]&=\frac{1}{n}\times\sum_{j=1}^{n}E[ X_{i,j}]\\ &=\frac{1}{n}\times\sum_{i=1}^{n}\int\frac{1}{x(\frac{\Gamma(\theta_{i}^{S}+ \theta_{i}^{S})}{\Gamma(\theta_{i}^{S})\Gamma(\theta_{i}^{S})}x^{S}}x^{S}(1-x )^{S}\hat{\theta}_{x}\\ &=\frac{1}{n}\times\sum_{i=1}^{n}\left(\frac{x}{S+\theta_{i}^{S}}\right)\\ &=\theta_{i}^{S}.\end{array} \tag{4}\]Thus, \(\hat{\theta}_{i}^{S}\) is an unbiased estimator of \(\theta_{i}^{S}\).  
\[\theta_{S}=\frac{1}{q}\sum_{i=1}^{q}\theta_{i}^{S}. \tag{5}\]  
Then, based on the concept of total quality management, when the satisfaction index value of service item \(i\) is greater than or equal to the average value \(\theta_{S}\), it is fine to maintain the current situation. When the satisfaction index value of service item \(i\) is smaller than the average value \(\theta_{S}\), then the service item \(i\) needs to be improved [20; 21]. Based on the stated above, the hypotheses of the statistical test for satisfaction index \(i\) are as follows:  
\[\text{null hypothesis }H_{0}:\ \theta_{i}^{S}\geq\theta_{S}; \tag{6}\]  
\[\text{alternative hypothesis }H_{1}:\ \theta_{i}^{S}<\theta_{S}. \tag{7}\]  
We assume that the test statistic is the unbiased estimator \(\hat{\theta}_{i}^{S}\) with significance level \(\alpha\); then, the critical region for satisfaction index \(i\) is  
\[CR_{Xi}=\Big{\{}\theta_{i}^{S}\Big{|}\hat{\theta}_{i}^{S}<C_{X0i}\Big{\}}. \tag{8}\]  
Thus, we have  
\[p\Big{(}\hat{\theta}_{i}^{S}<C_{X0i}\Big{|}\hat{\theta}_{i}^{S}=\theta_{S} \Big{)}=\alpha. \tag{9}\]  
Equivalently,  
\[p\Big{(}Z_{Xi}<\frac{\sqrt{n}(C_{X0i}-\theta_{S})}{S_{Xi}}\Big{)}=\alpha, \tag{10}\]  
where  
\[S_{Xi}=\sqrt{\frac{1}{n-1}\times\sum_{j=1}^{n}\left(X_{i,j}-\overline{X}_{i} \right)^{2}} \tag{11}\]  
and  
\[Z_{Xi}=\frac{\sqrt{n}\big{(}\theta_{i}^{S}-\theta_{S}\big{)}}{S_{Xi}} \tag{12}\]  
are approximate values, and they are distributed as standard normal distributions when sample size \(n\) is large. Then, we have  
\[\frac{\sqrt{n}(C_{X0i}-\theta_{S})}{S_{Xi}}=-z_{\alpha},\]  
where the critical value \(C_{X0i}\) is displayed below:  
\[C_{X0i}=\theta_{S}-z_{\alpha}\frac{S_{Xi}}{\sqrt{n}}. \tag{13}\]
## 3 Shaking Table Test
### Model Building  
The original structural components were numerous and complex, while the reduced space of the model structure was small, making construction and fabrication difficult. Therefore, appropriate simplification should be made without changing the mechanical mechanism of the structure when making a shaking table test model. In this model, non-main structural load-bearing components such as guardrails and curtain wall support components were omitted. Researchers simplified the consolidation of some load-bearing components, such as the consolidation of shear wall openings with constant lateral force stiffness. The model production process is shown in Figure 7.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Relation** & **Scaling Factors** \\ \hline Length & \(\text{S}_{\text{L}}\) & 0.025 \\ Elastic modulus & \(\text{S}_{\text{E}}\) & 0.2 \\ Acceleration & \(\text{S}_{\text{a}}\) & 1.8 \\ Density & \(\text{S}_{\text{P}}=\text{S}_{\text{E}}/(\text{S}_{\text{a}}\text{S}_{\text{L}})\) & 4.44 \\ Stress & \(\text{S}_{\text{\sigma}}=\text{S}_{\text{E}}\) & 0.2 \\ Mass & \(\text{S}_{\text{m}}=\text{S}_{\text{P}}\text{S}_{\text{L}}\)\({}^{3}\) & 0.0000694 \\ Concentrated force & \(\text{S}_{\text{P}}=\text{S}_{\text{o}}\text{S}_{\text{L}}\)\({}^{2}\) & 0.000125 \\ Frequency & \(\text{S}_{\text{f}}=(\text{S}_{\text{u}}/\text{S}_{\text{L}})^{0.5}\) & 8.485281 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Main similarity scaling factors.  
Figure 7: Model production process: (**a**) reinforcement cage binding of shear wall structure; (**b**) welding of steel truss structure; (**c**) concrete pouring of shear wall structure; and (**d**) model production completed.  
Based on the parameters of the viscous damper in the FE Model calculation results and the above similarity, a scaled viscous damper was fabricated and installed in a steel frame (Figure 8). Due to the need for disassembly and assembly during the test, both ends of the viscous damper were fixed with removable pins.
# Polarization-Insensitive Phase Modulators Based on an Embedded Silicon-Graphene-Silicon Waveguide
## 3 Graphene-Based Polarization-Insensitive Phase Modulator
In a practical fabrication process [34; 35], some important processes of the designed polarization-insensitive phase modulator based on embedded silicon-graphene-silicon waveguide are as follows: firstly, the ways of acquitting, transferring, and processing monolayer graphene are very important. Through chemical vapor deposition, a chip-sized graphene layer can be obtained on Cu film and is then protected by 200-nm thick poly (methyl methacrylate) film. The graphene sheet is rinsed and transferred on the fabricated Si waveguide using the FeCl\({}_{3}\) solution. E-beam lithography is then used to define the active region, and oxygen plasma is applied to remove the undesired graphene on one side of the waveguide, leaving the other side for metallization. Secondly, the Si waveguide fabrication with a groove can be realized by E-beam lithography, combined with the inductively coupled plasma etching process. Thermal evaporation is used to deposit metal for electrodes.
## 3 Types of Supplementary Dampers and Their Characteristics
### Tuned Mass Dampers
#### 3.5.4 Types of Tuned Mass Dampers  
There are several types of tuned mass dampers, each with unique features suited to different applications:  
(a) Pendulum-type TMDs utilize a pendulum mechanism for the auxiliary mass. These are particularly suitable for tall structures with low fundamental frequencies, where the long pendulum length can be accommodated.  
(b) Spring-mounted TMDs use mechanical springs to support the auxiliary mass. These are appropriate for structures with higher fundamental frequencies and where vertical space might be limited.  
(c) Tuned liquid dampers (TLDs) use liquid motion in containers to provide the mass-damping effect. While simpler to implement, they are generally less efficient than solid mass TMDs. However, they can be advantageous in situations where the maintenance of moving parts is challenging [120].  
(d) Multiple tuned mass dampers (MTMDs) employ multiple smaller TMDs tuned to slightly different frequencies. This approach provides a broader frequency bandwidth and improved robustness, making the system less sensitive to potential off-tuning.
# Integrating Classical Preprocessing into an Optical Encryption Scheme
## 1 Introduction  
The fast development of telecommunications and the increase of (potentially sensitive) data stored and exchanged by companies or individuals through public networks has made cryptography particularly important to guarantee the privacy, integrity and authenticity of users. The current approach to protect data involves the use of combinations of secret key [1] and public-key solutions [2], which base their security on empirical evidence or on the difficulty of solving certain mathematical problems respectively. A different approach has been explored since the 1980s, after [PERSON] and [PERSON] [3] proposed their seminal quantum key distribution protocol. Today research in quantum cryptography goes well beyond quantum key distribution [4; 5].  
Quantum-based cryptographic schemes have the conceptual appeal that security guarantees can potentially be argued based on fundamental laws of physics. However, popular quantum protocols, based on [3; 6], commonly rely on single photon sources, which can be challenging to implement. As a result, in recent years, the idea of using mesoscopic coherent states has gathered interest, and the AlphaEta protocol is a prominent example of such a design (see, e.g., [7; 8; 9; 10]). Its security has been the topic of several papers, including [9; 11; 12; 13; 14]. AlphaEta is also cited in Lloyd's work on quantum enigma machines [15]; [PERSON] notes the open problem _to construct a provably secure quantum enigma machine using linear optics and coherent states_. As an alternative to AlphaEta, other optical cryptographic solutions were considered, including the use of double random phase encoding (DRPE) [16; 17; 18]. It deservesnoting that not only has academia been focusing on quantum technologies, but several industries have started to commercialize quantum cryptographic tools [5], specifically for quantum key distribution and quantum random number generation.  
Capturing security guarantees that rely on physical assumptions with common security models for encryption poses somewhat of a challenge, especially when trying to integrate computationally secure primitives as well. [PERSON] and [PERSON] rightfully point out that protocols based on quantum optical noise appear to be a wonderful source of research questions [10], though. It is tempting to harvest both the strength of existing (computationally secure) efficient cryptographic constructions and the features offered by an AlphaEta-type protocol, where an eavesdropper faces an additional, physical, hurdle.
# Design and Performance Analysis of an In-Band Full-Duplex MAC Protocol for Ad Hoc Networks
## 3 Proposed MAC Protocol: AdHoc-FDMAC  
This MAC is proposed for FD communications using the IBFD technique, where all the nodes are FDNs. This section is described in two subsections, namely the control frame and data transmission.
## 3 Results and Discussion
### Scenario Analysis
#### 3.4.1 Scenario 1 (3 Access Points)  
With three access points, we were able to cover 27% of the campus with a signal level higher than \(-\)70 dBm, and 43% of the surface is covered by a signal level between \(-\)80 dBm and \(-\)70 dBm (Figure A3 in Appendix A).  
These three access points consume 360 Wh. Based on an operating duration of 10 h, the energy consumption of electricity produced from fossil energy (natural gas) produces 150 g of CO\({}_{2}\). So, if we replace the source of electricity with solar energy, we can reduce the amount of CO\({}_{2}\) to 19.8 g.
# Remote Sensing of Forest Burnt Area, Burn Severity, and Post-Fire Recovery: A Review
## 4 Results and Discussions
### Monitoring of Post-Fire Forest Recovery
This study showed that vegetation recovery of Eucalyptus, Callitris, and Acacia species was very fragmented. The regrowth of mid-story vegetation was 22-40% lower in BA of low and moderate compared to high fire severity [290]. In order to quantify how wildfire impacts forest recovery, several researchers have examined the combined use of optical sensors imagery and LiDAR data [225; 226; 230; 291]. [PERSON] et al. [292] studied the recovery rate of mixed pine-oak (_Pinus rigida_) forest in Long Island (NY) three years after wildfire by leveraging 1 m of simultaneous airborne imaging spectroscopy and LiDAR and 2 m of satellite multi-spectral imagery. They presented a new method for the estimation of tree species (oak vs. pine) recovery rates to different BS levels, derived from large spatial-temporal scales. According to the study, pine trees were more resilient to high BS with a higher recovery rate (12% per year) than oak (_Quercus alba_ and _Quercus occinea_) species (4% per year) [292]. [PERSON] et al. [293] estimated vegetation recovery in the Canadian boreal spans following wildfire, combining LiDAR, MODIS GPP, and a national Landsat-derived record of forest change. The research showed difference in forest canopy structure across the boreal spans, while correlations to GPP were stronger for forest stands by 15-25 years post-wildfire. Combining different RS images (optical, SAR, and LiDAR) with BS maps provides a more comprehensive assessment of post-fire forest recovery patterns. Therefore, further studies based on multi-source analysis are still needed with the inclusion of structural forest ground variables, species composition, environmental conditions, vegetation indexes, and BS [42; 44; 46].
## 4 Results
### Accuracy Assessment at Two Sites
#### 4.3.2 Comparison with the Otsu Method at the WeiFang Site
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Type**} & \multirow{2}{*}{**Threshold**} & \multirow{2}{*}{\begin{tabular}{c} **Overall** \\ **Accuracy** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Kappa** \\ **Coefficient** \\ \end{tabular} } & \multicolumn{2}{c}{**Commission Error**} & \multicolumn{2}{c}{**Omission Error**} \\ \cline{5-8}  & & & **Land** & **Water** & **Land** & **Water** \\ \hline \multirow{3}{*}{Image} & AT-EWC & 95.50\% & 0.9080 & 3.57\% & 2.65\% & 3.57\% & 2.65\% \\  & Otsu & 94.50\% & 0.8876 & 4.39\% & 6.98\% & 5.22\% & 5.88\% \\  & (\(-\)) & (0.01) & (0.02) & (\(-\)0.01) & (\(-\)0.04) & (\(-\)0.02) & (\(-\)0.03) \\ \hline \multirow{3}{*}{\begin{tabular}{c} 20 km \(\times\) 20 km \\ Block (High) \\ \end{tabular} } & AT-EWC & 96.00\% & 0.9199 & 2.91\% & 5.15\% & 4.76\% & 3.16\% \\  & Otsu & 95.00\% & 0.8997 & 4.76\% & 5.26\% & 4.76\% & 5.26\% \\  & (\(-\)) & (0.01) & (0.02) & (\(-\)0.02) & (0.00) & (0.00) & (\(-\)0.02) \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} 20 km \(\times\) 20 km \\ Block (Middle) \\ \end{tabular} } & AT-EWC & 96.00\% & 0.8947 & 2.03\% & 9.62\% & 3.33\% & 6.00\% \\  & Otsu & 84.00\% & 0.6364 & 2.42\% & 38.16\% & 19.33\% & 6.00\% \\  & (\(-\)) & (0.12) & (0.26) & (0.00) & (\(-\)0.29) & (\(-\)0.16) & (0.00) \\ \hline \multirow{3}{*}{
\begin{tabular}{c} 20 km \(\times\) 20 km \\ Block (Low) \\ \end{tabular} } & AT-EWC & 97.50\% & 0.8697 & 1.12\% & 13.64\% & 1.68\% & 9.52\% \\  & Otsu & 81.00\% & 0.4136 & 0.69\% & 66.07\% & 20.56\% & 5.00\% \\ \cline{1-1}  & (\(-\)) & (0.17) & (0.46) & (0.00) & (\(-\)0.52) & (\(-\)0.19) & (0.05) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison with Otsu method for the WeiFang site. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Data**} & \multirow{2}{*}{\
\begin{tabular}{c} **Overall** \\ **Accuracy** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Kappa** \\ **Coefficient** \\ \end{tabular} } & \multicolumn{2}{c}{**Commission Error**} & \multicolumn{2}{c}{**Omission Error**} \\ \cline{5-8}  & & & **Land** & **Water** & **Land** & **Water** \\ \hline
1 June 2019 & 95.00\% & 0.7790 & 2.87\% & 19.23\% & 2.87\% & 19.23\% \\
13 June 2019 & 96.50\% & 0.8427 & 2.29\% & 12.00\% & 1.72\% & 15.38\% \\
25 June 2019 & 95.50\% & 0.7978 & 2.86\% & 16.00\% & 2.30\% & 19.23\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Accuracy estimate of Ji’An site using Sentinel-1.
## 8 Implementation
### Parameters for Interference Evaluation  
After detecting the channel of the public APs, then the safe wireless transmitter will switch to a proper channel to interfere with the target public APs. However, there are several adjacent channels that can interfere with the public APs. Which channel should the safe wireless transmitter be switched to so that the channel interference between the safe wireless transmitter and the target public APs maximum and the packet loss rate will be the maximum? In this paper, we will introduce four parameters to quantify the channel interference between the safe wireless transmitter and the target public APs, and they are the number of packets received, the packet loss rate, the interference strength and the active ratio [48], respectively.  
The definition of the four parameters is as follows:  
* The number of received packets: The parameters are obtained by actual experiments. What we have obtained is a sequence of CSI measurements, and the length of the sequence is the number of received packets.
* Packet loss rate: \[Packet\ Loss\ Rate=\frac{RV\ of\ RP-IV\ of\ RP}{RV\ of\ RP}\] (3) In the above equation, \(RV\ of\ RP\) represents the reference number of received packets, and the packets are obtained when the safe wireless transmitter and target public APs work on different channels and there exists no channel interference between them. \(IV\ of\ RP\) represents the interference number of received packets, and the packets are obtained when the safe wireless transmitter and the target public APs work on adjacent channels and there will exist adjacent channel interference between them. We assume that when the safe wireless transmitter and the target AP work on different channels and there exist no channel interference between them, the packet loss rate is zero.
* Interference strength: \[IS=\sum_{i=0}^{RP}\frac{RSSI_{i\_noise\ removal}}{RP}\] (4) In the above equation, \(RSSI_{i\_noise\ removal}\) represents the RSSI value of the \(i\)-th packet from which the noise has been removed, and \(RP\) represents the reference number of the received packets. The value of \(IS\) represents the interference strength between the safe wireless transmitter and the target public AP; when the value of \(IS\) is greater, the interference strength between the safe wireless transmitter and the target public APs is stronger.
* Active ratio: \[AR=\sum_{i=0}^{RP}U_{i},\]\[U_{i}=\begin{cases}if\frac{|RSSI_{i}+Noise_{i}|}{RSSI_{i,noise_{i},removul}}\geq 1,&U_{i}=1\\ other,&U_{i}=0\end{cases} \tag{5}\]  
In the above equation, \(RSSI_{i}\) represents the RSSI value of the \(i\)-th packet and \(Noise_{i}\) represents the noise value of the \(i\)-th packets; when the value of \(AR\) is greater, the noise contained in the received packets is lower, and the channel interference between the safe wireless transmitter and the target public APs will be weaker.
# A Preliminary Study on the Use of Xylit as Filter Material for Domestic Wastewater Treatment
## 3 Results and Discussion
### Efficiency of Wastewater Quality Indicators Removal
#### 3.2.4 Ammonium Nitrogen  
The average values of inflow NNH4 at stage I were as follows: 100.1 \(\pm\) 11.3 mg/L (\(n\) = 7), 93.5 \(\pm\) 7.6 mg/L (\(n\) = 7), and 92.5 \(\pm\) 8.9 mg/L (\(n\) = 7) for AH1, AH2, and SCR, respectively. In stage II, Ptot average inlet values were as follows: 2.8 \(\pm\) 0.6 mg/L (\(n\) = 5) and 2.5 \(\pm\) 0.6 mg/L (\(n\) = 5) for AH1 and AH2, respectively. One record (the last one) was rejected as a rough error (inflow concentrations: 0.13 mg/L for both columns and outflow concentrations: 1.37 mg/L and 1.35 mg/L for AH1 and AH2, respectively). The inflow NNH4 concentrations are presented in Tables 6 and 7 Figures 8 and 9.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**AH1**} & \multicolumn{3}{c}{**AH2**} \\ \cline{2-7}
**Sample** & **Inlet** & **Outlet** & **Efficiency** & **Inlet** & **Outlet** & **Efficiency** \\ \cline{2-7}
**No.** & **mg/L** & **mg/L** & **\%** & **mg/L** & **mg/L** & **\%** \\ \hline
1 & 3.7 & 1.7 & 54.1 & 3.2 & 2 & 37.5 \\
2 & 4.5 & 3 & 33.3 & 4.5 & 2.3 & 48.9 \\
3 & 1.65 & 0.6 & 63.6 & 1.55 & 1.27 & 18.1 \\
4 & 1.59 & 1.11 & 30.2 & 1.6 & 1.16 & 27.5 \\
5 & 2.53 & 0.95 & 62.5 & 1.71 & 0.88 & 48.5 \\ \hline Avg. & 2.8 \(\pm\) 0.6 & 1.5 \(\pm\) 0.4 & 48.7 \(\pm\) 7.1 & 2.5 \(\pm\) 0.6 & 1.5 \(\pm\) 0.3 & 36.1 \(\pm\) 6.0 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Inlet and outlet NNH4 and removal efficiencies at stage II.  
Figure 8: Inlet and outlet NNH4 and removal efficiencies at stage I.  
Figure 9: Inlet and outlet NNH4 and removal efficiencies at stage II.  
In experiment I, the average efficiencies of NNH4 removal were as follows: 98.5 \(\pm\) 0.13% (\(n\) = 6), 96.6 \(\pm\) 1.7% (\(n\) = 7), and 97.1 \(\pm\) 1.1% (\(n\) = 7), for AH1, AH2, and SCR, respectively. NNH4 removal efficiencies at stage I are presented in Table 6 and Figure 8.  
In experiment II, the average efficiencies of NNH4 removal were as follows: 48.7 \(\pm\) 7.1% (\(n\) = 5) and 36.1 \(\pm\) 6.0% (\(n\) = 5), for AH1 and AH2, respectively. NNH4 removal efficiencies at stage II are presented in Table 7 and Figure 9.  
Ammonium nitrogen removal efficiency was very high during experiment I and almost equal (taking into account average values) for both types of filling. Very high efficiency of ammonium nitrogen removal resulted from good oxygenation (due to natural ventilation) of the filter bed (a low hydraulic load of wastewater, relatively free air supply to both the surface zone and the bottom of filters). The results obtained in experiment I were comparable to results obtained by [PERSON] et al. [11] for sorbents (including xylit) with a pH between 7 and 9--about 87%.  
During experiment II, ammonium nitrogen removal efficiency was much lower, as compared to experiment I. It could result from very low inflow concentrations (greywater) and some minimum demand for nitrogen incorporated into bacterial cell biomass.
## 3 System Model
### Energy Model  
We used a simplified energy model shown in [15] for radio hardware energy dissipation transmitting an \(l\)-bit message (\(E_{TX}\)) with distance d as follows:  
\[E_{TX}(l,d)=\left\{\begin{array}{ll}l*E_{\text{elec}}+l*\in_{fs}*d^{2};\text{ d}\leq d_{0}\\ l*E_{\text{elec}}+l*\in_{mp}*d^{4};\text{ d}\geq d_{0}\end{array}\right. \tag{4}\]  
All parameter descriptions are in Table 1.  
When receiving data, the radio expends the following [35]:  
\[E_{RX}(l)=l*E_{elec} \tag{6}\]  
Additionally, the energy dissipated by the cluster head during a single frame is:  
\[E_{CH}=l~{}E_{elec}~{}(m-1)+l~{}E_{DA}~{}m+lE_{elec}+l\in_{mp}d^{4} \tag{7}\]  
Assuming that there are N nodes which are distributed uniformly, if there are k clusters, then on the average number of nodes per cluster, \(m=\dfrac{N}{k}\). Each CH dissipates energy by receiving signals from the nodes, collecting the signals, and transmitting the gathered signals to the BS. The energy required in each non-cluster-head node can be expressed by [35]:  
\[E_{non-CH}=lE_{elec}+l\in_{fs}d^{2}toCH \tag{8}\]
## 3 Methodology
### Data Processing  
Considering that the robotic manipulator has a total of \(J\) joints and that each joint has a sensor that monitors and records its consumed energy, the energy consumption data at a time instant \(t\) can be described by the set \(e_{t}=\{e_{t}^{1},\cdots,e_{t}^{J}\}\). Assuming that the manipulator performs one operation starting from the first time instant \((t=1)\) to the \(n^{th}\) instant \((t=n)\), the energy consumption sequence associated with this operation is defined as the set \(\{e_{1},\cdots,e_{n}\}\). To enrich the feature representation and fix the size of the input to the neural network, a sliding window technique is adopted, which splits the energy sequence into several overlapped segments. Each segment has a predetermined length \(T\), the value of which also represents the sliding window size. For the \(i^{th}\) segment \(S_{i}\), this is composed of \(\{e_{i-T+1},\cdots,e_{i-1},e_{i}\}\). In this manner, the overlap length between two consecutive segments is \(T-1\), ensuring an offset of one single time instance between them. Thus, a data sequence of length n is transformed into a total of \(n-T+1\) segments, making the input to the neural network one segment instead of a single data point. This design improves feature representation and detection performance by incorporating more statistical features [26].