esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Dual-Strategy Approach for Constructing Knowledge Graphs in the Home Appliance Fault Domain
## 4 Experiments
### Experimental Results and Analysis
As a result, the extraction of \"appliance types\" performs worse compared to other tasks. Overall, both the Bert-BiLSTM-CRF and ChatGLM4-plus models demonstrate strong performance in relation extraction, with F1-scores of 81.34% and 86.33%, respectively, both exceeding 80%. Additionally, their accuracy and recall rates are also at a high level. Among these, ChatGLM4-plus has more parameters and has absorbed more knowledge during training, resulting in significantly better language understanding, long text processing, and reasoning capabilities compared to ChatGLM4-flash and ChatGLM3. This makes it more suitable for tasks requiring high precision and complexity. In Chinese language tasks, \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Entity Recognition Models** & _P/\%_ & _R/\%_ & _F1/\%_ \\ \hline Bert-BiLSTM & 94.59 & 84.67 & 89.36 \\ RoBERTa-zh-BiLSTM & 96.42 & 83.80 & 89.67 \\ RoBERTa-zh-IDCNN & 96.35 & 86.74 & 91.30 \\ Bert-BiLSTM-CRF & 95.77 & 90.14 & 92.87 \\ RoBERTa-zh-IDCNN-CRF & 94.43 & 89.91 & 92.12 \\ RoBERTa-zh-BiLSTM-CRF & 95.44 & 90.43 & 92.87 \\ RoBERTa-zh-BiLSTM-Attn-CRF & **96.64** & **90.49** & **93.23** \\ \hline \hline \end{tabular} The best-performing model’s results are highlighted in bold.  
\end{table} 
Table 6: Entity recognition models. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Relation Extraction Models** & _P/\%_ & _R/\%_ & _F1/\%_ \\ \hline Bert-BiLSTM-CRF & 77.41 & 75.86 & 81.34 \\ ChatGLM3 & 46.09 & 67.53 & 54.79 \\ ChatGPT-3.5-turbo & 64.76 & 77.14 & 70.41 \\ ChatGPT-4o & 76.53 & 69.67 & 72.94 \\ ChatGLM-4-flash & 76.11 & 63.06 & 68.97 \\ ChatGLM4-plus & **87.13** & **85.54** & **86.33** \\ \hline \hline \end{tabular} The best-performing model’s results are highlighted in bold.  
\end{table} 
Table 7: Relation extraction models. it clearly outperforms ChatGPT-3.5-turbo, indicating the strong advantages of domestic large language models in processing Chinese text. Ultimately, the ChatGLM4-plus model was selected for relation extraction on a dataset of 3156 appliance fault repair segments to construct a knowledge graph.
## 3 Attacks on HWMP
### External Attacks
#### 3.1.5 Impersonation Attacks (Path Diversion)  
It is possible for an attacker to impersonate another mesh STA to initiate the path request. This impersonation is performed by manipulating the originator and target address fields. Figure 10 shows an example of how an impersonation attack can be used with a path diversion attack.  
Information about the transmitted frames is processed according to Figure 10 as follows:  
M: M \(\rightarrow\) Broadcast: PREQ[A - B][Metric = 10]  
M: M \(\rightarrow\) Broadcast: PREQ[B - A][Metric = 10]  
A: A \(\rightarrow\) M: PREP[A - B][Metric = 0]  
B: B \(\rightarrow\) M: PREP[B - A][Metric = 0]  
First, a malicious mesh STA M will broadcast a PREQ frame with the originator address set to B, the target address set to A, and the metric set to 10. Then, it will broadcast another PREQ frame with  
Figure 10: Example of an impersonation attack (path diversion).  
Figure 9: Example of an impersonation attack (loop).  
the originator address set to A, the target address set to B, and the metric set to 10. Both A and B will reply to the request to form the path {A\(\leftrightarrow\)M\(\leftrightarrow\)B}.
# Ranking of Electricity Accumulation Possibilities: Multicriteria Analysis
## 2 Comparative Description of Selected Energy Storage Technologies
### Hydrogen Energy Storage  
Gain potential is placed on hydrogen energy storage technology systems, considering the possibilities of using the technical solution as an independent energy supply system in energy-isolated areas. The use of hydrogen has several applications besides the energy sector. It can be used both as an admixture for liquefied gas after the methanization process and as a fuel effect in vehicles, it is also possible to turn it into methanol, a resource that can be used in industry. In the system, electricity is stored by electrolyzing water to produce hydrogen and oxygen, whereby oxygen is released and hydrogen is stored. However, to transfer electricity to the grid, hydrogen is re-electrified by combining hydrogen with oxygen. An important aspect is that heat and water are released as a by-product, which is a usable resource. Currently installed projects in Europe use alkaline electrolysers directly, ranging in size from a few kW to several hundred MW, with a short response time, effectively following load changes affected by wind farm output. It is also expected that hydrogen will be transferred for mobility purposes and wholesale through the gas network [20]. In addition, it should be mentioned that hydrogen energy storage technologies are being further developed for example, as hydrogen-biomethane, and hydrogen-methane storage systems, increasing the quality of syngas and biogas, however, this will not be discussed in more detail.
# Distribution Grid Stability--Influence of Inertia Moment of Synchronous Machines
## 3 Maximum Admissible Grid Frequency Oscillations  
If the response of a load angle's swing has to have an amplitude \(A\) as the maximum, the power spectral density \(S_{\omega}(\Omega_{0})\) of the grid angle frequency must not be greater than \(S_{\omega,max}(\Omega_{0})\)  
\[S_{\omega,max}(\Omega_{0})=\frac{P_{d}^{2}\,B^{2}}{I^{2}}\,\beta_{A}^{2}, \tag{8}\]  
where \(\mathrm{S}_{\omega,max}(\Omega_{0})\) is the upper limit of the power spectral density of a grid angle frequency (s\({}^{-2}\)).  
Substituting the variable \(\Omega_{0}\) from Equation (5) into Equation (8) for the parameter \(I\) yields  
\[S_{\omega,max}(\Omega_{0})=\frac{P_{d}^{2}\,B^{2}}{k^{2}}\,\beta_{A}^{2}\, \Omega_{0}^{4}. \tag{9}\]  
The maximum size of a flying wheel for which the amplitude of the load angle's swing does not exceed the given value \(\beta_{A}\) is thus determined.  
Courses for \(S_{\omega,max}(\Omega_{0})\) according to Equation (9) for the chosen \(\beta_{A}\) (e.g., 0.1, 0.15, and 0.2 rad) are drawn in Figure 3. These courses constitute the upper limits of power spectral density in the grid angle frequency fluctuations \(S_{\omega,max}(\Omega_{0})\) for the permitted \(\beta_{A}\). The courses of the upper limits are fitted with a scaled \(I\) according to Equation (5):  
\[I=\frac{k}{\Omega_{0}^{2}} \tag{10}\]  
The maximum value of the moment of inertia being placed on the shaft of a synchronous machine is given by the first intersection point (from the right) of the function \(S_{\omega,max}(\Omega_{0})\) for the permissible  
Figure 3: Influence of the fluctuation spectrum of the grid and inertial momentum \(I\) on the amplitude of angular oscillation.  
maximum of the load angle oscillation amplitude \(\beta_{A}\) with the course \(S_{\omega}(\Omega)\) measured in the grid section at the considered time.
## 5 Experiment and Results
### Evaluation of Level 1
#### 5.1.1 Number of Serviced Vehicles  
Figure 5 shows the results of our evaluation in terms of the number of vehicles serviced by STL, ITMS, and the traditional traffic light. When the inter-arrival time was small, congestion was more likely to happen. On the contrary, when the inter-arrival time was very large, congestion was less likely to happen. In Figure 5, it is evident that when the inter-arrival time was less than 10 s, the number of vehicles serviced by the proposed algorithm and ITMS was higher than that of the traditional method. In fact, for an inter-arrival time of 1 s, the number of vehicles serviced by the traditional method was less than half of that of the vehicles serviced by the proposed algorithm and ITMS. In the same figure, it is evident that as the inter-arrival time increased, the gap between them (STL/ITMS and traditional) in terms of the number of vehicles serviced became smaller. In fact, when the inter-arrival time reached around 20 s, all algorithms serviced the same number of vehicles. This implies that the proposed algorithm serviced a large number of vehicles compared to the traditional method in a high-congestion environment. ITMS also serviced a large number of vehicles compared to the traditional method in high-congestion scenarios. However, comparing ITMS and STL, the number of vehicles serviced by them was the same for all the inter-arrival times. This is expected in a scenario where there is only one street that is always congested. However, if there is no congestion, the performance of both systems tends to be close to the performance of the traditional one, as shown in the Figure 5.
# Current Trends on Green Wireless Sensor Networks
## 2 Energy Conservation
### Radio Optimization
#### 2.1.5 Energy-Efficient Cognitive Radio  
A cognitive radio (CR) is an intelligent radio that can dynamically choose a channel in the wireless spectrum and can set its transmission and reception parameters according to the channel status. The subjacent Software-Defined Radio (SDR) technology is intended to build fully reconfigurable wireless transceivers that automatically tune their communication parameters to the requirements of the network, resulting in improved context awareness. Nevertheless, SDR requires substantial power usage in comparison to conventional devices because of the greater complexity inherent to the sophisticated new functionalities. In this scenario, the design of energy-efficient CR sensor networks is a key challenge for the intelligent use of battery power.  
Recent cognitive radio studies are interested in the development of cross-layer approaches for MAC [48; 49; 50] and routing [51; 52]. The field of clustering protocols have been especially active. For example, the work in [53] surveys 12 papers until 2017 that use clustering topologies in CR sensor networks with a focus on energy consumption. Several subsequent works also use CR together with clustering protocols. In [54] the Learning Automata-based Multilevel Heterogeneous Routing (LA-MHR) scheme for WSNs is proposed, and [55] presents the novel approach to integrate the sensor nodes with CR nodes to forward data towards the sink using opportunistically licensed channels.
## 3 Results and Discussion
### Restrictions of the Proposed Algorithm  
Despite having several advantages, the proposed algorithm has a few noteworthy restrictions that are listed below:  
* The proposed method should be chosen only in the absence of a large and high-quality training set with considerable similarities and dissimilarities among the sample images. This restriction is given not because of its accuracy but because of its high computational time and adopted pixel-wise exhaustive image analysis mechanism. The proposed approach processes each image, and hence, its computation time might substantially increase in the presence of a large and high-resolution image dataset, although the identification error should remain the lowest [35; 36; 37]. However, in the presence of a large training set, deep CNNs might also suffer from high computational costs.
* Sometimes, in the presence of CT scan images containing micro-pores and fractures, it can be difficult to choose suitable \(l\), \(\alpha,~{}\beta,~{}\gamma\) and \(\delta\) values, where we must rely on expert judgement. However, the same problem remains in deep CNN, where we often need to decide on several model hyper-parameter values.
* Multi-objective algorithms pose higher complexity and computation in comparison to their single-objective counterparts.
# A 12 dBm B1 dB N-Path Notch Filter for Transmitter Leakage Suppression in Wideband Receiver
## 3 Proposed N-Path Notch Filter Architecture
The adjusted CLC structure can correct the equivalent situation of the CLC structure, sacrifice low-frequency differential loss, and reduce high-frequency insertion loss substantially by affecting the capacitance on the low-impedance side, making an adjustable balance between low-frequency and high-frequency insertion loss. At the same time, the quality factor of the inductor affects the transmission line loss, which is reflected in the additional insertion loss of the filter, as shown in Figure 8b; as the Q value of the inductor decreases, the overall differential loss of the filter increases, and the characteristic frequency of the transmission line moves to lower frequency, resulting in asymmetry of the insertion loss on both sides of the operated frequency band, which requires a continued increase in \(C_{T1}\) and decrease in \(C_{T2}\). The off-chip inductor with the appropriate quality factor is selected in forming a balance between size and differential loss. In this design, the value of the inductor Q factor is chosen to be 80. In this design, the RF range of 300 to 600 MHz, the transmission line's bandwidth influences the impedance conversion's effectiveness, particularly at the band's edges, where the conversion becomes less efficient. As the bandwidth increases, the insertion loss shortfall also becomes more significant. This impedance mismatch results in an increased insertion loss of the filter. To mitigate this issue, an adjustable transmission  
Figure 8: (**a**) The CLC structure Q factor influences inductors and adjusts the CLC structure. (**b**) Effect of different Q factors of inductor. line structure can be employed to compensate for the resulting shortfall in insertion loss. As shown in Figure 9, without modulating the transmission line structure, for the C\({}_{T1}\) = \(C_{T2}\) curve in the figure, the parasitic capacitance on the Stage II side is larger due to the parasitic factor, resulting in the \(C_{T1}\) \(<\)\(C_{T2}\) curve in the corresponding figure; in this case, the insert shortfall at the edge of the filter band grows greater than 2 dB, and the filter loss increases. By adjusting the equivalent structure of the transmission line, it is possible to achieve the \(C_{T1}\) \(>\)\(C_{T2}\) curve in the figure, where the insert shortfall at the edge of the filter band grows less than 0.3 dB. The proposed filter utilizes a matching impedance of 25 \(\Omega\), which minimizes insertion loss at the lower transmission line impedances and increases insertion loss at the higher impedances. The overall insertion loss is influenced by parasitic capacitances inherent to the chip and the bandwidth of the transmission line. By appropriately adjusting the values of \(C_{T1}\) and \(C_{T2}\), the insertion loss is effectively reduced.
## 5 Simulation Results and Analysis
### System Identification and Tracking under Dynamic Inverse Control  
Secondly, in order to verify the effectiveness of the dynamic inverse control based on the identification model. The simulation results under dynamic inverse control law are given, as shown in Figures 7-10, and the curves of the estimated parameter \(\beta_{11}\) and \(\beta_{33}\) are shown in Figure 11.  
Figure 5: Altitude angles and errors of true system (TS) and identification system (IS) under stochastic constant control law \(u\).  
Figure 6: The flexible states of true system (TS) and identification system (IS) under stochastic constant control law \(u\).  
Figure 8: Output tracking curves and errors between true system (TS) and identification system (IS) under dynamic inverse control.  
Figure 7: Dynamic inverse control law.  
Figure 9: Altitude angles and errors of true system (TS) and identification system (IS) under dynamic inverse control.  
In this case, the vehicle starts at initial trim conditions as listed in Table 2. and tracks the reference trajectories of velocity and altitude generated by the second-order filters with natural frequency \(\omega=0.03\) rad/s and the damping ratio \(\zeta=0.95\).  
\[\begin{array}{l}V_{ref}(s)=\omega^{2}V_{c}(s)/\left(s^{2}+2\zeta\omega s+\omega ^{2}\right)\\ h_{ref}(s)=\omega^{2}h_{c}(s)/\left(s^{2}+2\zeta\omega s+\omega^{2}\right) \end{array}, \tag{25}\]  
where the final reference commands are \(V_{c}=8820\) ft/s and \(h_{c}=86,000\) ft.  
Figure 11: Curves of the estimated parameter \(\beta_{11}\) and \(\beta_{33}\) under dynamic inverse control.  
Figure 10: The flexible states of true system (TS) and identification system (IS) under dynamic inverse control.  
It is seen from Figure 8 that both the output of the actual system and the identification system track the given reference signal, and it is also seen that the identification system state quickly follows the actual system state. In addition, the attitude angle and elastic state of the identification system can also quickly converge to the state of the actual system as shown in Figures 9 and 10. These show that the designed adaptive DNN identification method and dynamic inverse controller are effective. Additionally, from Figure 11, it is seen that the estimated values of the parameter \(\beta_{11}\) and \(\beta_{33}\) are not equal to 0 throughout the flight process which indicates that the selected parameters are valid.  
Further observation of the simulation results under constant control law and dynamic inverse control, and it can be seen that under dynamic inverse control, the states of the identification system approach the states of the real system faster, because the weighting coefficient of the DNN is affected by the control law as shown in Equation (16).  
In summary, although the DNN identification method is independent of the control law design, its performance will be affected by the control law.
# Extending the Quality of Secure Service Model to Multi-Hop Networks
## 3 QoSS Applied to a Multi-Hop Network
### Calculation of Multi-Hop Network QoSSS
#### 3.4.3 Step 3  
Repeat steps 1 and 2 as necessary to further reduce the system in Figure 2 to one, point-to-point connection. By following these steps, the point-to-point equivalent QoSS for a complex network may be derived. For example, once the two links between nodes **X** and **Y** have been collapsed into one equivalent QoSS value by weighted average, then the series of three links of \(\langle\)**A3-X**, **X-Y**, **Y-D3**\(\rangle\) may be multiplied together per (5), at which point the system may be fully reduced to one point-to-point link.  
Figure 2: A Complex, Hybrid Multi-Channel, Multi-hop Network.
## 4 Hardware Implementation
\begin{table}
\begin{tabular}{c c} \hline \hline
**Characteristic** & **Specification** \\ \hline Frequency & 433 MHz \\ Network topology & Point-to-Multipoint, Point-to-Point, Mesh, and Peer-to-Peer \\ Modulation & FSK/GFSK/MSK/LoRa \\ Data rate & \textless{}300 kbps \\ Sensitivity & \(-136\) dBm \\ Output power & \(+20\) dB \\ Operating voltage & 1.8 V to 3.6 V \\ Current & Tx: 120 mA, Rx: 10.8 mA \\ RSSI & 127 dB \\ Link budget & 168 dB \\ \hline \hline \end{tabular}
\end{table} 
Table 1: SX 1278 LoRa specifications [35]. \begin{table}
\begin{tabular}{c c} \hline \hline
**Characteristic** & **Specification** \\ \hline Controller & 8-bit microcontroller \\ Architecture & RISC \\ Programming & In-system programming \\ Serial interface & Master/slave SPI \\ PWM channels & 6 \\ Pin 6 analog pins & 14 digital pins \\ Operating voltage & 2.7 V to 5.5 V \\ Current & Active state: 1.5 mA at 3 V-4 MHz, \\  & Power-down state: 1 \(\upmu\)A at 3 V \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Atmega328P specifications [36]. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Feature** \\ \hline Processor & Tensilica L106 32-bit processor \\ IEEE standard & 802.11 b/g/n \\ Frequency & 2.4 GHz \\ Data rate & 72 Mbps \\ Network Protocols & Ipv4, TCP/UDP, HTTP \\ Tx power & 20 dBm (802.11 b), 17 dBm (802.11 g) \& 14 dBm (802.11 n) \\ Rx sensitivity & \(-91\) dBm (802.11 b), \(-75\) dBm (802.11 g) \& \(-71\) dBm (802.11 n) \\ Operating voltage & 2.5 V to 3.6 V \\ Current & Average: 80 mA \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Technical specifications of ESP 8266 [37].
## 4 Experiments and Results  
In this paper, we present the application of Tromino tiling theory, a class of Polyomino with three cells in the context of a reconfigurable floor cleaning robot, hTromo. The developed robot platform is able to automatically generate a global tiling set required to cover a defined space while leveraging on the Tromino tiling theory. There are five set of theorems that this work sought to validate, as denoted in Section 2. The first set of experiments validated the application of Theorem 1 using a rectangular surface of 140 \(\times\) 126 cm, split into 10 \(\times\) 9 squares. Figure 12(a) presents the universal tiling set that was auto-generated by our path planning algorithm to cover the given area using only L- and T-trominoes. In order to validate the application of Theorem 2, Lemma 1, a rectangular area measuring 126 cm \(\times\) 112 cm was utilized and further sub-divided into 9 \(\times\) 8 square grids. Figure 12(b) illustrates the corresponding universal tiling set auto-generated by our path planning algorithm using which tiles the second test area with only T-set trominoes. The third set of experiments validates Theorem 2, Lemma 2. This was done in a rectangular area of 154 cm \(\times\) 126 cm, split into 11 \(\times\) 9 square grids.  
Figure 12: Tiled Testing Area with I- and L- trominoes.  
Figure 13c, shows the corresponding tiling set generated based on Theorem 2, Lemma 2. In the fourth set of experiments, obstacles were inserted within the test area in order to modify the area based on Theorem 3, Lemma 3. We utilized a square area of 154 cm \(\times\) 154 cm, subdivided into 11 \(\times\) 11 square grids. According to the assertion of Lemma 3, this modified area can be tiled using a \"T\"-set of tetromino pieces. Figure 13d illustrates the universal tiling set that was auto-generated based on Theorem 3, Lemma 3. The fifth set of experiments focused on validation of Theorem 3, Lemma 4. The test was performed within a square area of 140 cm \(\times\) 140 cm, further subdivided into 10 \(\times\) 10 square grids. To meet the requirements of Lemma 4 towards realizing a modified rectangle, obstacles were inserted into the middle of the test area. The global tiling set that was auto-generated based on Lemma 4 can be seen in Figure 13e.  
With Lemma 5 of Theorem 4, we used a square plot as a test arena with a dimension of 168 cm \(\times\) 168 cm and segmented it into 12 \(\times\) 12 square grids. Since the concern theorem deals with Aztec diamond space, we modified the defined area into a 6 th order diamond space by placing obstacles. Figure 13f shows the tiling set generated with 'T' trominoes according to the lemma 5 and the orange shaded areas are filled with obstacles. Similarly, for Lemma 6, we used a 112 cm \(\times\) 112 cm square plot which was segmented to an 8 \(\times\) 8 square grids. The considered area was filled with obstacles in order to convert the square area to a 4 th order Aztec diamond. Also, we placed a separate obstacle in the 4,4 cell to modify area as a deficient diamond. Figure 13g shows the tiling set generated with 'T' trominoes according to the lemma 6.