esa-sceva/satcom-chunk-collection huggingface dataset:
# Internet of Drones Intrusion Detection Using Deep Learning
## 5 Simulation Results
### Performance Analysis
#### 5.2.7 Signature-Based Approach vs. LSTM_RNN  
Although the paper's main concern is machine learning approaches, this subsection examines one of the nonmachine learning approaches, specifically signature-based intrusion detection. We based our results on Snort [49] version 2.9.18 with the latest rules 23 September 2021. Snort is an open-source network intrusion prevention system that can analyze and record IP traffic in real-time. It can detect buffer overflows, stealth port scans, CGI attacks, SMB probes, OS fingerprinting attempts, and many more attacks and probes. With the help of TcpReplay, we fed Snort with the dataset records. We selected UNSW-NB15 for this set of experiments because it is more recent than DDCup99 and NSL-KDD, and it handled their problems as well. A preprocessing step was conducted to remove all of the unnecessary information from the dataset. We also based our results in this section on binary classifications of the attacks.  
Figure 28 shows the performance of both Snort and LSTM_RNN in terms of accuracy, precision, recall, and F-score. As expected, the LSTM_RNN performed much better than the Snort in every aspect. LSTM_RNN accurately detected the intrusion with 36% better than the Snort. In addition, it was clearly noticed that Snort had a low precision value of almost 1%. Therefore, we recommend that although machine learning algorithms require more time for training, they give more accurate results than nonmachine learning algorithms.  
Figure 27: LSTM_RNN Performance Over ToN_IoT Dataset.
## 5 Empirical Case Study: Courier Company in Bangkok
### Towards Balancing Vehicle's Total Working Time
#### 5.4.2 Finding Optimal Working Time Limit  
Define the _optimal working time_ to be the smallest working time limit leading to no vehicle's working time violation. In order to obtain the optimal working time, we search for such smallest working time \(\tau^{*}\in\{1,2,3,\dots\}\) for which at the end of Phase II, no vehicle violates the working time \(\tau^{*}\) (i.e., a successful termination status is returned). In particular, for each fixed time instance between 9 a.m. to 7 p.m., we solve an optimization problem for optimal \(\tau^{*}\). Referring back to our motivating example, Figure 7(a) suggests that the optimal \(\tau^{*}\) for Problem B--12 p.m. will lie somewhere between 490 and 500 min (transitioning from violating to non-violating situation).  
Table 2 reports the optimal working time of problems in Scenarios A and B for all time instances. From the table, we see that the optimal working time for Problem B--12 p.m. is 494 min. The table gives,  
Figure 8: Analysis results of Problem B—12 p.m.  
in addition, the percentage change in the number of resulting customer assignment from the original coverage area (vehicle-zone map). For this particular problem, the percentage change is found to be 18%. Overall the change is less than 28% and 18% for problems in Scenarios A and B, respectively. Moreover, we provide the average travel speed (in line with the plot given earlier in Figure 4) for a side-by-side comparison in the table. Figure 9 presents the relationship of the optimal working time \(\tau^{*}\) in terms of the average travel speed. We can see that the optimal working time is inversely proportional to the average speed. Therefore, the proposed Phase II has proven capable of finding a feasible solution from a solution with time violation very quickly, while at the same time preserving a certain level of customer assignment from the original coverage area.
# Amharic Speech Search Using Text Word Query Based on Automatic Sentence-like Segmentation
## 3 Related Work
### Searching on Speech  
[PERSON] et al. [21] presented an interactive media player that enables the user to perform offline audio content-based searching capabilities with a given multimedia file. The research objective was to alleviate the problem of massive online courses (MOOCs), a free online course available for anyone to enroll, but in which only 15% of users complete the course.  
[PERSON] and [PERSON] [29] proposed an Amharic speech search engine by designing an Amharic speech document. Since most search engines are designed for English, they struggle to find documents written in Amharic. The prototype developed by the author has four essential components: crawler, audio processing, indexer, and query engine. The experimental results revealed that the Amharic speech retrieval engine had an accuracy of 80% on the top 10 results and a recall of 92% as compared to its corresponding retrieval engine.  
[PERSON] et al. [30] proposed a system that can automatically process a YouTube video file. It allows users to identify the discussed topics in a significantly shorter time through a search engine. The topic of interest is described by keywords used for searching the video or audio material. The proposed system works in a way that first checks the coming multimedia data types and their file extension. If it is a video file in *.wav format and it is automatically transcribed to its respective English text. From the transcribed text, the respective topic can be deduced. After transcription, the indexed topic with the predefined list of topics (keyword spotting) is matched. Then, the matching results from the previously mentioned process and the respective video file are stored in the database, which can be used by the users while requesting the system by entering text query word. The transcription accuracy of the proposed system was 50-80%.  
The other research made by [PERSON] [31] was the Turkish Broadcast News transcription and retrieval system. Developing the OOV was a challenge where even sub-word-based recognition units were utilized. To alleviate this problem and increase the accuracy, the researchers used moderately sized vocabularies, which performed better for a vocabulary size of 500 k. The researchers developed a Spoken Term Detection system and a Spoken Document Retrieval system. To retrieve the spoken data, ASR was used.  
In the present study, we aim to achieve two purposes: First, to check the performance of an ASR system that is developed using automatically segmented audio files on automatically segmented test speeches. Second, to investigate the performance of searching a text word and displaying the time interval in which the word is spoken in a speech file.
## 3 Results
### Visualization of Landslide Detection  
To visualize the separated results of the case in Section 3.3.1, the predicted patterns of the No. 3 models for distinguishing landslide source and runout targets were generated, as illustrated in Figure 14. The black and red and blue colors in this figure represent the ground truth and prediction results, respectively. As illustrated in Figure 14a, the developed models displayed suitable detection of the landslide sources (high PA(S)) with some false alarm errors (low UA(S)). Figure 14b also illustrated that the model provided reliable results for detecting runout areas with higher UA(R) and PA(R). These patterns accorded with the results of the No. 3 model in Figure 10b.  
The detected distributions with the topographic situation in a three-dimensional (3D) space were further examined. Figure 15 displays the patterns of the No. 3 model for predicting the No. 1 and No. 2 landslide source and runout inventory polygons. In this figure, red and yellow represent the correct and incorrect predictions, respectively. Figure 15a,b displays accurate detections for the landslide sources. The results presented in Figure 15c indicate that the lower part of a runout area can be detected, whereas the upper region may be miscategorized as a landslide source. This finding reveals that elevation is a critical factor for the separation of landslide source and runout areas. This finding also accords with the result of the factor analysis presented in Section 3.1. Future studies can determine the elevation-based threshold to improve the capability of the developed models.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Method** & **UA(R)** & **PA(R)** & **UA(S)** & **PA(S)** & **AUC** & **OA** \\ \hline No. 1 & RF\_5000 & 0 & 0 & 0.64 & 1 & 0.67 & 63.87\% \\ No. 3 & RF & 0.59 & 0.93 & 0.94 & 0.63 & 0.85 & 73.94\% \\ No. 3* & RF & 0.63 & 0.91 & 0.93 & 0.70 & 0.85 & 77.54\% \\ \hline \hline \end{tabular}
* With the topographic roughness index.  
\end{table}
Table 6: Performances of the No. 1 model using the RF method with a cost of 5000 and No. 3 model with the RF algorithm, where OA, AUC, PA, UA, S, and R represent the overall accuracy, area under the ROC curve, producer’s accuracy, user’s accuracy, landslide source, and runout, respectively.  
Figure 14: Prediction results of the No. 3 model using the RF algorithm to separate landslide source and runout signatures with the areas of inventory polygons of \(\geq\)100,000 m\({}^{2}\). Cases of (**a**) landslide source and (**b**) runout polygons.  
Figure 15: Three-dimensional (3D) view of the No. 3 model for predictions of separating landslide source and runout areas. The landslide source areas of the (**a**) No. 1 and (**b**) No. 2 polygons in the landslide inventory (located region A and B in Figure 14), and (**c**) the runout regions of the No. 1 and No. 2 inventory polygons (located region C in Figure 14). Red and yellow represent correct and incorrect results, respectively.
## 3 Results  
### Shear Bond Strength Outcomes  
The mean SBS values and standard deviations in MPa for each experimental group are presented in Table 3. All specimens of the untreated specimens of lithium dis silicate restorative failed before shear bond strength test, so they were not included in the statistical analysis. The highest SBS values were observed for Group F (positive control group), followed by Group B, while the lowest values were observed for Group D. However, one-way ANOVA indicated no statistically significant differences among groups B, C, D and E (\(p\) > 0.05), while Group F was significantly different only from Group D (\(p\) = 0.026), as can be seen in Table 4.  
Figure 1: The workflow of the experimental part of the study.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Experimental** & **N** & **Abbreviations** & **Shear Bond Strength** \\
**Groups** & **N** & **(MPa)** & **(MPa)** \\ \hline A & 10 & Negative control & N/A * \\ \hline B & 10 & SB + MEP + AU & 10.70 \(\pm\)2.40 a,b \\ \hline C & 10 & SB + IC + MEP + AU & 10.04 \(\pm\)3.18 a,b \\ \hline D & 10 & IC + MEP + AU & 9.34 \(\pm\)3.46 a \\ \hline E & 10 & MEP + AU & 10.42 \(\pm\)2.17 a,b \\ \hline F & 10 & SB + MEP + IC + MP + AU (Positive control) & 12.94 \(\pm\)3.17 b \\ \hline \hline \end{tabular}
\end{table}
Table 3: Means and standard deviations of shear bond strength (MPa) of the experimental groups in the study. AU: AdheSE Universal; IC: Ivoclean; MEP: Monobond Etch & Prime; MP: Monobond Plus; SB: sandblasting with 29 μm Al2O3 particles.
# A Service-Constrained Positioning Strategy for an Autonomous Fleet of Airborne Base Stations
## 5 Energy Consumption
### Vertical Flight  
A strictly vertical flight (without horizontal displacements) is divided into three different cases depending on the vertical velocity \(V_{V_{i}}\). If \(V_{V_{i}}\) is positive, the helicopter is climbing; if \(V_{V_{i}}\) is negative, the helicopter is descending; and, if \(V_{V_{i}}\) is zero, the helicopter is hovering.  
Regarding the climbing and hovering states, the same expression can be used for the supplied power \(P_{V}\), which is detailed in Equation (40a), where \(V_{Hov}\) is the induced velocity at the rotor in the hovering state defined as  
\[V_{Hov}= \sqrt{\frac{W^{\prime}}{2\cdot\rho\cdot\pi\cdot r^{2}}}, \tag{39}\]  
where \(r\) is the propeller blade radius and \(\rho\) is the density of air, which depends on the altitude and is detailed later at the end of this section.  
In the descending state, the modeling of the power is more difficult as there are different behaviors depending on \(V_{V_{i}}\). If \(V_{V_{i}}\) is lower than \(-2\cdot V_{Hov}\), we are in the windmill brake state. In this state, the energy is extracted from the air to maintain the rotation, like in a wind turbine, and its consumption is negative which means that the batteries are charged, as detailed in Equation (40c). However, as it is clearly explained in [33], between \(-2\cdot V_{Hov}\) and 0 there are two states that have a more complex behavior. Thus, consequently, they can only be modeled empirically. These states are the vortex ring state (from \(-V_{Hov}\) to 0) and the turbulent wake state (from \(-2\cdot V_{Hov}\) to \(-V_{Hov}\)). They have been studied in different works and some models, which we borrow, have been extracted empirically as in [34]. Specifically, in the vortex ring state, the author of [34] used the same expression as in the climbing state (note that Equation (40a) starts at \(-V_{Hov}\) instead of 0) and for the turbulent wake state the expression detailed in Equation (40b) is adopted. Accordingly, Equation (40) summarizes all the possible consumption regimes in vertical maneuvers:\[P_{V}=\left\{\begin{aligned} \frac{1}{2}\cdot V_{V_{i}}\cdot W^{ \prime}+\frac{1}{2}\cdot W^{\prime}\sqrt{V_{V_{i}}^{2}+4\cdot V_{Hov^{\prime }}^{2}}& V_{V_{i}}\geq-V_{Hov},\\ V_{V_{i}}\cdot W^{\prime}\cdot(-0.991+0.373\cdot\bigg{(}\frac{V_{V _{i}}}{V_{Hov}}\bigg{)}^{2}),&-2\cdot V_{Hov}\leq V_{V_{i}}\leq- V_{Hov},\\ \frac{1}{2}\cdot V_{V_{i}}\cdot W^{\prime}-\frac{1}{2}\cdot W^{ \prime}\sqrt{V_{V_{i}}^{2}-4\cdot V_{Hov^{\prime}}^{2}}& V_{V_{i}} \leq-2\cdot V_{Hov}.\end{aligned}\right. \tag{40a}\] \[P_{V}=\left\{\begin{aligned} &\frac{1}{2}\cdot V_{V_{i}}\cdot W^{ \prime}+\frac{1}{2}\cdot W^{\prime}\sqrt{V_{V_{i}}^{2}+4\cdot V_{Hov^{\prime }}^{2}}& V_{V_{i}}\geq-V_{Hov},\\ \end{aligned}\right.\] (40b) \[P_{V}=\left\{\begin{aligned} &\frac{1}{2}\cdot V_{V_{i}}\cdot W^{ \prime}-\frac{1}{2}\cdot W^{\prime}\sqrt{V_{V_{i}}^{2}-4\cdot V_{Hov^{\prime }}^{2}}& V_{V_{i}}\leq-2\cdot V_{Hov}.\end{aligned}\right. \tag{40c}\]
# Research on Kalman Filter Fusion Navigation Algorithm Assisted by CNN-LSTM Neural Network
## 4 Experimental Comparison
### Disturbed Scenario
#### 4.2.2 Neural Network Architecture Combining CNN and LSTM  
The navigation dataset serves as input information, and the training process is depicted in Figure 12.  
The measurement error obtained through training is shown in Figure 13.  
The final training outcome indicates a loss error of RMSE_error = 0.7395.
## 1 Introduction
In summary, current research on the spatial patterns of archaeological sites primarily focuses on using spatial analysis tools in GIS to reveal the spatial and temporal distribution characteristics of sites. The research scope is mostly concentrated in traditional archaeological hotspots, such as the Yellow River and Yangtze River basins. In contrast, studies on archaeological sites within Sichuan Province are relatively limited, with most research focusing on areas related to the ancient Shu civilization, lacking a systematic investigation of sites across the entire province. Additionally, in terms of research perspectives, there is a lack of comprehensive analysis of the distribution characteristics of sites and their associations with natural geographical factors from a geographical spatial viewpoint. In terms of research methods, existing studies often rely on single methods or data from local regions, lacking a comprehensive revelation of the multi-scale and multi-dimensional spatial distribution patterns. Including the neglected sites in the spatial and temporal distribution research of remains is of great significance for interpreting the more authentic human-environment relationship during the historical periods of the region. Therefore, this study focuses on Sichuan Province as the research area, systematically compiling comprehensive site data within the region. By employing GIS methods, it analyzes the spatial and temporal distribution characteristics of these sites, exploring their relationships with factors such as elevation, slope, and proximity to water from a geographical spatial perspective. The findings provide new insights into the evolution of human-environment interactions in the region during historical periods, offering valuable references for related research.
## 1 Introduction  
Efficient light management techniques are necessary for increasing the short circuit current density and quantum efficiency of silicon thin-film solar cells [1]. Experimentally, high conversion efficiencies have been achieved by texturing the contact layers of silicon solar cells [2; 3; 4; 5; 6; 7; 8; 9]. The nanotextured contact layers reduce reflection losses and enhance the scattering and diffraction of light within the solar cells. Moreover, the optical path length is increased leading to an enhanced quantum efficiency and short circuit current density in the red and infrared range of the optical spectrum (wavelengths between 600 and 1100 nm) [6; 7; 8; 9]. Furthermore, alternative approaches, such as 3D solar cells [10; 11; 12; 13; 14; 15] or nanowire solar cells, have gained considerable attention in recent years [16; 17; 18; 19; 20]. Axial nanowire solar cells are based on the texturing of the active layer of the solar cell instead of texturing the contact layers. Several studies suggest that such nanowire solar cells exhibit very good light trapping properties [10; 16; 17; 18; 19; 20]. In this study, solar cells with textured contact layers and solar cells with textured absorber are compared taking realistic device structures into consideration. Solar cells on flat substrates are used as reference solar cells.  
The optical simulations of the microcrystalline silicon thin-film solar cells on flat substrates are described in Section 2.1. Results for solar cells with textured absorber are presented in Section 2.2. The optical simulations for solar cells with textured contact layers are presented in Section 2.3. A comparison of the different structures is given in Section 3, before providing an outlook and a summary of the results in Sections 4 and 5.
## 7 Experimental Investigation  
### Hardware Setup  
An experimental hardware setup has been built to validate the effectiveness of the voltage balancing circuit based on the buck-boost converter with the proposed controller. It basically consists of an inverter prototype, a buck-boost converter, load of RL type and a  
Figure 10: Simulation results of power loss and efficiency with respect to modulation index. (**a**) Total power loss (**b**) Efficiency (**c**) Power distribution among the power switches at 0.9 modulation index.  
single DC source as presented in Figures 11 and 12. Here, the PV source is represented by a single DC supply that provides \(\text{V}_{\text{in}}\). Additionally, four DC-link capacitors are connected between the inverter and the converter, and each capacitor reference voltage is equal to \(\text{V}_{\text{in}}/4\). The power circuit and the control unit are the two main parts in the hardware construction of the buck-boost converter. The main power circuit comprises the semiconductor power devices, dead band generators and gate drives. Four power switches (IGBT model IRG4 PC40 UD) are used. In order to prevent voltage surges during turning OFF, each IGBT is connected to an RC (resistive-capacitive) snubber. The value of the RC snubber used in this experiment is 10 \(\Omega\) and 1 nF. To complete the power circuit, dead band generators and gate drives were added. The dead band generators produce a blanking time of 1 \(\upmu\)s to ensure that the power switches are not turned ON at the same time, thus avoiding a short circuit across the DC source. The gate drives provide isolation between the power circuit and the control unit for protection purposes, and also increase the voltage level of the switching signals to +15 V for the IGBTs to operate. To measure the voltages and currents for the control unit to use as inputs, voltage and current sensors of models LV25P and LA25-NP from LEM were used.  
Figure 11: Block diagram of experimental setup for capacitor voltage balancing.  
Figure 12: Photo of experimental setup to investigate capacitor voltage balancing performance.  
The control unit is represented by the controller board eZdsp TMS320F2812. The control algorithm for the capacitor voltage balancing was implemented on a 32-bit fixed-point digital signal processor (DSP) for real-time application. The analogue-to-digital (ADC) module, event manager timers and general input/output ports are special features in eZdsp board, which are used for DSP programming.  
To capture the results of the inverter output and capacitor voltages, oscilloscope is used. Table 1 shows the circuit components' values. The inverter load consisted of a three-phase load of 120 \(\Omega\) and 69 mH per phase. The experiment was set at a modulation index of 0.9 so that nine voltage levels could be obtained in the line-to-line output voltage waveforms. A switching frequency of 5 kHz was applied.
## 3 Measurement Setup
### Time Frame  
In order to gain representative data from the tests, a time span of 28 days was chosen, starting on the 20 th of December 2010. For the purpose of a direct comparison we had to leave out days when not all of the nodes were running. Observing such a long period, however, will prevent a biasing of the results caused by random load effects of PlanetLab nodes and their network connections. Initially, nodes started the HTTP script twice and the throughput scripts four times within an hour. Because some of the nodes provided only a very slow connection and the scripts would not finish before the next was started, this frequency was reduced. The HTTP script was then started once and the throughput scripts twice during an hour. But even with this change in the setup, scripts were sometimes started a second time before the last script was finished, but this did not impair the quality of the measurements.