esa-sceva/satcom-chunk-collection huggingface dataset:
# Toward the Generation of an Isolated TW-Attosecond X-ray Pulse in XFEL
## 2 Methods without Current Modulation
### Good Trajectory Region Method  
Only the electrons that stay in a straight path in an undulator--called the good trajectory region (GTR) [39]--can participate in the FEL radiation process. This also means that the FEL duration can be shorter than the electron pulse itself. Recognizing this fact, a new scheme was proposed for the TW-as  
Figure 1: (**a**) Schematic diagram for the good emittance region (GER) method in [PERSON] et al. [38]. The first blue line on the top: GERs have different spaces with respect to the neighborhood GER. The first red line: the radiation pulses generated from the electron beam in the first undulator. The last red peak in the “tail” part is our target radiation pulse (TRP). The second blue line: the electron beam is delayed to match the second GER to the TRP. The second red line: the TRP is amplified in the second undulator. The third blue line: the electron beam is delayed further to match the third GER to the TRP. The third red line: the TRP is amplified further by the third GER in the third undulator unit. (**b**) Simulation results: “1 A” (“5 A”) means a wavelength of 0.1 nm (0.5 nm). Reproduced with permission from [PERSON] et al. [38]. XFEL: X-ray free electron laser.  
XFEL pulse, in which an electron beam was tilted in an undulator entrance [39]. If we can control a part of the electron beam to stay in a straight path and the other parts not to, we can introduce the delay used in [PERSON] et al. [38], as explained in Figure 2a, and amplify a photon pulse several times by using electron delay units between undulators. In Figure 2a, the green dashed line represents the GTR in the undulator. The blue line shows the electron beam distribution in space. We can see that the longitudinal beam distribution is tilted to GTR. We can control the tail part to stay in the GTR at the first stage of the undulator line. During the first undulator section, the electrons overlapped with the GTR will radiate. This radiation is now the target radiation pulse (TRP) (the red line represents the TRP). In the second undulator unit, the tilted electron beam is delayed to match the TRP to the middle part of the electron beam, as shown in the middle part in Figure 2a. At the third undulator unit, the electron beam is delayed further to match the TRP to the head part of the electron beam for the final amplification. There are several methods to tilt an electron beam, one of which is a transverse deflector structure [39].  
By using this simple idea, it was shown that an attosecond X-ray pulse at a wavelength of 0.1 nm with a power of \(2.38\pm 0.94\) TW and a pulse duration of \(363\pm 6\) as for Swiss-FEL parameters can be obtained, as shown in Figure 2b.
## 2 Data Quality Analysis
### DQA Conclusions  
According to the results of DQA methods, the qualities of BDS ephemeris and almanac data are similar to that of GPS most of the time. However, sometimes BDS has some irregular ephemeris and almanac information. A BDS/GPS receiver is thus suggested to include the above DOA methods to obtain the integrated positioning with sufficient quality.
## 4 Simulation Experiment
### Compare the Conventional Frequency 16-Tupling ROF System with Our Scheme  
When the optical switch and electrical switch in Figure 1 are open, the system is the conventional frequency 16-tupling MMW ROF system. The downlink data are simultaneously modulated onto the \(\pm 8^{\text{th}}\)-order sidebands.  
The conventional ROF system reaches the maximum transmission distance when the eye diagram is fully closed. The maximum transmission distance satisfies the following equation [36]:  
\[z<\frac{\tau c}{16\lambda_{c}^{2}Df_{RF}} \tag{12}\]  
where \(\eta\) is the duty cycle of the code word, \(\tau\) is the code word period, \(D\) is the dispersion constant of fiber, \(f_{RF}\) is the frequency of the RF driving signal, \(\lambda_{c}\) is the center wavelength of the optical carrier, respectively. When \(\eta=1\), \(\tau=0.4\) ns, \(f_{RF}=10\) GHz, \(D=16.75\) ps/nm/km, \(\lambda=1552.52\) nm, according to Equation (12), the maximum transmission distance is 18.6 km.  
The optical spectrum at point A is shown in Figure 11, from which we can see that the data are modulated on the \(\pm 8^{\text{th}}\)-order sidebands.  
Figure 10: The relationship between the BER and the received power of PD in the downlink and uplink: (a) downlink; (b) uplink.  
Figure 9: The relationship of the Q factor with the transmission distance of the fiber.  
Figure 12 shows the spectrum of the generated frequency 16-tupling MMW. From Figure 12, we can see that the main component in the output signal from the PD is the frequency 16-tupling MMW, along with the data.  
Figure 13 shows the eye diagram obtained at different transmission distances in the conventional frequency 16-tupling MMW ROF system. From Figure 13, we can see that the width of the code word becomes narrower and narrower as the transmission distance increases. The eye diagram is almost closed when the distance is 18.6 km; these results are in agreement with Equation (12).  
Figure 14 shows the eye diagrams obtained at different fiber transmission distances for our designed system. From Figure 14, we can see that the eye diagram is still open even though the transmission distance is 60 km.  
Figure 11: Optical spectrum at the output of the \(\$^{\text{th}}\)-order sideband generator (Point A) in Figure 1.  
Figure 12: Spectrum at the output of the PD1 (Point E) in Figure 1.  
Figure 13: Eye diagram at difference transmission distances: (**a**) BTB; (**b**) 6 km; (**c**) 12 km; (**d**) 18.6 km.
# Navigation Control and Signal Processing Methods for Multiple Autonomous Unmanned Systems
## 2 An Overview of the Published Articles
The simulation results indicate that the PSO-FSM achieves a better result than SM and FSM, where the PSO-FSM changes the control gain adaptively under different velocities and actuator saturation conditions, and the maximum lateral errors of the SM and PSO-FSM for mobile vehicle autonomous turning are 0.32 m and 0.03 m, respectively. When the location of the mobile vehicle deviates from the expected path at 4 m in a lateral direction, the distance of the guided trajectory for the mobile vehicle to reach the expected path is no more than 5 m [67]. In another study, to solve the problems of measurement information abnormal error and nonlinear filtering in UWB navigation and positioning, an ultra-wideband position algorithm based on a maximum cross-correlation entropy unscented Kalman filter is proposed. The algorithm first obtains the predictive state estimate and the covariance matrix through traceless transformation. Then, it reconstructs observation information using the nonlinear regression method based on the maximum cross-correlation entropy criterion, which enhances the robustness of the unscented Kalman filter algorithm for heavy-tailed noise. The simulation and actual test results show that this algorithm has better positioning accuracy and stability than the traditional filter algorithm in a non-Gaussian noise environment. This algorithm effectively solves the problem that the UWB indoor location is easily affected by indoor environments, resulting in fixed deviation for that location [68]. These studies collectively highlight the future of navigation control and signal processing methods for autonomous unmanned systems. Given the breadth of this topic, this Special Issue includes papers from focused research to broad overviews. It is clear that many factors, from cost-effectiveness and the role of people to the importance of different models, will influence the future of this field.
## 4 Challenges and Future Research Directions
### Energy-Efficiency and Reliability  
Both UAVs and smart electric vehicles are battery-operated. Many conventional UAVs typically have a battery life of less than half an hour, which limits their operational time. Since ITS operations can be demanding, requiring longer flight times, multiple sensors, and continuous network connectivity, conventional UAVs may struggle with insufficient battery life [20]. Advancements in battery materials and design are crucial to address these limitations, and exploring the integration of solar and other renewable energy sources could be beneficial. Additionally, optimizing energy consumption through enhanced flight time, operability, and coverage should be investigated to improve the performance of UAVs in ITS applications [31].
# Robots in Partially Known and Unknown Environments:
## 5 Experiments and Results
### OnlineRMP Analysis
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**OfflineRMP**} & \multicolumn{4}{c}{**OnlineRMP**} \\ ID & **Av. Length** & **Best Length** & **Smoothness** & **Time [s]** & **Av. Length** & **Best Length** & **Smoothness** & **Time [s]** \\ \hline (a) & 4806.4 & 4806.4 & **108.2** & 1.0 & 8658.1 & 5543.7 & 89.4 & **0.3** \\ (b) & 2502.9 & 2488.2 & **105.6** & 3.0 & 6899.2 & 2780.0 & 94.5 & **0.2** \\ (c) & 749.8 & **748.4** & **132.4** & 0.5 & 1493.7 & 864.3 & 88.3 & **0.1** \\ (d) & 3600.8 & 3596.1 & **119.0** & 1.4 & 7259.4 & 5304.8 & 87.9 & **0.3** \\ (e) & 2786.6 & 2781.0 & **134.5** & 2.6 & 9215.6 & 3846.9 & 90.0 & **0.3** \\ (f) & 2032.4 & 2021.8 & 71.0 & 0.5 & 2991.8 & 2127.3 & **103.0** & **0.2** \\ (g) & 2137.6 & 2045.7 & 88.2 & 1.1 & 3117.0 & 2414.8 & **102.1** & **0.2** \\ (h) & 1597.3 & 1586.5 & **116.6** & 0.7 & 2132.2 & 1714.5 & 97.0 & **0.2** \\ (i) & 4119.6 & 4118.4 & **112.4** & 12.1 & 15,850.7 & 6071.9 & 93.7 & **0.2** \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison between OfflineRMP and OnlineRMP. Figure 15: Test instance solution found by OnlineRMP, complete information of environment in (**a**), and gathered information by the robot in (**b**). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**ID** & **Name** & **Length** & **Smoothness** & **Time [s]** & \% **Discovered** \\ \hline (a) & back\_and\_forth & 8658.1 \(\pm\) 1740.7 & 89.4 \(\pm\) 8.6 & 0.3 & 1.6 \\ (b) & corridor\_wavy & 6899.2 \(\pm\) 3299.0 & 94.5 \(\pm\) 3.5 & 0.2 & 0.3 \\ (c) & bugtrap1 & 1493.7 \(\pm\) 640.2 & 88.3 \(\pm\) 7.4 & 0.1 & 2.2 \\ (d) & clasps & 7259.4 \(\pm\) 1216.2 & 87.9 \(\pm\) 0.2 & 0.3 & 4.0 \\ (e) & complex & 9215.6 \(\pm\) 2877.7 & 90.0 \(\pm\) 0.2 & 0.3 & 1.2 \\ (f) & potholes & 2591.8 \(\pm\) 272.7 & 103.0 \(\pm\) 4.3 & 0.2 & 0.5 \\ (g) & rockpile & 3117.0 \(\pm\) 629.4 & 102.1 \(\pm\) 3.9 & 0.2 & 0.2 \\ (h) & var\_density & 2132.2 \(\pm\) 407.2 & 97.0 \(\pm\) 5.4 & 0.2 & 0.1 \\ (i) & square\_spiral & 15,850.7 \(\pm\) 6285.4 & 93.7 \(\pm\) 4.7 & 0.4 & 4.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Average solutions found by OnlineRMP. Figure 16: Best paths found by OfflineRMP. Figure 17: Cont.
# Multiband and Lossless Compression of Hyperspectral Images
## 2 Related Works  
Lossless compression of hyperspectral images is generally based on the predictive coding model. The predictive-based approaches have different advantages: they use limited resources in terms of computational power and memory usage and achieve good compression performances. Often, these models are suitable for on board implementations.  
Spectral-oriented Least SQuares (SLSQ) [3], Linear Predictor (LP) [3], Fast Lossless (FL) [4], CALIC-3D [5], M-CALIC [5], and RLS [6] are among the state-of-art predictive-based techniques.  
The _Consulative Committee for Space Data Systems_ (_CCSDS_) has specified the _CCSDS 123 standard_, which outlines a method for lossless compression of multispectral and hyperspectral image data and a format for storing the compressed data [7; 8]. The main objective is to establish a Recommended Standard for a multispectral and hyperspectral images, and to specify the compressed data format. In literature, many proposed approaches implement the recommendations of the CCSDS 123 standard for the lossless compression of hyperspectral images, as for instance, the ones described in [9; 10; 11].  
Other approaches are designed for offline compression, since they use more sophisticated techniques and/or they require the complete availability of the hyperspectral image. These approaches are not suitable for an on board implementation but can achieve better compression performances. [PERSON], in [12], proposed an approach for the compression of hyperspectral image through Look-Up Table (LUT). LUT predicts each pixel by using all the pixels in the current and in the previous band, by searching the nearest neighbor, in the previous band, which has the same pixel value as the pixel located in the same spatial coordinates as the current pixel. LUT has high compression performances, but it uses more resources in terms of memory and CPU usage.  
Other lossless techniques are based on dimensionality reduction through principal component transform [13] or they are based on the clustered differential pulse code modulation [14]. An error-resilient lossless compression technique is proposed in [15].  
For the lossy compression of hyperspectral images, the compression algorithms are generally based on 3D frequency transforms: as for examples 3-D Discrete Wavelet Transform (3D-DWT) [16], 3-D Discrete Cosine Transform (3D-DCT) [17], Karhunen-Loeve transform (KLT) [18], _etc_. These approaches are easily scalable. On the other hand, they must maintain the entire hyperspectral image at the same time in memory. Locally optimal Partitioned Vector Quantization (LPVQ) [19; 20] applies a Partitioned Vector Quantization (PVQ) scheme independently to each pixel of the hyperspectral image.  
The variable sizes of the partitions are chosen adaptively and the indices are entropy coded. The codebook is included as part of the coded output. This technique can be used also in lossless mode, but the high costs required in terms of CPU and memory do not allow an on board implementation.
# Simultaneous Indoor Tracking and Activity Recognition Using Pyroelectric Infrared Sensors
###### Abstract  
Indoor human tracking and activity recognition are fundamental yet coherent problems for ambient assistive living. In this paper, we propose a method to address these two critical issues simultaneously. We construct a wireless sensor network (WSN), and the sensor nodes within WSN consist of pyroelectric infrared (PIR) sensor arrays. To capture the tempo-spatial information of the human target, the field of view (FOV) of each PIR sensor is modulated by masks. A modified partial filter algorithm is utilized to decode the location of the human target. To exploit the synergy between the location and activity, we design a two-layer random forest (RF) classifier. The initial activity recognition result of the first layer is refined by the second layer RF by incorporating various effective features. We conducted experiments in a mock apartment. The mean localization error of our system is about 0.85 m. For five kinds of daily activities, the mean accuracy for 10-fold cross-validation is above 92%. The encouraging results indicate the effectiveness of our system.  
Keywords:Wireless Sensor Network (WSN); Pyroelectric Infrared (PIR) sensor; random forest; simultaneously tracking and recognition +
Footnote †: journal: Sensors
## 1 Introduction
In many production and non-production organizations, the response time to the customer demand is very important and is considered as a competitive advantage. One of the very important factors affecting the response time to the customer is the transportation time. Consequently, many researchers focus on the integrated transportation and production planning/scheduling problems [29; 30; 31]. A survey of integrated production scheduling and outbound distribution models is presented in [32], and a review of literature on the production routing problem (PRP) can be found in [33]. To the best of our knowledge, the scheduling problem with all four mentioned features, i.e., job and machine acceptance, and renewable resource and transportation constraints, has never been studied in the past. Only the contributions in [9; 13], which consider both the job and machine selection in a parallel machine environment, are very similar to our research; however, the renewable resource and job and resource transportation constraints are additionally taken into account in this paper. This problem is strongly NP-hard, since the parallel machine scheduling problem with a fixed number of machines and jobs and without resource constraints is already strongly \(\mathcal{NP}\)-hard [34]. The remainder of the paper is organized as follows. We begin by presenting the problem in Section 2 through the aid of an illustrative example. We then continue with an exact resolution approach based on mixed-integer linear programming and with several heuristic approaches in Section 3. In Section 4, we evaluate these approaches using artificially generated datasets (available on request), before finishing with several concluding remarks in Section 5.
## 4 Results and Discussion
### Effect of CAWA on the Geotechnical Properties
#### 4.2.4 Results of Unconfined Compression Strength Tests  
The specimens underwent an unconfined compression test to ascertain their compressive strength after 7 days of curing. As demonstrated in Figure 8, the addition of CACW to all mixes produced results that were better than the UCS value of the control specimen (soil only). With 10% CACW substitution, the greatest UCS value was attained. Figure 8 demonstrates that, in comparison to UCS values achieved with 10% CACW replacement, CACW replacement at a higher percentage than 10% led to lower UCS values. After a 7-day curing period, the UCS values of the specimens treated with 0, 5, 10, 15, and 20% CACW were 430, 735, 897, 827, and 795 KPa, respectively. The optimum concentration of CACW required to improve the compressive strength of the combinations was 10%. This suggested that for completely filling the pore space and producing efficient soil-binder reactions, 10% CACW with 2% cement is the optimal dosage. At a proportion above this cutoff, CACW completely fills all pore spaces, and extra CACW congregates on the grains in soil-binder mixtures. As a result, the amount of contact surface area required for chemical reactions was reduced. The same trend is observed when 2% cement is added to the soil-CACW mixes with higher values of UCS. The UCS values of the specimens treated with 0, 5, 10, 15, and 20% of CACW in addition to 2% of cement were 680, 1035, 1390, 1120, and 1090 KPa, respectively, after a 7-day curing period. These results are consistent with those obtained earlier from UPV and CBR tests. Figure 8 depicts the impact of various CACW percentages on UCS, and as can be observed from the graph, 10% CACW resulted in the greatest UPV and UCS values, as previously reported. When the CACW percentage was 10% higher than expected, both the UPV and the UCS values decreased.  
Figure 7: Effect of addition CACW on the soaked CBR value.  
In the construction of rigid pavement, the USA Army Corps of Engineers (USACE) has recommended a minimum 7-day qu of 1380 kPa (200 psi) for subbase and subgrade and 3450 kPa (500 psi) for base course layers, according to the ACI Committee 230 Report [33]. However, these values are 1725 kPa (250 psi) and 5175 kPa (750 psi), respectively, for the construction of flexible pavement. Based on this criterion, only soil mixes with 10% CACW and 2% Portland cement pass the ACI requirements for use in subgrade and subbase layers in rigid pavement construction. Thereafter, this mix (soil-10% CACW-2% cement) will be further evaluated for durability criteria.  
The _p_-values and statistical results that support the trends described in Figure 8 are shown in Table 2. Based on the statistical results presented, it is seen that the differences are statistically significant since the _p_-values are very small compared to the reference _p_-value (i.e., 0.05). This indicates there is significant evidence against the null hypothesis.
## 3 Results, Analysis, and Discussion  
### Results  
The details of the experiment and the measurements obtained are summarized in Table 4. The usual range of roughness tolerances required in the aeronautical and aerospace sector in terms of \(Ra\) (\(\upmu\)m) is 0.8 \(\upmu\)m \(<Ra<\) 1.6 \(\upmu\)m [41]. Generally, each range of surface roughness is associated with a dimensional tolerance and a production cost. In other words, to achieve an optimum surface tolerance, a very accurate finishing is required and, therefore, more time will be required in the finishing operations and a better-quality tool and equipment will be needed. In short, the best finishing is associated with a higher cost of the manufacturing process.  
Generally, a low surface roughness does not create problems in the case of repair and maintenance operations since the aim of a surface roughness tolerance is the limitation of corrosion and crack propagation. In operations where higher surface roughness is required, such as painting or areas where the plasma will be projected, a preblasting is carried out to increase the roughness of the surface [62; 63; 64; 65].  
Figure 3: Schematic diagram of the experimental set-up for the re-drilling trials on Mg-Al-Mg multimaterial specimens.  
In Table 4, the values obtained within the required tolerance range are represented in green text, and the values below the minimum required tolerance are represented in blue text. When blue values appear, the process could be better adjusted so that all measurements remain below the maximum tolerance but optimizing the cost and time of the machining operation. Roughness measurements above the maximum tolerance are represented in red text and are not acceptable.