esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Description  
Figure 2 shows a two-robot cooperative platform for implementing HWMP, developed at Beijing University of Technology (BJUT). The welding torch and the milling tool are mounted on two independent robots. The first robot is RTI2000 (igm Robotersysteme AG, Wiener Neudorf, Austria), equipped with two Fronius Synergic 5000 power sources to implement WAAM based on Tandem GMAW (gas metal arc welding). Tandem GMAW differs from conventional GMAW as two welding wires are passed through the same welding torch and, therefore, provides much higher productivity [24]. Based on preliminary experiments, it is known that tandem GMAW is capable to produce wall structures of widths ranging from about 4 mm to 17 mm, which is especially beneficial for fabricating stiffened panels of different specifications. The wire material used in this study is Al2325 alloy with the chemical composition of Cu 3.9-4.8%, Mn 0.1-1.0%, Ti 0.15%, Mg 0.4-0.8%, Zn 0.3%, etc., in addition to Al, and the substrate material is Al2219 alloy. The wire diameter is 1.2 mm, and the shielding gas is Ar at a rate of 22 L/min. Aluminum alloys are one of the most widely used materials in aircraft components because of their reasonable cost, high stiffness-to-weight and strength-to-weight ratios, and excellent machinability. The second robot is KR500 (KUKA AG, Augsburg, Bavaria, Germany), which is a heavy-duty robot that is suitable for milling applications. It is equipped with a high-speed electric spindle ES779 with a maximum spindle speed of 22,000 rpm. The uncoated carbide alloy milling tool is adopted, which has a diameter of 14 mm and a helix angle of 55\({}^{\circ}\). The working mode is down milling. No cooling and lubricating agent are used.  
The work principle of HWMP for fabricating stiffened panels is displayed in Figure 3. Step 1: the welding torch moves along the length of the stiffeners and deposits \(N\) layers onto an existing thin plate or the previous layers; Step 2: the top surface of the deposited layers is milled to a prescribed thickness \(H\) in order to facilitate the subsequent deposition; Step 3: the two side surfaces of the deposited layers are milled to obtain the desired geometric accuracy and surface quality. These steps  
Figure 1: Processing technologies for stiffened panels. (**a**,**b**) Applications of stiffened panels [23]; (**c**) riveeting; (**d**) welding; (**e**) machining; and (**f**) HWMP.  
alternate until the whole part is created. It should be emphasized that the deposited part must cool down to room temperature before next deposition or milling to avoid excessive heat accumulation. If \(N\) is too small, the alternation of deposition and milling would be repeated many times, which decreases the productivity; but if too large, the axial cutting depth is also too large, which increases the cutting force. Therefore, \(N\) is determined to be six in this study. The total thickness of six layers is about 12 mm (2 mm for one layer). \(H\) is determined to be 8 mm such that the irregular surfaces could be removed completely.
# Microseismic Data-Direct Velocity Modeling Method Based on a Modified Attention U-Net Architecture
## 3 Numerical Tests and Results
### Results
#### 3.3.2 Local Marmousi Models
They trained their network using independent microseismic records separated from multiple microseismic events within the velocity models, whereas we have considered how to update velocity models using coupled records when microseismic events are intertwined and inseparable. In addition, their research focused on VTI models, this paper used TTI models that including anticlines, synclines, and anomalous velocity models. This enables the model to effectively address geological diversity, making microseismic monitoring applicable to a wide range of fields.
## 6 Conclusions  
A novel system that consists of four probes with different spacings is proposed to realize the measurement and exact reconstruction of a pair of parallel profiles. Besides the straightness error of the measurement reference, this system and method can also eliminate the data processing error and retain a high lateral resolution. Using the new method, the characteristics of the measured profiles will be retained to the maximum extent due to the controllable interval of sampling points, and the reconstruction result of measured profiles, especially the stepwise parts, will not have data processing error as the high-order harmonic component distortion will not exist. With a high spatial frequency of the measured profiles, as the data processing error can reach the same magnitude as the height difference of the profile, the advantage of the new method in improving the reconstruction accuracy is obvious. For the reconstruction of complex profiles, prior knowledge is not needed, which means the zero calibration of probes can be skipped in this new method and the measurement process is simple to operate. The exact reconstruction can be realized when the yaw error of the measurement reference and the measurement error of sensors can be obtained through other instruments or ignored. The abilities to overcome the data processing error and zero-adjustment error have been confirmed by the theoretical analyses and simulations. An experiment was also implemented to demonstrate the advantages of the new method.  
Conceptualization, X.C. and C.J.; methodology, X.C.; software, X.C.; validation, C.S. and L.F.; formal analysis, X.C.; investigation, X.C. and C.L.; resources, C.S., S.L. and L.F.; data curation, X.C. and C.L.; writing--original draft preparation, X.C.; writing--review and editing, C.S.; visualization, C.L.; supervision, C.S.; project administration, C.L.; and funding acquisition, C.L. and L.F.  
This research was funded in part by the National Science and Technology Major Project of China (2016 ZX04003001) and in part by the High-tech Ship Research Project (first phase of Low-speed Marine Engine Engineering).  
The authors declare no conflict of interest.
## 4 Results
### Model Performance for Each Observation Element  
Table 3 displays the optimal and least effective imputation techniques for 11 meteorological observation elements. The first line of each cell in the table records the best method, and the second line records the worst method. The RMSE, MAE, R\({}^{2}\), and SMAPE were the average values of the imputation results of the imputation algorithm at five missing value rates. The definitions of the RMSE and MAE are shown in Equations (6) and (7), respectively, and the definitions of the R\({}^{2}\) and SMAPE are given in Section 4.1. \[RMSE=\sqrt{\frac{1}{m}{\sum\limits_{l=1}^{m}(y_{l}-\hat{y_{l}})^{2}}} \tag{6}\]  
\[MAE=\frac{1}{m}{\sum\limits_{l=1}^{m}}|(y_{l}-\hat{y_{l}})| \tag{7}\]  
According to the records in Table 3, the RF is undoubtedly the most ideal method for imputing missing values. For the 11 meteorological observations presented in this paper, the imputation results of the RF could maintain the highest R\({}^{2}\) and the lowest RMSE, MAE, and SMAPE. AdaBoost, ERT, Enet, BR, ARD, SVR, and TCN had the worst records, especially AdaBoost, which is not ideal for imputing missing values of the temperature (TOBS, TMIN, and TMAX) and humidity (RHUM, RHUMN, and RHUMX) data. The 20 imputation methods used in this paper performed poorly on the WSPDX, WSPDV, and SRADV, which can be observed very clearly with the R\({}^{2}\) and SMAPE, two dimensionless indicators. The R\({}^{2}\) of RF and TCN, which they perform the best, only reached 0.48-0.66 for these three observations. In the future, it is worth paying attention to the imputation of missing values for wind speed and solar radiation. Figure 8 shows the imputation results for each observation variable at the 2008 meteorological observation site. Each subplot draws the best and worst imputation results for one observation variable. As mentioned in Section 4.1, the data missing rate is the main factor affecting the data imputation results. When the data missing rate reaches 80%, there is a significant difference between the imputation results and the original data. When the
# Remote-Sensing Image Encryption Algorithm Using the Advanced Encryption Standard
###### Abstract  
With the increasing use of multimedia in communications, the content security of remote-sensing images attracts much attention in both the academia and industry. The Advanced Encryption Standard (AES) is a famous symmetric cryptosystem. A symmetric remote-sensing image encryption algorithm using AES is presented. Firstly, to reduce the encryption times, the sender groups 16 pixel values together, and converts them into big integers; secondly, the sender encrypts big integers with AES and the chaotic system; finally, the encrypted image is obtained from encrypted big integers. Simulation data show that our algorithm exhibits both the high security and efficiency.  
image encryption; remote-sensing image; advanced encryption standard (AES); chaotic system +
Footnote †: journal: Applied Sciences
# Unsupervised Classification of Human Activity with Hidden Semi-Markov Models
###### Abstract  
The modern sedentary lifestyle is negatively influencing human health, and the current guidelines recommend at least 150 min of moderate activity per week. However, the challenge is how to measure human activity in a practical way. While accelerometers are the most common tools to measure activity, current activity classification methods require calibration studies or labelled datasets--requirements that slow the research progress. Therefore, there is a pressing need to classify and quantify human activity efficiently. In this work, we propose an unsupervised approach to classify activities from accelerometer data using hidden semi-Markov models. We tune and infer the model parameters on accelerometer data from the UK Biobank and select the optimal model based on features used and informativeness of the prior. The best model achieves an average correlation of 0.4 between the inferred activities and the reference ones, with the overall physical activity obtaining a correlation of 0.8. Additionally, to prove the clinical significance of the method, we validate it by performing a linear regression between the inferred activities and anthropometric measures such as BMI and waist circumference. We show that for a sedentary behaviour and total physical activity, the proposed method achieves comparable regression coefficients to the reference labelled dataset. Moreover, the proposed method achieves a good agreement with a labelled dataset for daily time spent in a sedentary behaviour and total physical activity. The unsupervised nature of the method allows for a data-driven classification that does not require calibration studies or labelled datasets and can thus facilitate both clinical research as well as lifestyle recommendations.  
accepted: 15 August 2022  
17 August 2022  
**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.  
**Key words:** activity classification; accelerometer; hidden Markov models; wearable sensors; UK Biobank
## 3 Results and Discussion
### Effect of Water Jet Impact Angle on Rock Breaking  
The results from the experiments testing water jet impact angles are shown in Figure 9. From the data plotted in this figure, we were able to draw the following conclusions.  
1. There is an optimal jet impact angle that maximizes crushed pit depth for both rotational and fixed erosion tests. In the range examined by our experiments, an impact angle of 10\({}^{\circ}\) resulted in the deepest pits. The main reason for this is that the water jet eroded the rock to form a slot and there was high-pressure water in the slot. This high-pressure water acts as a \"water cushion,\" and when the water jet impacts the rock at a specific angle, the water cushion can reduce the jet's reflection angle. After a groove is formed by the water jet, the flow of water is facilitated and the cuttings are washed away. However, if the impact angle is increased too much, the impact force transmitted from the jet to the rock will be reduced, resulting in decreased erosion [24; 25]. Our experiments run at different flow velocities, rotation rates, and impact angles resulted in no significant change in crushed pit diameter. The diameters of the crushed pits for the 12-100-30 specimens increased by only 0.4 mm, and the diameter of the crushed pits varied by \(\pm 5\%\). The change was not large and may have been caused by differences in specimen strength or may have only been an apparent change caused by measurement errors.
2. Under fixed erosion, when the jet pressure was 6 MPa, the variations in rock-breaking depth for different impact angles were small; the maximum crushed pit depth was 14.60 mm. The rock-breaking depth under rotating jet erosion was much greater, and the maximum pit depth was 28.80 mm, which was essentially twice the depth of the fixed erosion pits. This shows that for low-pressure water jets, jet rotation speed is the dominant factor for breaking rock and the jet impact angle is of secondary importance.
3. The volume of rock broken during the rotation and fixed erosion experiments showed completely different growth modes. Under our experimental conditions the rock-breaking volume increased exponentially as impact angle increased. Although the effective rock-breaking distance exceeded the effective rock-breaking distance of the jet as the impact angle of the jet increased, the depth to which the rock was broken became significantly shallower as the impact angle increased. However, as shown in Figure 10, an oblique jet impact exerted a tensile force on the surface of the rock. The rock was rapidly damaged in an area extending radially from the jet and the radius of the crushed pit increased dramatically. This resulted in an increase in the rock-breaking area of the jet so that the rock-breaking volume by jet rotary erosion presented an exponential growth mode.  
Figure 9: _Cont._
## 3 Algorithmic Errors
### AI-Enhanced Navigation Systems
#### 3.5.3 Monitoring Environmental Conditions  
AI technology is also instrumental in monitoring and responding to environmental conditions that affect navigation. These systems gather data on various environmental factors, including weather conditions, sea states, visibility, and tidal movements, all of which can significantly impact a vessel's route and operational safety. AI algorithms analyze these data to predict adverse environmental conditions and recommend necessary adjustments to the ship's course or speed. For instance, if severe weather is forecast along the planned route, the AI system can suggest alternative paths to avoid the storm. This proactive approach enables vessels to navigate safely through challenging environments, reducing the risk of weather-related accidents and ensuring timely arrivals. Furthermore, by continuously adapting to changing environmental conditions, AI enhances operational resilience and contributes to the overall reliability of maritime transportation [13; 14; 64].  
The capabilities of AI in improving traffic efficiency and reducing accidents have also been recognized as a means to restore visually degraded images from video cameras during restricted visibility due to fog in the observed area. The research proposes a contrastive learning framework to enhance visibility under foggy conditions in intelligent maritime transportation systems. Specifically, the proposed learning method can fully learn both local and global image features, which positively impacts visual quality improvement. A total of 100 clear images depicting water traffic scenes were selected as the synthetic test dataset, and good dehazing results were achieved both visually and in terms of metrics. The enhanced images can be effectively utilized to promote the accuracy and robustness of ship detection. Consequently, maritime traffic supervision and management can be improved in the intelligent transportation system [65].
# Design of a Configurable Spike-Encoding Circuit Based on Focal Plane Array
## 3 Circuit Implementation and Simulation Results
### Charge Packet Circuit  
A charge packet circuit is hardware-friendly for pixel-level circuit implementation. The integrating capacitor is discharged by conducting the current through the photodiode. When the capacitor node potential falls below the threshold voltage, a comparator switches, producing a spike and activating a circuit reset to recalibrate the capacitor. Figure 5 depicts the circuit's construction.  
The circuit repeats this procedure during each counting cycle. In a comparator with a fixed threshold voltage \(V_{ref}\), the pulse intervals vary with the magnitude of the PD input current, resulting in a frequency pulse code that is linearly proportional to the current.
## 2 Materials and Methods
### Ground Sampling and Determination of Physiological Parameters  
Alongside the spectral analysis, we chose representative 1 m double rows of winter wheat plants for sampling near the canopy spectral sampling point in each experimental plot (N1-N5 treatments). After removing soil debris, the sampled plants were placed in an oven at 105 \({}^{\circ}\)C for 30 min to eliminate any remaining moisture. The temperature was then adjusted to 80 \({}^{\circ}\)C for consistent drying until the plant's mass remained constant. The biomass of the plant, specifically the dry mass, was determined after the drying process. The desiccated wheat plant samples were pulverized and milled, and then the nitrogen content of the plant was measured using a Kjeldahl nitrogen meter.  
The nitrogen nutrient index (NNI) is determined by dividing the actual nitrogen concentration of the above-ground plant by the critical nitrogen concentration of the crop. This calculation is based on the formula provided by [PERSON] [18].  
\[\text{NNI}=\frac{N_{a}}{N_{c}} \tag{1}\]  
where \(N_{a}\) is the actual nitrogen concentration, %, and \(N_{c}\) is the critical nitrogen concentration, %.  
The range 0.95 < NNI < 1.05 indicates that the crop nitrogen nutrition level is in the optimal state. A nitrogen nutrition shortage occurs when the nitrogen level falls below 0.95. This leads to delayed crop growth and stunted plant height. An excess occurs when the nitrogen nutrition level surpasses 1.05. This causes the crop to become overly dependent on chlorophyll production, leading to inefficient growth and a delayed maturation period [19]. The critical nitrogen concentration (\(N_{c}\)) refers to the minimal nitrogen concentration necessary for the crop to achieve its maximum growth rate of dry matter on the ground. In North China, the formula for calculating \(N_{c}\) is as follows [20]:  
\[N_{c}=4.15\times W^{-0.38} \tag{2}\]  
where \(W\) is the above-ground biomass in t/hm2.  
Figure 2: Flowchart showing the steps taken to process the hyperspectral imageries.
## 1 Introduction
This paper presents a comprehensive evaluation of BACM's effectiveness, detailing its design principles, implementation in AODV and GPSR, and a comparative performance analysis. Through simulations and real-world testing, we demonstrate how BACM can provide a robust, adaptive solution for enhanced routing in dynamic wireless networks. Further, this work provides an important contribution by giving a detailed examination of how major routing parameters--MaxJitter, Hello/Beacon Interval, and route validity time--influence the performance of AODV and GPSR routing protocols in urban and highway settings within VANETs and MANETs, respectively. We show in simulation results that optimizing these parameters can significantly improve network performance, such as improving AODV throughput by 12% in urban environments with increased MaxJitter and increasing GPSR's Packet Delivery Ratio by 10% in cities with extended route validity time. Our findings fill a gap in the existing literature by providing practical insights into how these parameters can be fine-tuned to improve routing efficiency under dynamic and diverse network conditions, which is especially important for the development of reliable communication systems for self-driving vehicles. Hence, this study provides the following:  
* A detailed study on the routing parameters MaxJitter, Hello/Beacon Interval, and route validity time and their impact on performance in urban and highway in autonomous navigation;
* Accurately identified how the key parameters could significantly improve network performance with up to a 12% increase in AODV throughput in urban settings and 10% for GPSR's Packet Delivery Ratio in cities;
* The findings offer practical applications for enhancing routing efficiency in complex network conditions that allow reliable communication systems for autonomous vehicles.
## 2 Materials and Methods
### Methods  
#### 2.3.1 Ensemble Empirical Mode Decomposition (EEMD)  
EEMD can effectively handle nonlinear and non-stationary signals, and it is widely used in various fields [43]. In the domain of remote sensing and earth sciences, EEMD is more helpful in extracting useful information and trends to better understand the dynamics of the earth system [44; 45]. The mathematics of EEMD is as follows:  
Step 1: The white noise sequence N(t) is added to the original signal e(t), generating a new time series E(t). The formula is:  
\[\text{E(t)}=\text{e(t)}+\text{N(t)} \tag{1}\]  
Step 2: N repetitions repeated the first and second steps and added N(t).  
\[\text{E(t)}=\sum_{\text{i=1}}^{\text{n}}\text{IMF}_{\text{i}}(\text{t})+\text{ R}_{\text{n}}(\text{t}) \tag{2}\]  
Step 3: After the Step 2 several Intrinsic Mode Function (IMFs) can be obtained, and then several IMFs are averaged to obtain a new IMF, which is then represented as \(\text{IMF}_{\text{nj}}(\text{t})\). The formula is:  
\[\text{IMF}_{\text{n}}(\text{t})=\frac{1}{Z}\sum_{\text{j=1}}^{\text{n}}\text{ IMF}_{\text{nj}}(\text{t}) \tag{3}\]  
\[\text{R}_{\text{m}}(\text{t})=\frac{1}{Z}\sum_{\text{j=1}}^{\text{n}}\text{R}_ {\text{mj}} \tag{4}\]where IMF\({}_{\rm n}\)(t) is a single IMF component and R\({}_{\rm m}\)(t) is the trend item. Therefore, E (t) can be decomposed as:  
\[\rm E(t)=\sum_{n=1}^{m}IMF_{n}(t)+R_{m}(t) \tag{5}\]