esa-sceva/satcom-chunk-collection huggingface dataset:
# Machine Learning for Early Outcome Prediction in Septic Patients in the Emergency Department
## 4 Discussion
#### Limitations  
This study has the limitation of an observational study. Some data were missing and could not be retrieved, due to the lack of reporting within the electronic health record. We performed analysis and imputation of missing data to reduce the influence of missingness. Data were considered missing at random after visual analysis, while no formal analysis of the type of missingness was performed. Patients included in this study relied on ICD coding for the identification of EHR data, an approach with several limitations, including suboptimal sensitivity and specificity in record retrieval. The total number of patients retrieved in this study is not comparable to that ascribed to big data analysis. However, it is comparable to previous studies on ML applied to ED [46]. In addition, as previously demonstrated by our group, machine learning techniques can be used in settings where big data are not yet available, with performance and reliability comparable to those of standard statistical techniques [47]. Accordingly, machine learning can be a useful resource for patient stratification and classification in an emergency setting and in smaller hospitals. In this work, we opted for the random forest algorithm, which is described as one of the most easily interpretable and scalable models available, and can be further increased by special techniques such as aggressive subsampling. [48]. Due to the small sample size, we were, however, unable to assess the scalability of our model within the present study.
# New Landslide Disaster Monitoring System: Case Study of Pingding Village
## 2 Material and Methods
### Historical Disasters  
The 921 Earthquake in 1999: The serious collapse areas that developed during the 921 Earthquake included the northeast slumping side slopes and the northwest Linbeiken Collapse Zone. The Linbeiken Collapse Pit experienced collapses of different scales, and despite many restoration works being done, the Linbeiken Collapse Pit has doubled in size in the past decade. The Yunlin County Government completed construction of the reinforced soil retaining wall in April 2003.  
Typhoons Mindulle and Aere in 2004: Landslides once again struck the south bank of Linbeiken in September 2004 (Figure 3). The areas with reinforced soil retaining walls and part of the north-side tea garden collapsed into the Linbeiken Collapse Pit. The collapse created a stage terrain with a 30 m height difference. The collapse area of the first landslide covered approximately 1 ha. Afterward, it was followed by several collapses and slumps of different scales. The damage caused by this collapse included the slump of a 2 m\({}^{2}\) tea garden, but there were no casualties. Residents from four dwellings moved for safety purposes. Regarding public facilities, a diversion ditch of approximately 23 m in length and three areas of PVC (polyvinyl chloride) piping collapsed and were pulled apart.  
Continuous torrential rain in June 2006: The flood on 9 June 2006 caused the collapse of the gabion slope protection on the north side of the Linbeiken source bank and the wash-away of part of the Yunlin 61 st Line Highway roadbed.  
22 May 2007: At 6 a.m., with no influence of rainfall, the original collapse area of the Yunlin 61 st Line Highway suffered from another topple, damaging two-thirds of the main access road of the nearby villagers, making it impassable. Emergency work such as fence establishment and warning sign installations were carried out. The main reason for this collapse was the gravity effect influenced by saturation of the soft soil caused by the cracking of tap water pipes of the area, which led to the seepage of tap water and drizzle on previous days.
## 5 Results and Discussion
### Glass-Fiber Reinforced Plastic (GFRP)
In view of the dominance of transverse attenuation, this aspect is an important key to understanding the Lamb-wave behavior in anisotropic plates. Lamb-wave attenuation tests of the XP-GFRP sheet indicated that observed \(\alpha\)-S\({}_{\rm o}\) and \(\alpha\)-A\({}_{\rm o}\) values were approximately equal to its bulk-wave attenuation coefficients. However, the selected \(\alpha_{\rm L}\) and \(\alpha_{\rm T}\) values were the lowest of the longitudinal and transverse attenuation coefficients, and the observed attenuation exceeded them. Because multiple vibration modes define Lamb waves, the results presented here are insufficient to suggest physical insight with respect to wave attenuation mechanisms. Complicating this task is  
Figure 18: (a) Observed \(\alpha\)-S\({}_{\rm o}\) values for 2.3-mm XP-GFRP sheet with the calculated attenuation and dashed lines for \(\alpha_{\rm L}\) (=\(\alpha_{33}\)) and \(\alpha_{\rm T}\) (=\(\alpha_{32}\)). Blue dotted line is for \(\alpha_{\rm L}\) = \(\alpha_{22}\), showing differences in 0\({}^{\circ}\) and 90\({}^{\circ}\) directions. Two test series are plotted using blue symbols. Both used Olympus V101 Tx and KRN-BB-PC Rx. Data with + symbols used short-pulse excitation and dot data used 400-kHz Gaussian pulse. (b) Observed \(\alpha\)-A\({}_{\rm o}\) values for 2.3-mm XP-GFRP sheet with the calculated attenuation and dashed lines for \(\alpha_{\rm L}\) and \(\alpha_{\rm T}\) (=\(\alpha_{32}\)). Red dotted line for \(\alpha_{\rm T}\) = \(\alpha_{32}\) is also plotted. Three data series are shown. Red dot data used Olympus V103 Tx and KRN-BB-PC Rx, red square data with AET AC375L Tx and PAC Pico Rx, and red + data with NDT C16 Tx and KRN-BB-PC Rx. All Tx used 60\({}^{\circ}\) incident angle and short-pulse excitation. the commanding roles of transverse-wave attenuation. Further exploration is necessary both from theory and experiment.
# AI-Driven Adaptive Communications for Energy-Efficient Underwater Acoustic Sensor Networks
## 5 Performance Analysis
### Numerical Results
#### 5.2.3 Delay Analysis  
Figure 10 shows the end-to-end delay (in s) as a function of packet length (in bytes). Both BPSK and QPSK follow the general trend of decreasing delay as the packet length increases. However, BPSK consistently requires approximately twice as much time as QPSK. Smaller packets incur proportionally higher overheads than larger packets, so increasing packet size allows more useful information to be carried per packet and, thus, improves efficiency and reduces delay. Furthermore, the higher spectral efficiency of QPSK enables it to transmit more bits per symbol than BPSK, which further shortens the end-to-end delay. For packet lengths of 2 and 3 bytes (representing rare fish species), only BPSK modulation can be chosen; however, it provides the advantage of greater robustness at lower gains and in noisy conditions. For the remaining fish classes, the delay trend aligns closely with the general behavior described above.
# Game Theory-Based Interactive Control for Human-Machine Cooperative Driving
## 4 Vehicle Interactive Control Strategy
### Shared Control Strategy
#### 4.1.3 Human-Machine Shared Control Model  
In an HSCV, driver and automation system control inputs are integrated to control the vehicle jointly. The continuous state equation of the vehicle-driver model is as follows:  
\[\dot{\mathbf{x}}(t)=\mathbf{A}_{c}\mathbf{x}(t)+\lambda\mathbf{B}_{c}\mathbf{u}^ {\underline{b}}(t)+(1-\lambda)\mathbf{B}_{c}\mathbf{u}^{\underline{e}}(t) \tag{13}\]  
The subscripts \(h\) and \(e\) are used to distinguish human driver control inputs and automation system control inputs, respectively, as well as their associated matrices. Where the vehicle status vector is \(\mathbf{x}=\left[\begin{array}{cccccc}v_{y}&v_{x}&\phi&\omega&Y&X\end{array} \right]^{\mathrm{T}}\), control input vectors are \(\mathbf{u}^{e}=\left[\begin{array}{cccc}\delta^{e}&a^{e}\end{array}\right]^{ \mathrm{T}}\) and \(\mathbf{u}^{\underline{h}}=\left[\begin{array}{cccc}\delta^{h}&a^{h}\end{array} \right]^{\mathrm{T}}\). The matrices \(\mathbf{A}_{c}\) and \(\mathbf{B}_{c}\) are defined as follows.  
\[\mathbf{A}_{c}=\left[\begin{array}{cccccc}-\frac{2C_{f}+2C_{r}}{m\upsilon_{ x}}&0&0&-\upsilon_{x}+\frac{2C_{r}l_{b}-2C_{r}l_{a}}{m\upsilon_{x}}&0&0\\ \omega&0&0&0&0&0\\ 0&0&0&1&0&0\\ -\frac{2C_{r}l_{a}-2C_{r}l_{b}}{l_{b}\upsilon_{x}}&0&0&-\frac{2C_{r}l_{a}^{2 }+2C_{r}l_{b}^{2}}{l_{b}\upsilon_{x}}&0&0\\ 1&0&\upsilon_{x}&0&0&0\\ 0&1&-\upsilon_{y}&0&0&0\\ \end{array}\right]\]  
\[\mathbf{B}_{c}=\left[\begin{array}{cccccc}\frac{2C_{f}}{m}&0&0&\frac{2C_{f}l _{a}}{l_{z}}&0&0\\ 0&1&0&0&0&0\\ \end{array}\right]^{\mathrm{T}}\]  
In computer programs, it is necessary to use the sampling time \(T_{s}\) to discretize the continuous form of the formula. The discrete state equation can be obtained as follows:  
\[\mathbf{x}(k+1) =\mathbf{A}\mathbf{x}(k)+\lambda\mathbf{B}\mathbf{u}^{h}(k)+(1- \lambda)\mathbf{B}\mathbf{u}^{e}(k) \tag{14a}\] \[\mathbf{z}(k) =\mathbf{C}\mathbf{x}(k) \tag{14b}\]  
where \(k\) represents the discrete time index and the matrix discretization method is \(\mathbf{A}=\mathbf{I}_{6}+T_{s}*\mathbf{A}_{c}\), \(\mathbf{B}=T_{s}*\mathbf{B}_{c}\). \(\mathbf{z}\) is a matrix that filters \(\mathbf{x}\) according to the control target, and \(\mathbf{C}=\left[\begin{array}{cccccc}0&0&1&0&0&0\\ 0&0&0&0&1&0\\ \end{array}\right]\) is the slack matrix, used for the selection of vehicle state.
## 2 Spatial Domain Chaotic Watermarking  
The initial chaotic watermarking was conducted in the spatial domain via scrambling with a generated sequence using the chaotic logistic map.
## 4 Hyperspectral Image Destriping Method
### The Comparison of Single-Band Destriping Methods Based on the Same Paradigm  
We extracted a sub-image of size \(5000\times 5000\times 32\) in our experiment and selected several bands (band 10, band 24, band 31) to display effects, and compared the experimental results with the destriping methods based on the same paradigm.
## 5 Conclusions  
In an urban setting, network traffic demands are usually high, and network infrastructure operates at maximum or near-maximum capacity. In such a setting, traffic patterns may at first appear random and non-uniform. Such patterns are usually a function of time of day, population distribution, season, weather, special events (fans at a football stadium, conferences, festivals), etc. This study investigated various graph-coloring algorithms, and developed an improved and optimal L(3,1) graph-coloring algorithm to color a hexagonal cellular graph matrix (m \(\times\) n). This algorithm was based on the Fixed Channel Allocation (FCA) scheme. Cell grids are permanently stationed and located at the same distance from the nodes. This study resolved the challenge posed by co-channel interference by creating a co-channel reuse distance, \(\sigma\). Additionally, the problem caused by interference from adjacent channels was resolved by imposing channel separation. It was observed that channel gap has an inverse relationship with the distance between stations. Other constraints taken into account were interference between adjacent channels, as well as co-channel interference. These findings suggest appropriate changes that network providers can make to channel allocation to maximize channel assignments which bring about transmission efficiency.
# Responsible AI in Farming: A Multi-Criteria Framework for Sustainable Technology Design
## 2 Farmers' Autonomy within a Complex Socio-Technological-Ecological Environment
### Concept of Autonomy  
Depending on the domain and the subject under study, different notions of autonomy are present. The concept of \"autonomy in farming\" is multifaceted as well and can thereby also be viewed from different perspectives. It can refer to farmers' ability to make independent decisions about their agricultural processes or maintain their financial independence [10]. It can also refer to the autonomous systems on the field and their ability to achieve certain goals [11]. In practice, there is a range of complex social, economic, legal, ethical, and environmental interactions (e.g., weather variability, pests, and water availability) that may positively or negatively affect the perceived freedom of choice and the ability to achieve one's goals. However, the definition of autonomy is not limited to the \"perceived ability\" to make a decision but also in the absolute sense, as regulations, environmental and economic constraints, cultural practices, biased predispositions, or social embedding can limit one's ability to make independent and beneficial choices. Thereby, autonomy is influenced by a variety of aspects, such as trust in the technology and one's abilities, financial stability, access to information and markets, digital literacy, security and privacy of systems, as well as economic, social, technological, and psychological dependencies in all forms [12; 13; 14; 15; 16]. Therefore, the rapid integration of AI technologies into the daily lives of farmers could threaten this autonomy in various ways. For instance, AI-driven decision-making tools could limit independent control over agricultural practices by increasing technological blindness or negatively influencing the costs associated with adopting and maintaining new technologies that can only be managed by external service providers [7]. Table 1 gives a high-level overview of the exemplified cause-effect relationships that influence farmers' autonomy.
## 3 Methodology
### BP Neural Network Delay Model
#### 3.2.1 Data Normalization  
In order to map all sample data to the same scale, it needs to be subjected to the maximum value normalization process, i.e., all sample data are mapped to a range of 0-1, as shown in Formula (1).  
\[x_{i}=\frac{x^{\prime}_{i}-x^{\prime}_{\text{min}}}{x^{\prime}_{\text{max}}-x ^{\prime}_{\text{min}}} \tag{1}\]  
where \(x_{i}\) represents the normalized sample data, that is, the input data of the neural network model; \(x^{\prime}_{i}\) represents the original sample data; \(x^{\prime}_{\text{min}}\) represents the minimum value in the original sample data; \(x^{\prime}_{\text{max}}\) represents the maximum value in the original sample data.
## 3 Experimental Setup  
Figure 2a presents a lateral view of both shafts (1 and 2, placed in the headstock) involved in the beating phenomenon due to rotary unbalance.  
A friction belt transmission with a flat drive belt 3, a pulley 4 (on shaft 1) and a pulley 5 (on shaft 2) synchronously rotates both shafts (the theoretical value of transmission speed ratio is 1:1). Here 6 depicts an additional mass (10.8 g, a permanent magnet) placed in different angular positions on pulley 5 and used to change the internal unbalancing of the shaft 2.  
The shaft 1 is also the lathe main spindle (with the jaw chuck labelled with 7 on Figure 2b placed on the opposite side of Figure 2a). Figure 2b shows an absolute velocity vibration sensor 8 (an electrodynamic seismic geophone Geo Space GS 11D, now HGS Products HG4 as described in [40]), placed on the headstock. The geophone corner frequency (8 Hz) is smaller than the minimum frequency of the headstock vibration (17 Hz). No significant resonant amplification at the corner frequency can be identified (the open circuit damping being 34% of critical damping). The geophone sensitivity is 31.89 V/m/s.  
The signal delivered by the vibration sensor (a voltage proportional to the vibration velocity of the headstock) is numerically acquired by a personal computer via a computer-assisted numerical oscilloscope PicoScope 4424 from Pico Technology, Saint Neots, UK (USB powered, four channels, 12 bits resolution, 80 MS/s maximum sampling, 32 MS memory) [41]. Due to the high sensitivity of the geophone and oscilloscope features (e.g., a numerically controlled internal amplifier) the supplementary amplification of the signal delivered by vibration sensor is not necessary.  
The computer-aided processing of this signal was done in Matlab. Firstly, the signal delivered by sensor must be mathematically divided by the sensitivity of the sensor in order to obtain the vibration velocity evolution. Secondly, the velocity of vibration must be numerically integrated (by antiderivative calculus) in order to obtain the vibration displacement evolution. The description and analysis of the  
Figure 2: (**a**) A lateral view of the shafts involved in the beating phenomenon (with a flat belt transmission between the shafts); (**b**) A front view of the lathe headstock with the vibration sensor.  
evolution of some of the other vibration features (e.g., the combination frequency \(f_{c}\), the beat vibration amplitude and the free vibrations of the lathe on foundation) were also performed.  
In order to measure the average value of the instantaneous angular speed (IAS) \(\omega_{1}\) of the main spindle (shaft 1) rigorously, the technique described in our previous work [42] was used (with a two phase multi-pole AC generator placed in the jaw chuck, as an IAS sensor). The same technique (which refers only to signal processing, briefly described later on) was used for an accurate measurement of the combination frequency \(f_{c}\).
## 1 Introduction
[13] used multi-year (2005-2012) Envisat ASAR observations to create, for the first time, a global potential water body map at a spatial resolution of 150 m. Errors concentrated along shorelines and coastline, but this global water map has an accuracy of \(\sim\)80% compared to the reference data. The Mekong Delta in Southeast Asia (one of the largest deltas in the world) is a vast triangular plain of approximately 55,000 km\({}^{2}\), most of it lower than 5 m above sea level. The seasonal variation in water level results in rich and extensive wetlands. For instance, the Mekong Delta region covers only 12% of Vietnam but produces \(\sim\)50% of the annual rice (with two or three harvests per year depending on the provinces), represents \(\sim\)50% of the fisheries, and \(\sim\)70% of the fruit production. In the Delta, the dry season extends from November to April and the rainy season from May to October. Many researches have been carried out to monitor the surface water in the Delta, using both optical and active microwave satellite images. Ref. [14] produced a monthly mean climatology of the water extent from 2000 to 2004 with a spatial resolution of 500 m, using visible and NIR MODIS/Terra data. However, with 85% to 95% cloud cover during the wet season over the Mekong Delta [15], remote sensing methods derived from visible and NIR images present some limitations. Different SAR observations have also been exploited to study floods and wetlands over the Delta. Ref. [16] mapped flood occurrence for the year 1996 over the Delta using five ERS-2 observations. Ref. [17] used 60 Envisat ASAR observations during the years 2007-2011 to study the flood regime in the Delta. Thanks to the launch of the Sentinel-1A &B satellites, as well as the free data policy of the European Space Agency (ESA), Sentinel-1 SAR observations are now regularly and freely accessible for scientific and educational purposes, over large parts of the globe. Similar to previous SAR instruments, Sentinel-1 instruments show strong potential for detecting open water bodies at high spatial resolution [18; 19]. With the advantage of higher temporal resolution than previous SAR instruments, Sentinel-1 has the ability to monitor the seasonal cycle of water extent every six days over Europe and the boreal region, and with slightly reduced temporal sampling elsewhere. In this study, we propose a methodology using Sentinel-1A SAR observation for monitoring water surface extent within Cambodia and the Mekong Delta for the year 2015. It is based on a Neural Network (NN) algorithm, trained on visible Landsat-8 images (30 m spatial resolution). At the time of this study, the temporal resolution of Sentinel-1 over the Delta was 12 days: it reduced to 6 days after the launch of the Sentinel-1B in April 2016. The Sentinel-1 SAR data and the ancillary observations are described in Section 2, including the pre-processing steps. Section 3 presents the NN methodology, along with sensitivity tests. Results and comparisons with other products are provided and discussed in Section 4. Section 5 concludes this study.
# An Under-Sampling Array Signal Processing Method Based on Improved Hadamard Matrix
## 5 Conclusions  
In this paper, we proposed a compressive sensing processing model for under-sampled array signals. Aiming at the key technology of measurement matrix, an improved Hadamard matrix based on the compression zeroing method is proposed, and the column-to-column non-correlation in the measurement matrix is enhanced, thereby improving its processing speed and reconstruction estimation performance. It can be seen from the simulation and measured data processing results that the compressive sensing processing method proposed in this paper has higher resolution, and can greatly reduce the computing time, and compared with the Toeplitz circulant matrix, m-sequence matrix, Sequential-Part Hadamard matrix, the improved Hadamard matrix proposed in this paper not only has the satisfying performance under high compression ratio, but also maintains good target orientation estimation performance at 41% compression ratio, and there is no obvious estimation error until the compression ratio is 37%. This method provides an effective way for array signal processing, especially for under-sampling array signal processing.  
In addition, there are many sparse array structures that have similarities to the compression method proposed in this paper, such as the coprime array and nested array, etc., and it is also trying to use a smaller number of equivalent sensors to estimate the same number of sources or given the same number of sensors and estimate as many sources as possible. However, these sparse array structures do not have the flexibility of the method proposed in this paper. As long as a certain compression ratio is satisfied, it is not necessary to satisfy the arrangement conditions of these sparse array structures and better estimation results can be obtained.  
Conceptualization and methodology, T.S. and Q.G.; validation, Q.G. and M.L.; formal analysis, Y.W. and Y.G.; writing--original draft preparation, T.S. and Q.G.; writing--review and editing, Q.G.; funding acquisition, T.S. and Y.G. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Zhejiang Provincial Natural science foundation of China (No. LZ20F010002), and the funding from the Extension Fund from Underwater Test and Control Technology Key Laboratory (No. YS24071802).  
Data Availability Statement: Data sharing not applicable.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Improved Hadamard**} \\ \hline Compression Ratio & 1 & 2 & 3 \\
70\% & 81.5\({}^{\circ}\) & 85\({}^{\circ}\) & 89\({}^{\circ}\) \\
60\% & 81.5\({}^{\circ}\) & 84.5\({}^{\circ}\) & 89\({}^{\circ}\) \\
52\% & 8