esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Correlation of CFAPAR with Phenological Variables
These findings show that besides the ubiquitous importance of GSL (grassland: 79% of pixels with significant correlation, mean \(r=0.75\); cropland: 91% of pixels with significant correlation, mean \(r=0.8\)), biomass production in the more arid grassland band in the north is sensitive to the maximum greenness the vegetation attained given the limited water availability and, to a lesser degree, to the timing of vegetation onset (and presumably, to the timing of rainfall). On the contrary, in the southern band, where croplands are more widespread and where the growing season is generally longer, the timing of SOS appears to be more critical for biomass production and promising as an early indicator of seasonal performance. In this area, crops are sown as soon as enough precipitation is received for effective crop growth; late sowing delays plant growth and may prevent full biomass development before flowering. It is worth noting that exceptions to this behavior are not infrequent: no significant correlation is found between \(\Delta\)SOS and CFAPAR in scattered and large areas of the region. In such areas, where the timing of season start has no effect on seasonal production, vegetation monitoring and biomass production forecast should not be based on SOS. At the same time, this finding questions the reliability of the traditional approach using the anomaly of a remote sensing indicator (e.g., NDVI) to assess vegetation status, as a delay in SOS leads to a negative anomaly during the green-up period without any actual effect on final production in these areas where no significant correlation is found between \(\Delta\)SOS and CFAPAR. By limiting the analysis to the pixels showing a significant correlation between \(\Delta\)SOS and CFAPAR, we can highlight the influence of the SOS delay on the final biomass production of each single pixel. Figure 5 shows the variation in CFAPAR per unit variation in SOS (_i.e._, the CFAPAR decrease associated with a one-day delay in SOS), expressed in terms of the percentage of the pixel-level mean CFAPAR value. Despite the fact that \(\Delta\)SOS is more strongly correlated with CFAPAR in the southern agricultural band, the relative impact of SOS variation is higher in the north, because CFAPAR and GSL are reduced from south to north (see Figure 2). In other words, the annual production of the herbaceous dominated areas in the north, characterized by a short pulse of vegetation growth, is more susceptible to changes in SOS, although not in a spatially-consistent manner. Figure 5: Percentage of variation of the mean seasonal biomass production proxy (CFAPAR) for a one-day anomaly in the timing of SOS. The metric is computed as the slope of the linear regression CFAPAR _vs._\(\Delta\)SOS expressed as a percentage of the pixel-level mean CFAPAR. Pixels where the linear regression is not significant (n.s.) at \(p=0.05\) are masked in grey.
# Quantitative Analysis of Influencing Factors on Changzhou Ship Lock Capacity
## 3 Methods
### Eliminate Navigability Data Redundancy Based on Hydrological Analysis  
The shipping industry on the Xijiang River exhibits distinct seasonal characteristics, with the low-water period typically occurring from September to the following April, and the flood season from May to August each year. Due to influence of climatic and geographical factors, the flood season may arrive earlier or later by 1-2 months in some areas. During the low-water period, reduced precipitation and the need to dilute salinity lead to insufficient shipping base flows. Additionally, factors such as riverbed erosion, channel regulation, and hydroelectric power generation severely constrain the Changzhou ship lock capacity [6]. For ship passing data that do not meet navigability conditions, hydrological factors play a decisive role in influencing the ship lock capacity, which affects the analysis of influencing factors.  
To address this situation, ship passing data and hydrological monitoring data are compared on a unified time scale, and periods that do not meet navigability conditions are identified through custom query filters. The specific steps are illustrated in Figure 4. Firstly, preprocess the ship passing data and hydrological monitoring data to obtain daily cargo volume data and daily water hydrological data. Secondly, for daily hydrological data, calculate the time periods when the upstream water level is below the minimum navigable level, the downstream water level is below the minimum navigable level, the water level difference between upstream and downstream exceeds the maximum lock water head [27], and the water level difference is below the normal range. For daily cargo volume data, identify periods when the cargo volume is significantly below normal, typically due to temporary navigation suspensions caused by equipment maintenance, water accidents, or extreme weather. Then, merge the two sets of time periods, considering that non-navigable conditions affect normal navigation not just at a single point in time, but also in neighboring periods. Combining adjacent time periods improves the continuity and accuracy of the data. Finally, plot daily cargo volume data, daily water hydrological data, non-navigable conditions, and the corresponding time intervals on a unified time axis to visually present the analysis results and conduct a detailed analysis of the extracted data.
# Optical Fiber Sensor with Stable Operating Point for AC Magnetic Field Measurement
## 3 Sensor Fabrication and Characterization
### Sensor Characterization
A Tesla meter (AIPLI, Quzhou, China; KT-101) was placed near the sensor to monitor the magnetic field intensity, and an FBG demodulator (CASSTK, Shenzhen, China; SAI-4 YX3 AL) was used to monitor and record the shift of the HAFBG peak in real time. The magnetostrictive square rod is stretched by the increasing magnetic field intensity, causing an increase in the HAFBG period and a red shift in the Bragg wavelength, as seen in Figure 3b. The fitting magnetic field response curve shows a quadratic function shape, expressed as \(\lambda=1549.57-1.79\times 10^{-4}.B+4.39\times 10^{-4}.B^{2}\), which is mainly caused by the natural nonlinear response of giant magnetostrictive materials [16]. For a low magnetic field range from 0 to 2.5 mT, the approximate linear sensitivity \(S_{low}\) is about 0.84 pm/mT, and for a high magnetic field range from 3.5 to 6 mT, the approximate linear sensitivity \(S_{high}\) is about 3.95 pm/mT. Figure 3: (**a**) Experimental system setup for DC magnetic field sensing. (**b**) Bragg wavelength shift of the HAFBG as a function of DC magnetic field intensity. Figure 2: (**a**) Measured spectra of the sensor corresponding to the ambient temperature in the range 21.0–39.9 °C. (**b**) Measured spectra of the sensor corresponding to the heating laser power in the range 0.005–456.4 mW. (**c**) Ambient temperature response of the HAFBG Bragg wavelength. Inset: Schematic diagram of the experimental setup for temperature measurement. (**d**) Heating laser power response of the HAFBG Bragg wavelength. Inset: Schematic diagram of the experimental setup for heating laser power measurement.
# Soluble Elements Released from Organic Wastes to Increase Available Nutrients for Soil and Crops
## Materials and Methods
### Residue Characterization and Methods  
All residues were subjected to conditioning processes consisting of air drying at room temperature inside a greenhouse (reaching temperatures over 40 \({}^{\circ}\)C), shredded and sieved (2 mm). Residue characterization consisted of the analysis of bulk density (\(\rho_{\text{b}}\)), organic matter content (OM), moisture content (MC) and elemental composition. \(\rho_{\text{b}}\) was calculated volumetrically as a ratio between residue mass and volume by using the cylinder method. An LED digital drying and sterilization oven (J.P. SELECTA(r), Conterm 2000253, Barcelona, Spain) was needed to get MC and OM (UNE-EN 13040) [45]. Biowaste samples were dried at 103 \({}^{\circ}\)C until the difference between two successive weightings was less than 0.1 g for MC and 0.01 g for OM. The determination of organic matter (OM), expressed as a percentage by weight of dry matter, also required a muffle (Nabertherm, controller P320) and was determined by loss on ignition at a temperature of 450 \({}^{\circ}\)C until the difference between two successive weightings is less than 0.01 g (UNE-EN 13039) [46].  
Elemental composition was determined by atomic absorption spectrometer (AAS) (Thermo Scientific, iCE 3000 Series AA Spectrometer) after acid digestion (69% nitric acid + H\({}_{2}\)O\({}_{2}\)) of samples (0.2 g) in a microwave. AAS is calibrated before use by testing the absorbance with solutions of quantitative certificated standards. Instrumental parameters are listed in Table 1.  
The aqueous extraction of nutrients (1:10 \(w/v\)) of each residue was obtained by using 100 mL of deionized water added to 10 g of residue and shaking for 2 h. After filtering, the pH was measured by using a CRISON GLP 21 pH meter, electrical conductivity (EC) with a CRISON GLP 31 conductivity meter, and macro (Na, K, Ca, Mg) and micronutrients (Fe, Mn, Cu, Zn) composition with an AAS (Thermo Scientific, iCE 3000 Series AA Spectrometer).  
Additionally, nutrient solubility index (I\({}_{N}\)) was calculated as the percentage of nutrients extracted in aqueous solution with respect to elemental composition in each residue, both expressed in dry weight basis, according to Equation (1) [43]:  
\[\text{I}_{N}=(\text{W}_{N}/\text{C}_{N})\times 100 \tag{1}\]  
N: macro or micronutrient; W\({}_{N}\): water extractable nutrient content; C\({}_{N}\): elemental composition nutrient content.
# Composition and Basic Physical Properties of the Phobos Surface: A Comprehensive Review
## 4 Surface Composition of Phobos
### Speculative Composition of Phobos
#### 4.3.2 Giant Impact Hypothesis  
If Phobos formed as a result of a giant impact (high-energy collision) capable of creating the Borealis basin [42], it should consist of roughly half Martian material and half impactor material [34]. Interaction with lower energy may result in a higher content of Martian material, up to 80% [14; 43]. However, the final composition should be quite similar to that predicted by [PERSON] et al. [33], but with less iron-rich silicate dust [14]. Additionally, it is hypothesized that the Martian material will largely originate from the Martian mantle (depths up to \(\sim\)50-150 km) [34].  
If the giant impact hypothesis is correct, Phobos should be depleted in volatile substances such as water vapor because impacts are generally energetic events [32; 34].  
[PERSON] et al. [33], in investigating the chemical composition of the building blocks of Phobos (and Deimos) in the giant impact scenario, utilized thermodynamic equilibrium calculations. A schematic of this chemical modeling in the giant impact scenario, where dust originates from condensing gas and solid bodies form from solidifying material, is presented in Figure 3.  
Thermodynamic calculations, utilizing various types of impactors (such as Mars, carbonaceous chondrites, enstatite chondrites, and comets), predict a wide variety of final compositions carrying different amounts of C, H, O, Fe, and Si [33].  
Each type of impactor should impart specific, unique markers [33], for example, the following:  
* Mars + CV (CV--carbonaceous chondrites) would contain large amounts of metallic iron, SiO\({}_{2}\), iron sulfides, and carbon;
* Mars + Comet would have pyroxenes and the highest content of carbon and ice;
* Mars + EH (EH--enstatite chondrites) would contain a high content of metallic iron, SiO\({}_{2}\), iron sulfides (FeS and MgS), and trace amounts of SiC;
* Collisions with Mars-like objects would yield iron oxides;
* Mars + CI (CI--carbonaceous chondrites) would include iron oxides, water ice, and carbon.  
The presence or absence of compounds such as metallic iron, iron oxides, iron-rich silicates, sulfides, carbon, or water ice could indicate the nature of the impactor [33].
# Design and Evaluation of Steganographic Channels in Fifth-Generation New Radio +
## 4 Protocol Analysis and Covert Channel Design  
The focus of our analysis was on the Layer 2 protocols of 5G New Radio, which include the Service Data Adaption Protocol (SDAP), Packet Data Convergence Protocol (PDCP), and Radio Link Control (RLC). They seem the most promising part of the 5G protocol stack for the reasons given below: The exploitation of MAC procedures in 4G and 5G for covert communication was already covered by [PERSON] in [18; 19], as mentioned before. On Layer 3, the RRC is not considered a suitable protocol for continuous covert transmission, since it is only used for signaling and control instructions on the control plane. The physical layer (Layer 1) does not seem suitable for information hiding because modifications of the time-critical procedures would probably cause failures of the wireless connection. In the following, the headers of the Protocol Data Units (PDUs) of the SDAP, PDCP and RLC are analyzed. For convenience, the headers are shown in Figure 2.
## 3 Results
### Preparation of Data for Multivariate Forecast Model  
The time series acquired data from the experiment to the gateway server are converted to CSV format to be used in LSTM as input data. Before LSTM data preparation in Anaconda Python, a separate environment is created by the installation of Keras with Theano, as TensorFlow proved the slowness of the system used for implementation. Scikit-learn, Pandas, NumPy, Seaborn, and Matplotlib libraries are also installed for data preprocessing, visualization, and machine learning algorithms. Preprocessing of the data was conducted as an initial phase for the prediction that is required for missing values tracking and elimination. At this step, the time-series data is to fill the missed values. The interpolation of the variables before and after a timestamp is used for missed values. Figure 10 shows the sample of the first five rows retrieved using head() method in pandas among 19,879 records of the dataset.
# Environment-Aware Knowledge Distillation for Improved Resource-Constrained Edge Speech Recognition
## 5 Experimental Results and Discussion
### To Distill or Not to Distill (A Robust Model)  
Several works have proposed to make large speech models more robust to environmental factors via adversarial training [15], disentanglement [16], or data augmentation [4], to name a few methods. The results reported herein suggest, however, that applying conventional distillation methods to robust teacher models does not guarantee that the resulting compressed model will retain robustness to its fullest. Results from Table 1, for example, show the WER achieved from the DistilRobustHuBERT student model being roughly 2.5 times higher than that achieved by the original Robust HuBERT teacher. In the high-noise conditions, from Table 2, WER went up almost three times with the compressed student versions. While distilling from a noise-robust teacher (DistilRobustHuBERT) showed some improvement over distilling from an original teacher (DistilHuBERT), the obtained results are still far from those achieved with the proposed method. These findings suggest that adding robustness to the distillation process, as proposed herein, is more important than finding a robust latent representation in which distillation can be applied to.
## 3 Experimental and Computational Methods
### Computational Setup
#### 3.2.4 Numerical Schemes, Models and Thermodynamic Table  
RANS transient simulations with the proposed RFM model are performed using the \(k\omega\)-\(SST\) turbulence model [42]. The numerical solution of the transport equations is based on a pressure-based solver with a modified Pressure Implicit with Splitting Operator (PISO) algorithm for real fluid simulations. Such a modified PISO has been validated in previous work, such as in references [16; 24; 26]. The spatial discretization is second-order accurate with a central difference scheme and the time integration is performed using  
Figure 4: Computational model of de Laval nozzle used in simulations showing the mesh details. The nozzle dimension are depicted alongside in mm.  
a Crank-Nicolson scheme for the momentum equation and an implicit first-order Euler scheme for the rest of the equations. The time step is not fixed in the simulations as the CONVERGE solver [33] does a good job in adjusting the step, as per the requirements based on the Courant-Friedrichs-Lewy (CFL) number. The initial time step of the simulations is set to one microsecond.  
A 3D table covering the full range of (P-T-Y) values for the binary mixture (CO\({}_{2}\) + N\({}_{2}\)) was generated for all the simulation conditions. This thermodynamic table has its three axes (1401 \(\times\) 201 \(\times\) 6) as range of pressure (1.5-8.5 MPa), temperature (240-340 K) and species mass fraction YCO\({}_{2}\) (0.95-1). The employed thermodynamic table resolution is based on previous studies [16; 24; 25; 26; 43], and will be shown to provide sufficiently accurate results in the following sections. In addition, table sensitivity was independently performed and the above mentioned table matrix size was found to be the most suitable.
## 3 Results
### Comparison of CPU Resources for Different Orders  
This section investigates the computing efficiency of models at different orders. Higher-order gravity field models produce more precise orbit determination results, but they also add significant computing complexity. This is especially true for spaceborne GNSS receivers with limited CPU power, where computational efficiency is important. Thus, in addition to comparing accuracy, it is critical to assess the computational efficiency of various gravity field models. To accomplish this, this section examines the computation times of various order models for processing identical data.
# Similarity Estimation for Large-Scale Human Action Video Data on Spark
## 3 Proposed Framework
### Similarity Measure  
Cosine similarity is applied to estimate the similarity of human action from two human action videos (e.g., user performed video and reference video). We also need to consider the human movement speed when comparing two different videos. Hence, we apply the neighbor frame comparison scenario. Suppose we have one reference video and one user performed a video to be compared for obtaining similarities. We compare each user frame with three neighbor frames of reference videos. The reason is that if a user performs a motion late, then it is a wrong motion in consideration of the whole action performance similarity because we are comparing motion similarity between a user action video and reference action video. More importantly, all action performances are successive. The maximum comparison computation value of each comparison group is the final similarity value of the user frame. The comparison process of all frames between two videos are demonstrated in Figure 3. Table 1 shows similarity measures with different neighbor frames. Two feature vectors correspond to the user video \(F_{q}\) and the reference video \(F_{d}\), respectively:  
\[cos\theta=\frac{F_{q}\cdot F_{d}}{\|F_{q}\|\cdot\|F_{d}\|}. \tag{8}\]  
Figure 2: An example of Edge based Local Pattern Descriptor.  
In Algorithm 1, for the process of the feature extraction algorithm, input is human action video data, and output is ELP feature vectors. We have _Video_Name N_ and _Video_Data D_ for input. We have _Video_Name N_ and _Feature_Vector V_ for output. Load human action video data stored in the HDFS. Then, \(flatMap\) stage is implemented circularly. The background image is defined as _B1_. The number of frames is defined as \(i\). For all video data, background subtraction is applied. it is defined as _Ba_ after finishing background subtraction. Then, ELP is applied to all _Ba_, thus feature vectors are obtained. In this way, all feature vectors of relevant human action video are obtained, defined as \((N,V)\).  
```
Input: RDD [VideoName N, VideoData D] Output: RDD [VideoName N, FeatureVector V] for all (\(N\),\(D\)) \(\in\) RDD[\(N\),\(D\)] parallel do /*flatMap stage*/  BI \(\leftarrow\) BackgroundImage for i=1 to NumberofFrame  a \(\leftarrow\) D[i]  Ba \(\leftarrow\) apply BackgroundSubtraction(a,BI)  V[i] \(\leftarrow\) apply ELP(Ba) endfor  add (\(N\),\(V\) to result RDD) endfor
```  
**Algorithm 1**Feature Extraction
# Water IoT Monitoring System for Aquaponics Health and Fishery Applications
## 6 Farmhouse System  
### Gateway Sensor  
In order to extend the communication range as proposed in Figure 3, a gateway sensor has been proposed.  
For the Gateway sensor, we will use a state-of-the-art, ready-to-use sensor, specifically Pycom FiPy [31] with PySense 2.0 by Pycom [32] extension boards. However, even if the hardware is currently mature enough, the software inside the sensor needed to perform the 'forwarding' capabilities between LoRa/LoRaWAN and WiFi does not exist. This software has been fully developed in the context of this work to produce a fully functionally sensor. Figure 10 shows an image of the prototyped gateway. The prototype has two different antennas to connect two different wireless technologies, and it has a USB cable mainly used to provide power to the device (and program the software when required). Figure 11 also shows the PyCase enclosure bought for the device to create a ready-to-deploy product.
# Combined Access Barring Scheme for IoT Devices Using Bayesian Estimation
## 2 Random Access Procedure in LTE-A
### Extended Access Barring  
In EAB, each IoT device is assigned a class number ranging from 0 to 9 and the devices affiliated with only one class can transmit while the other classes are barred. The eNodeB broadcasts a 10-bit bitmap with all bits equal to 0 except one. The index of the bit value 1 indicates the the class number which is unbarred. For example bitmap 0010000000 represents that class number 2 is unbarred while all the other classes are barred. So by allowing only one class among 10 to transmit, the number of collisions can be reduced. Even if the devices of only one class is allowed to transmit, their may be large number of devices within a single class and that also can lead to collisions and then congestion. For coping this issue, EAB also comes with discontinuous transmission (DTX) in which all the devices within one unbarred class do not transmit simultaneously. Each IoT device has its own paging frame (PF) and within paging frame its own paging occasion (PO). Each IoT device wakes up at its own PO within its own PF and then checks from bitmap whether its class is barred or not. It transmits if its class is unbarred otherwise it sleeps again. So there is quite less chance that two devices within one class have same PF and PO, and hence collision can be controlled. However, the devices have to wait longer to transmit the message. Please note that additional access delay is experienced by the IoT devices due to DTX mode in EAB. Thus in EAB, collisions are reduced at the cost of increased access delay while in ACB, the access delay is reduced at the cost of collisions. Thus we need to have a barring mechanism that reduces delay as well as collisions. So we propose combined access barring (CAB) which is given in the next section.