esa-sceva/satcom-chunk-collection huggingface dataset:
# Can Biochar Improve the Sustainability of Animal Production?
## 2 Summary of Biochar-Production Techniques
### Production Processes  
#### 2.1.1 Pyrolysis  
The name for this high-temperature process is derived from the Ancient Greek terms \(\pi\)i\(\varphi\) (p\(\dot{\alpha}\)r, \"fire\") and \(\lambda\)\(\omega\)\(\alpha\)\(\alpha\) (lisis, \"loosing\"). The high temperature treatment removes volatile matter in the form of high-energy condensable (bio-oil) and non-condensable gases (CO\({}_{2}\), CO, H\({}_{2}\), and simple hydrocarbons). Pyrolysis involves the thermal treatment of a carbonaceous material in the absence of oxygen [14]. Typically, the raw material is heated at temperatures between 400 and 900 \({}^{\circ}\)C under a continuous flow of nitrogen or helium.  
Depending on the heating rate, pyrolysis can be slow, fast, or flash, although slow pyrolysis is preferred for biochar production [15]. As the biomass is subjected to anoxic heating, it undergoes dehydration followed by the decomposition and volatilization of volatile compounds resulting in a porous carbon-rich solid mass called biochar [16; 17]. Excellent reviews on the pyrolysis processes and the mechanisms are available in the literature [18; 19].  
Several types of biomass have been explored for pyrolysis to synthesize biochars for applications in water treatment, soil conditioning, and catalysis. A few of the biochars synthesized from animal wastes are discussed here. [PERSON] et al. [20] employed slow pyrolysis between 300 and 800 \({}^{\circ}\)C for the synthesis of biochar from swine manure. Subsequent analysis of the biochars indicated that the pH of the biochar increased with an increase in the carbonizing temperature.  
In addition, a temperature of 700 \({}^{\circ}\)C was found to be the optimum for the development of mesoporous porosity in biochar. Swine manure was also used as a substrate for the synthesis of biochars for the removal of copper from wastewater. [PERSON] et al. [21] pyrolyzed fresh and composted manure at 400 and 700 \({}^{\circ}\)C and investigated the adsorption of copper in a batch system. They observed that the adsorption was endothermic with adsorption capacities between 9.15 and 21.8 mg\({}_{\text{Cu(II)}}\) g\({}^{-1}\) for fresh and composted biochars.  
[PERSON] et al. [22] employed the slow pyrolysis of cow manure at 300-700 \({}^{\circ}\)C (2 h) to prepare biochars. Surface analysis of the biochars suggested an increase in surface basicity with temperature that suggested their suitability in conditioning acidic soils. Biochars from cow manure were synthesized by [PERSON] et al. [23] using temperatures of 300, 500, and 700 \({}^{\circ}\)C. Based on their results, the porosity of biochars increased with an increase in temperatures. When tested for the adsorption of tetracycline from water, the biochars chemisorbed 15-26.7 mg g\({}^{-1}\). [PERSON] et al. [24] employed poultry litter as a precursor for the synthesis of high-surface-area (>3000 m\({}^{2}\) g\({}^{-1}\)) biochar equipped with hierarchical porosity that was used as a supercapacitor electrode.
## Materials and Methods
### Module Description
#### 2.3.2 Tile Stitching  
DG imagery is delivered in bundles, each containing adjacent tiles. To form a single image covering the area of interest, the tiles are stitched together at this second step. Both R ('mosaic' function of 'raster' package) and GDAL (gdalwarp function) have algorithmic implementations that are required to perform this step. The results obtained from these algorithms are identical, and this also holds for the proprietary software results (i.e., ERDAS Imagine), with which they have been compared. In the current implementation, GDAL is used. RapidEye scenes are not undergoing this step as the images, either in the 2A or 3 processing level, are delivered as a single image covering the whole scene.
# Unsupervised Deep Anomaly Detection for Industrial Multivariate Time Series Data
## 3 Materials and Methods  
### Problem Formulation  
Industrial multivariate time series data involve multiple variables. In this research, multivariate time series sampled at regular intervals are denoted as \(X=\{x_{1},\cdots,x_{T}\}\in\mathbb{R}^{T\times k}\), where \(T\) represents the maximum length of timestamps and \(k\) represents the number of variables. Each time observation \(x_{t}\in\mathbb{R}^{k}\) represents data from multiple variables collected at timestamp \(t\), and each \(x_{t}\) is a k-dimensional vector. Multivariate time series anomaly detection is utilized to identify whether a time observation point is anomalous. When given a time series \(X\), the corresponding anomaly label sequence is denoted as \(Y=\{y_{1},\cdots,y_{T}\}\), where \(y_{t}\in\{0,1\}\). A label of 0 indicates that the time point at timestamp \(t\) is normal, while a label of 1 indicates an anomaly at timestamp \(t\).
# WiFi FTM, UWB and Cellular-Based Radio Fusion for Indoor Positioning
###### Abstract  
High-precision indoor localisation is becoming a necessity with novel location-based services that are emerging around 5G. The deployment of high-precision indoor location technologies is usually costly due to the high density of reference points. In this work, we propose the opportunistic fusion of several different technologies, such as ultra-wide band (UWB) and WiFi fine-time measurement (FTM), in order to improve the performance of location. We also propose the use of fusion with cellular networks, such as LTE, to complement these technologies where the number of reference points is under-determined, increasing the availability of the location service. Maximum likelihood estimation (MLE) is presented to weight the different reference points to eliminate outliers, and several searching methods are presented and evaluated for the localisation algorithm. An experimental setup is used to validate the presented system, using UWB and WiFi FTM due to their incorporation in the latest flagship smartphones. It is shown that the use of multi-technology fusion in trilateration algorithm remarkably optimises the precise coverage area. In addition, it reduces the positioning error by over-determining the positioning problem. This technique reduces the costs of any network deployment oriented to location services, since a reduced number of reference points from each technology is required.  
indoor positioning; fusion technologies; UWB; WiFi fine time measurement; LTE; maximum likelihood estimator +
Footnote †: journal: _Sensors_  
1
## 2 Background
### Dynamic Identity and C2 Systems  
Once a device has an established identity, that device may elect to join a C2 network, either autonomously or as a result of a user-interaction. In low-security scenarios a C2 server may be configured to permit joining of any devices without further establishment of their credentials and as such, a _simple join_ operation may be conducted.  
Since any device joining the system may have just generated (or regenerated) its identity it is not possible for the C2 server (or a human operator of that C2 server) to ascertain the physical device to which that identity pertains. For low security systems (those where there are no sensitivities to the data and where the system itself is not controlling critical operations), this may be regarded as acceptable. However, for systems where there is the risk of an attacker injecting false data into the system, such an approach cannot be adopted.  
The behaviour governing what types of join can be permitted (either globally, or on a device type basis) is determined in software by the C2 system.  
The risk from a simple join is that an attacker could stage an attack against the system by intercepting the initial registration request from a device and then registering their own device and joining this instead. Since in the context of a simple join there is no validation of the physical identity of the device that has just requested to join, an attack may register their own device in place of the real device.
# Salt-Fog Corrosion Behavior of GCr15 Steels Treated by Ultrasonic Strengthening Grinding Process
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Experimental**} & \multicolumn{6}{c}{**Element/wt\%**} \\ \cline{2-7}  & **C** & **O** & **Fe** & **Cr** & **Cl** & **Ca** \\ \hline Polished & 12.60 & 22.11 & 63.92 & 0.80 & 0.57 & - \\ \hline USP & 13.93 & 25.23 & 58.40 & 0.92 & 1.52 & - \\ \hline DUSGP & 12.71 & 32.62 & 51.37 & 0.58 & 1.78 & 0.94 \\ \hline WUSGP & 12.87 & 32.50 & 48.25 & 1.03 & 4.57 & 0.78 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Element atomic fraction of corrosion product in crack area of samples. Figure 11: Schematic diagram of salt spray corrosion reaction of samples for initial stage (**a**) and reaction stage (**b**). the sample surface. The thickness of the corrosion layer becomes thicker over time, which reduces the corrosion rate of Cl\({}^{-}\) to the matrix material in the later stage of corrosion. In Sample USGP, a dense strengthening layer and film are formed on the surface after surface treatment. Thus, the corrosion rate of Cl\({}^{-}\) was reduced in the initial stage of corrosion. The corrosion mainly occurs in the weak regions of the substrate and in the active anode area will trigger a redox reaction to produce Fe(OH)\({}_{2}\) film. With the continuous erosion of Cl\({}^{-}\), the migration rate of Cl\({}^{-}\) deoxygenation increases, which destroys the corrosion layer. Then, the electrolyte slowly flows into the matrix to corrode at the damaged regions, but the corrosion rate is greatly reduced due to the effect of the dense strengthening layer and the passivation film. In WUSGP, an obvious better performance of corrosion resistance was obtained and can be interpreted as follows: the surface matrix undergoes plastic deformation after surface treatment, resulting in an increase in the martensite content with different shapes, such as needle-like and lath-like, which makes the surface structure denser and hinders Cl\({}^{-}\) from eroding the matrix. Moreover, the high dislocation density and small grain size can significantly improve corrosion resistance.
## 3 Numerical Experiments and Discussions
### Vision Pseudorange with Calibration  
The mean of VP error with different LPE is shown in Figure 6. As shown in Figure 6a, there is a nonzero value range from 4.7 m to 0.2 m in the mean of VP error without calibration. Although the landmark positions are very accurate and LPE is centimeter-level, LPE still causes VP error that cannot be ignored and may raise a false alarm for integrity monitoring. Besides, as the vision system is an angling system, the error caused by LPE approximate linear increases as the length of the line-of-sight increases. As shown in Figure 6b, with the calibration algorithm, the mean of the calibrated VP error is less than 1 m during the simulation. The results illustrate that our calibration algorithm reduces the mean of VP errors significantly. Theoretically, the mean of the calibrated VP error is 0 as shown in Equation (14), while the error cannot be completely reduced due to the linearization error.  
The standard deviation of VP error with different image resolution and DE is shown in Figure 7. The results show that higher resolution, shorter line-of-sight and less DE can generate more accurate  
Figure 5: The positions of the landmarks.  
Figure 6: The mean of VP error with different LPE. (**a**) The mean of VP error without calibration; (**b**) The mean of calibrated VP error.  
vision pseudoranges, while the error of vision system is still much larger than GPS system. Such a situation may be improved as feature matching accuracy has been increasing over the years [31].  
Although the time-variant error in the vision measurements cannot be protected against directly, we can mitigate its impact by increasing the image resolution and improving the accuracy or robustness of the feature detection algorithm, which is also an important topic in the computer vision community. Furthermore, a consistency check method for the standalone vision system can be applied to reduce the impact of vision fault [12]. The image processing for aviation applications is worth being investigated in our future work.
## 2 Details and Performance Analysis of QODELFA  
### Main Procedures of QODELFA  
The QODELFA proposed in this paper is basically a unique combination of the DE algorithm and LF perturbation. Furthermore, the concept of QOBL is applied to generate the initial population of the combined DELFA. The main operations and steps of QODELFA are demonstrated in detail as follows.
## 3 Proposed Model
### Background  
[PERSON] et al. [58] described a fast CNN architecture inspired by AlexNet [7] model. They have shown that reducing the number of kernels in convolutional layers in their architecture does not impact the output performance in comparison to AlexNet. Figure 1 illustrates the aforementioned fast CNN architecture. This architecture is comprised of eight layers, including five convolutional layers (Conv. Layers) and three fully connected ones (FC Layers). The input image of this architecture is \(224\times 224\times 3\) and the structure of the five convolutional layers are as follows: (i) 64 kernels of size \(11\times 11\times 3\) in the first layer (Conv. 1 in Figure 1), (ii) 256 kernels of size \(5\times 5\times 64\) in the second layer (Conv. 2), and (iii) 256 kernels of size \(3\times 3\times 256\) in the next three layers (Conv. 3, Conv. 4, and Conv. 5). Both of the first two fully connected layers has 4096 neurons (FC6 and FC7). The last fully connected layer, referred to as the softmax layer, has an output of 1000 dimensions (FC8).  
Figure 1: Chatfield’s fast convolutional neural network (CNN) architecture. Conv: convolutional layer; FC: fully connected layer; st: stride; pad: padding; LRN: local response normalisation.
# Fast Fourier Asymmetric Context Aggregation Network:
## 3 Proposed Method  
### Overview  
Our method takes as input a design sketch \(S\), texture patch \(T\), and clothing region mask \(M_{c}\), with the goal of synthesizing a clothing image \(C_{\mathrm{gen}}\) whose semantics align with both \(S\) and \(T\). The sources and combination strategies of these input components will be elaborated on in subsequent sections.  
The primary challenge of the task lies in ensuring that the synthesized clothing image not only conforms to the appearance pattern of the sketch but also propagates texture content effectively and coherently across the clothing regions. This imposes stringent requirements on the alignment of global information within the image. Moreover, we consider the interplay between texture patch size and synthesis performance. For instance, larger texture patches provide richer texture information but require more effort from the designer to obtain. Consequently, a synthesis strategy that is more robust to variations in texture patch size can significantly enhance the practical applicability of our method.  
To address these challenges, we propose a frequency-domain-based content inference block (FF-CDRB) that leverages frequency-domain characteristics to synthesize fine-grained texture content. Furthermore, we introduce the ACAM, which, during the decoding phase, utilizes heterogeneous features to construct a more comprehensive global relationship. The ACAM enhances information through texture patch, enabling better exploitation of the limited guidance provided by the texture patches. This approach not only improves the consistency between the appearance structure and texture content but also enhances the model's robustness to variations in texture patch size.  
Figure 2 shows the overall framework of FCAN. In this chapter, we will provide a detailed introduction to the workflow of FCAN.  
Figure 2: The overall framework of FCAN. The FCAN workflow consists of three stages: the encoding stage, content inference stage, and context aggregation and decoding stage. (**a**) **Encoding Stage:** In the encoding stage, FCAN utilizes an encoder to encode the input information into multi-scale features.  
(b) Content Inference Stage: In the content inference stage, FCAN employs concatenated FF-CDRB blocks to perform content inference and initial context modeling on the deepest features obtained in the encoding stage. (c) Context Aggregation and Decoding Stage: In this stage, the proposed ACAM is used to achieve global context aggregation with heterogeneous features from different stages. The enhanced features are then fused into the corresponding decoding steps, ultimately generating the clothing image.
# A Data Driven Approach for Raw Material Terminology
## 2 Available (Terminological) Resources
### General Purpose Morphological Dictionaries  
Serbian has an extensive system of inflection and a complex agreement system that makes extraction of terminology more complicated, and thus the use of general purpose morphological dictionaries is indispensable for every lexicographic task [32].  
An important lexical resource used for morphological analysis and extraction are the comprehensive electronic morphological dictionaries for Serbian (SrpMD) of simple- and multi-word units, covering general lexica, proper names, encyclopedic knowledge and terminology from a number of domains [33], with nearly 200.000 lexical entries. SrpMD entries include both a lemma and inflected forms supplied by grammatical information, semantic markers, domain information and relations of several types: derivational, lexical variation, component relations (between single words and terminological phrases).  
For example, lexical entry _'tudar'_ (miner, person engaged in mining, a worker in a mine) contains information related to part of speech: _'N'_ (noun), morphological class _'N2'_, semantic tag _'+Hum'_ (human), domain _'DOM = mining'_. Its inflected forms are: _'tudar'_ (ms1v), _'tudara'_ (mp2v:ms2v:ms4v:mw2v:mw4v), _'tudar'_ (mp4v:ms5v), _'tudari'_ (mp1v:mp5v), _'tudarima'_ (mp3v:mp6v:mp7v), _'tudarom'_ (ms6v), _'tudaru'_ (ms3v:ms7v) where brackets show grammatical information: _'m'_--masculin, _'s'_--singular, _'p'_--plural, _'1-7'_--cases, _'v'_--animate.  
The entry _'tudar'_ is also related to the relational adjective _'tudarski'_, and appears as a component of several terminological phrases, for example, rudar na okresivanju (ripper), rudar na ugliu (collier), rudar-podgradivac (timherman), and so forth.  
Over the past years, more entries related to raw material were added to SrpMD, which initially contained more than 3000 simple-word entries and 2000 multi-word entries from the raw material domain. The number of their morphological forms recorded in this resource is significantly larger. The simple-word forms pertaining to raw material terminology that have been processed and included in SrpMD [34] enabled further extraction of related terminological phrases according to the methodology described in [19]. Namely, for extraction to be effective, it is very important that the domain is relatively well covered with simple domain-specific words.
# Key Action Mechanisms of Intentional Mistuning
## 3 Verification of AMM Results
### Effect of Relative Size of Damping-Coupling  
As was mentioned in Section 2.1, for the intentional mistuning pattern to be effective, the damping must be large as compared to the remaining coupling among the resulting blades of the same type. In order to highlight the importance of this point, the same calculations of the previous section have been repeated but with the material damping reduced to \(\xi=5\times 10^{-4}\); a value comparable to the remaining coupling coefficient \(a_{2}\) (see Figure 4). Figure 7 shows the corresponding vibration amplitudes. It is clearly seen that (as predicted by the AMM results) there is now almost no beneficial effect from the implementation of the intentional alternate mistuning pattern in this case.  
Figure 5: Maximum vibration amplitude for increasing intentional mistuning amplitude for 50 random mistuning patterns. Rotor A, alternate pattern, \(\xi=10^{-2}\).  
Figure 6: Frequency sweep and (inset) blade displacement at the maximum (black line corresponds to the tuned amplitude), for \(Aim=0\%\)**(top)**, \(Aim=5\%\)**(middle)**, and \(Aim=10\%\)**(bottom)**. Rotor A, alternate pattern, \(\xi=10^{-2}\).  
Figure 7: Maximum vibration amplitude for increasing intentional mistuning amplitude for 50 random mistuning patterns. Rotor A, alternate pattern, \(\xi=5\times 10^{-4}\).
# A Network Traffic Abnormal Detection Method: Sketch-Based Profile Evolution
###### Abstract  
Network anomaly detection faces unique challenges from dynamic traffic, including large data volume, few attributes, and human factors that influence it, making it difficult to identify typical behavioral characteristics. To address this, we propose using Sketch-based Profile Evolution (SPE) to detect network traffic anomalies. Firstly, the Traffic Graph (TG) of the network terminal is generated using Sketch to identify abnormal data flow positions. Next, the Convolutional Neural Network and Long Short-Term Memory Network (CNN-LSTM) are used to develop traffic behavior profiles, which are then continuously updated using Evolution to detect behavior pattern changes in real-time data streams. SPE allows for direct processing of raw traffic datasets and continuous detection of constantly updated data streams. In experiments using real network traffic datasets, the SPE algorithm was found to be far more efficient and accurate than PCA and Basic Evolution for outlier detection. It is important to note that the value of \(\varphi\) can affect the results of anomaly detection.  
network traffic; traffic graph; abnormal detection; sketch; evolution Article