esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Therefore, feature selection is a significant method of improving classification accuracy, reduce the unnecessary or redundant input features and contributing a better understanding of important features and the underlying process that generated the datasets. It can be said that there is a need to overcome the other problems such as feature redundancy, high-dimensional features, and overfitting. Moreover, another problem for IDS is dealing with imbalanced datasets, such as the majority of the samples belong to Probe and denial of service (DoS) attacks, while very few samples belong to user-to-root (U2R) or remote-to-user (R2L) attacks due to this problem, often the classifier obtains the redundant and biased samples, while in real world the minority attacks are usually more dangerous than majority attacks. Furthermore, a single IDS can examine enormous amounts of information containing redundant and erroneous features, while at this stage, IDS encounters some difficulties such as noise and increased classifier time to tackle aforementioned problems. An effective IDS is needed to reduce false alarm rates and at the same time, it is also required to be effective in identifying the attacks as well as manage high detection rates and reduce the time. In order to overcome aforementioned problems, we employ a feature selection algorithm that can give an estimate about discriminative features for the IDS classification. In this paper, we are proposing a data mining algorithm called (SPLR) with \(\ell 1\)-Regularization for misuse intrusion detection system problem. The SPLR is a recently developed technique used for classification; the SPLR has been widely employed in several applications such as computer vision, pattern recognition problem, signal processing [9] and now in the intrusion detection system. The major contributions of this work are summarized as,1. We employ a unique framework, the sparse logistic regression (SPLR) for an intrusion detection system (IDS). The SPLR has not been applied by any other researcher in the domain of IDS, as per our knowledge. 2. The SPLR reduces the cost function for IDS classification with a sparsity constraint. 3. Regularization through SPLR, feature selection has been mapped into penalty term of sparsity optimization in order to select more effective and interpretable features for IDS classification. 4. The SPLR has shown different characteristics that are exceptionally suitable for IDS such as high classification accuracy (detection rate [DR]) and training time to build a model and average training time per sample etc.
## 2 Methods
### Image Enhancement Algorithms
The modification of the HE method that also supports its potential for image enhancement is the contrast limited adaptive histogram equalization (CLAHE). The method limits the contrast amplification by histogram clipping at a specified value before computing the CDF. Therefore, the resulting output image from this method as given in Figure 3b is not brightened excessively because the peaks that are present in the input image are still clearly visible in the output image. Images captured in a hazy environment have high-intensity pixels in the background for each channel, either in monochrome or RGB images, whereas the object is mainly disturbed by shadows, streaks, etc., causing it to have low intensity. The goal of haze removal in CV is given in Equation (4), in which \(I(i)\) is the image intensity, \(J(i)\) is the scene radiance, \(A\) is the global atmospheric light, and \(t(i)\) is the light portion that is not dispersed and reaches the sensor [74]. The direct attenuation of \(J(i)t(i)\) decays in the air as a multiplicative distortion of the scene radiance, whereas the air light term from \(A(1-t(i))\) is the additive of the scene radiance that shifts the image colors. \[I(i)=J(i)t(i)+A(1-t(i)) \tag{4}\]  
[PERSON] et al. [74] proposed a modification of Equation (4) that is based on statistics of haze-free images. The concept is defined as haze removal using dark channel prior (HRDC) and is expressed by Equation (5) below. The transmission of \(t(i)\) is restricted by the lower bound \(t_{0}\) so a small amount of haze is preserved in the dense haze region. \[J(i)=\frac{I(i)-A}{\max(t(i),t_{0})}+A \tag{5}\]  
Previous studies conducted by [PERSON] et al. [73] discovered that low-lighting video or image enhancement has similarities with dehazing or haze removal operation. Equation (4) is modified by [PERSON] et al. [73] following the haze removal procedure that is started by inverting the low-lighting image as \(R(i)\). The global atmospheric light \(A\) is selected from the highest intensity pixel from the input image \(I(i)\). A multiplier \(P(x)\) is introduced to adjust \(t(i)\) because the brightness of the object is still low when \(t(i)\) is being applied directly to the low-light image. The multiplier \(P(x)\) is set following the assigned \(t(i)\) value to avoid over- or under-enhancement of the input image. This procedure is expressed in Equation (6) and is defined as haze removal with an inverted operation (HRIO) in this study. \[J(i)=\frac{R(i)-A}{P(i)t(i)}+A \tag{6}\]  
A clear difference between the dehazing algorithms expressed in Equations (5) and (6) can be observed in Figure 3d,e. The output image from the HRDC algorithm is almost similar to that of CLAHE, resulting in a more natural image without oversaturated colors. Furthermore, the improvement of white level intensity is more visible in the HRIO method such that the separation between the background and the object is more obvious.
# Review of Satellite Remote Sensing of Carbon Dioxide Inversion and Assimilation
## 4 Carbon Flux Data Assimilation and Inversion Studies
### Framework and Techniques for Assimilation of Atmospheric CO\({}_{2}\)
#### 4.3.2 Atmospheric Transport Model  
The atmospheric transport model serves as the \"forward\" model in the atmospheric CO\({}_{2}\)-assimilation system, connecting atmospheric CO\({}_{2}\) concentrations and land surface fluxes. It simulates the distribution of atmospheric CO\({}_{2}\) concentrations by using land surface carbon fluxes and meteorological driving data as inputs. The model utilizes global atmospheric chemical transport models to simulate atmospheric CO\({}_{2}\) and compare it with observational data. This research helps understand the spatiotemporal variations of CO\({}_{2}\) in different regions and reveals the changes in carbon emissions and land ecosystem carbon sources and sinks in different regions globally. It is an essential component of the CO\({}_{2}\)-assimilation system.  
The atmospheric chemical transport model can be divided into mesoscale atmospheric chemical transport models and global atmospheric chemical transport models. Mesoscale atmospheric chemical transport models are commonly used to simulate the distribution of atmospheric CO\({}_{2}\) concentrations over a short period in a specific region. Compared to global atmospheric chemical transport models, they have higher resolution and often include nested regional models with resolutions ranging from a few kilometers to tens of kilometers. Global atmospheric chemical transport models, on the other hand, are used to simulate the distribution of atmospheric CO\({}_{2}\) concentrations over a longer period globally, with lower resolutions generally between 1\({}^{\circ}\) and 3\({}^{\circ}\). Some commonly used mesoscale atmospheric chemical transport models for simulating regional atmospheric CO\({}_{2}\) concentrations include WRF-Ghem [164], CMAQ [165], CHIMERE [166], and REMO [167]. For simulating global atmospheric CO\({}_{2}\) concentrations, commonly used global atmospheric chemical transport models include GEOS-Chem [168], MOZART [169], TM [170], and LMDZ [171]. Unlike  
Figure 23: Schematic diagram of the general data-assimilation system.  
observations, atmospheric transport models can provide globally comprehensive coverage of atmospheric CO\({}_{2}\) concentrations with higher spatiotemporal resolutions.  
However, due to the resolution of atmospheric chemical transport models and the fact that the values are averaged over a certain period and space, combined with the uncertainties in land surface carbon fluxes and the \"chaotic\" nature of the atmospheric system, long-term predictions of atmospheric CO\({}_{2}\) are challenging, and the uncertainties cannot be eliminated regardless of how accurately the physical and chemical processes of CO\({}_{2}\) transport are simulated by the models.
## 1 Introduction
### Evolution of TA Systems  
Different families of type I TA systems have been described. Some type I loci are found only in a limited number of bacteria, while others are found in different phyla. Up to 26 type I loci have been predicted in the _Escherichia coli_ O157: H7 Sakai strain [20]. In contrast to type II and type III systems that spread by horizontal gene transfer, type I loci seem to be inherited vertically and arise by duplication in specific lineages [20]. Although type III systems appear to be prone to horizontal gene transfer, they are less abundant than type II systems [29]. They have been grouped into three families based on toxin sequence similarity, and up to six type III loci have been found in one species [29]. Type III loci are found mainly in Firmicutes and Fusobacteria and to a lesser extend in Proteobacteria, Archaea and on phages [29]. As type III system identification is based on three-dimensional structure similarity so far, further bioinformatics approaches will probably reveal a higher number and diversity of type III systems. As type IV and V systems were discovered recently, their abundance, dissemination and evolution have not been investigated yet.  
Type II TA systems are probably the most abundant and the best described class of TA systems. Currently, type II toxins are classified in 12 super-families based on amino acid sequences and three-dimensional structure similarities [7]. Type II antitokins form 20 super-families and are based on the same criteria [7]. It was thought for some time that each toxin super-family is associated with a specific antitoxin super-family. However, bioinformatics and experimental studies showed that \"mixing and matching\" occurs, indicating that type II systems have been assembled from these toxin and antitoxin super-families at different occasions by _in situ_ displacement, as proposed by [PERSON] and [PERSON] [7, 30, 31, 32, 33, 34]. In addition, shuffling between TA types also occurs. The type III ToxN toxin is an endoribonuclease that shares the same fold as the CcdB/MazF type II super-family [21]. Type I toxins are generally small inner membrane proteins that disrupt the proton motive force (PMF), such as the type V GhoT toxin [18, 35, 36, 37]. Strikingly, the type I toxin SymE, which shows endoribonuclease activity, shares the same fold as the MazE type II antitoxin super-family [38], and the type IV CbeA antitoxin presents a RelE fold [39]. Therefore, such evolutionary processes offer a wide range of possibilities for interactions between toxin and antitoxin molecules, as well as with cellular components.
## 3 Results
### Influence of Surface Roughness on Cavitation Erosion  
The roughness of the material surface has attracted the interest of many researchers [20], which is also one of the important factors affecting the cavitation erosion rate. The high roughness of the metallic surface increases the pressure loss along the flow path, which will suppress the strength of the cavitation flow. As discussed above, the brass has good anti-cavitation ability, and different machining methods of the brass will induce different surface topographies. The brass sample was machined by milling and grinding, respectively, ensuring that the surface roughness range from 0.2 to 6.3 \(\upmu\)m. Then, the brass sample was fixed in the experimental section, and two hours later, the brass sample was cleaned in an ultrasonic bath for 5 min followed by air drying. The dried sample was weighed five times with an electronic analytical balance and the average value was taken as the weight loss of metallic materials. The surface roughness of the brass sample machined by milling was greater than the grinding method. Figure 9a shows the change in the weight loss of the brass with increasing acting time, which was machined by milling and grinding. It was found that with the increasing action time, both the weight loss of the brass sample machined by milling and grinding increased, and the weight loss of the milling specimens was slightly lower than that of grinding. Figure 9b presents the variation in the cavitation erosion rate of the milling and grinding specimens with increasing acting time. In the stable period of cavitation erosion, the cavitation erosion rate of the milling specimen was lower than that of the grinding specimen, because the roughness of the brass machined by milling  
Figure 8: The developmental stage of the brass sample under increasing acting time.  
Figure 7: (**a**) The cumulative weight loss curves of brass samples with increasing acting time. (**b**) Curves of the cavitation erosion of brass samples with increasing action time.  
was greater than that by grinding. The rough surface could reduce the average velocity of fluid, increase the pressure loss along the flow path, and suppress the cavitation strength.
# Parametric Study on the Buckling of Unbraced Steel Frames under Fire Situation
## 2 Materials and Methods  
This study analyzed 9 steel frames compromising regular and irregular geometries that were firstly designed at room temperature. At this step, several types of geometry were investigated for the irregular ones, with horizontal and vertical spans of different heights between stories and with cantilevered stories. It is assumed that frames have pinned or fixed supports with rigid connections between beams and columns that remained rigid during the evolution of the temperature. A more appropriate characterization of the rigidity of the connections during a fire is out of the-scope of the present study. Then, the calculation of the buckling length of the columns in the case of fire comprised 12 different fire cases. These buckling lengths were then used to calculate the fire resistance using the simplified design methods of the EN1993-1-2 [15]. Finally, the results obtained with the EN1993-1-2 were compared with those yielded by advanced design methods, i.e., the finite element method (FEM). In the FEM calculations, the members were divided into ten beam finite elements, and no heat transfer between compartments was considered; that is, only the compartment elements that are under fire conditions were subject to the action of temperature while the others remained at normal temperature. To allow the direct comparison with the simplified design methods, the variation in temperature over the element was not considered, thus it was constant along the cross-section and the member. Finally, the thermal expansion was only accounted for in the calculation of the fire resistance of the frames.  
It can be seen in Figure 1 a summary of all case studies on this work. It is noteworthy that two analyses were conducted in determining the buckling factor for each scenario: (1) considering the fire at each story individually; (2) considering the fire on two stories simultaneously; (3) considering frames with pinned and fixed supports; and finally, (4) considering braced and unbraced frames. Frame geometries are detailed in Figure 2.  
Figure 1: Cases considered in this study.
# Tracking of Moving Targets Through Asynchronous Measures
## 2 Asynchronous Tracking Algorithm
 \tag{8}\]  
The linear estimator \(\mathbf{\hat{q}}_{1}\), obtained by zeroing the derivative of the quadratic form \(\text{Y}(\mathbf{q})\) with respect to the vector \(\mathbf{q}\)[14], is given by  
\[\mathbf{\hat{q}}_{1}=\mathbf{\hat{q}}_{0}+\left(\frac{\hat{\rho}-|\mathbf{ \hat{r}}-\mathbf{r}_{\text{s}}|}{\sigma_{\rho}^{2}}\right)\cdot\left(\mathbf{ C}_{0}^{-1}+\frac{\mathbf{L}\cdot\mathbf{L}^{T}}{\sigma_{\rho}^{2}}\right)^{-1} \cdot\mathbf{L} \tag{9}\]  
and its corresponding covariance matrix \(\mathbf{C}_{1}\)[14] is given by  
\[\mathbf{C}_{1}=\left(\mathbf{C}_{0}^{-1}+\frac{\mathbf{L}\cdot\mathbf{L}^{T}} {\sigma_{\rho}^{2}}\right)^{-1} \tag{10}\]  
We remark that, as the best linear estimator is obtained by the introduction of a single new measure, its application during tracking can be implemented without any limitation on the order and the timestamps of the ranging measurements coming from the set of the available anchors; the measurements are handled serially, exactly when they become available at the target or processing unit.
###### Abstract  
The \({}^{3}\)Cat-3/MOTS (3: Cube, Cat: Catalunya, 3: 3 rd CubeSat mission/Missio Observacio Terra Satel lit) mission is a joint initiative between the Institut Cartografic i Geologic de Catalunya (ICGC) and the Universitat Politecnica de Catalunya-BarcelonaTech (UPC) to foster innovative Earth Observation (EO) techniques based on data fusion of Global Navigation Satellite Systems Reflectometry (GNSS-R) and optical payloads. It is based on a 6U CubeSat platform, roughly a 10 cm \(\times\) 20 cm \(\times\) 30 cm parallelepiped. Since 2012, there has been a fast growing trend to use small satellites, especially nanosatellites, and in particular those following the CubeSat form factor. Small satellites possess intrinsic advantages over larger platforms in terms of cost, flexibility, and scalability, and may also enable constellations, trains, federations, or fractionated satellites or payloads based on a large number of individual satellites at an affordable cost. This work summarizes the mission analysis of \({}^{3}\)Cat-3/MOTS, including its payload results, power budget (PB), thermal budget (TB), and data budget (DB). This mission analysis is addressed to transform EO data into territorial climate variables (soil moisture and land cover change) at the best possible achievable spatio-temporal resolution.  
GNSS-R; soil moisture; downscaling; mission analysis; earth observation +
Footnote †: journal: Sensors
# On the Use of Bright Scatterers for Monitoring Doppler, Dual-Polarization Weather Radars
## 1 Introduction
These include: Test signals inserted at various points of the radar chains by laboratory test equipment during preventive maintenance; signals transmitted by the radar and measured by an external receiver to check the amplitude and shape of the transmitted radar pulse (e.g., [8]), signals from the Sun during operational weather scanning for the daily monitoring of the pointing accuracy of the antenna [9] and of the relative calibration of the receiver [10; 11; 12]; signals from the Sun with maximized Signal-to-Noise ratios (offline Sun-tracking on demand) for the absolute calibration of the receiver [13]; ground clutter maps in clear sky conditions (for instance, percentile maps). In this technical note, we propose complementing the above-mentioned multi-sensor and multi-signal monitoring approach with a wealth of spectral and polarimetric information associated with an individual scatterer that we have defined as a \"Bright Scatterer\" (BS). In order to be \"bright\", a dominant point target, with deterministic backscattering properties, should be present in the radar bin, which has to be located close to the radar and hit by the antenna beam axis. The particular Doppler and polarimetric signatures of the BS emerge in one single, high-range-resolution radar bin: In the case of the Swiss radar, the sampling volume, which depends on the antenna Half Power Beam Width (HPBW) and the pulse length, is 1\({}^{\circ}\)\(\times\) 1\({}^{\circ}\)\(\times\) 83.33 m. Hence, we present detailed information regarding the bin that contains the BS, which is a high metallic tower that has a huge radar cross section (RCS). It is located on Cimetta at an altitude of 1650 m and has an 18 km range: the corresponding (HPBW) azimuthal (and elevation) angular resolution is 314 m. The idea of using return signals from a radio tower is not new. In Reference [14], for instance, the focus was on the stability of the radar reflectivity, while in Reference [15] it was on the differential reflectivity. In this note, several other features are analyzed in detail: the mean radial velocity and the spectrum width which complement radar reflectivity. As far as polarimetric information is concerned, in addition to differential reflectivity, the co-polar correlation coefficient and the differential phase shift are presented. A geographic description of the BS location and the radar site is given in Section 2, together with detailed information regarding the radar hardware and scan program. Section 3 presents and describes the results, which are based on the analysis of five clear sky days (1440 samples acquired with a 5-min sampling time): It is shown that the spectral and polarimetric signatures of the BS are impressive, in terms of stability and reproducibility. The findings and their implications are discussed in Section 4, which also presents future research directions. The conclusions are presented in Section 5.
## 3 Method
### Word Distribution Feature Extraction  
This section describes how word distribution features are extracted based on meaningful word segmentation, which primarily focuses on the detection of dictionary-based AGDs. Since dictionary-based DGAs use English words to randomly generate the AGDs, we adopt the same approach as [PERSON] et al. [20], by restoring the component words of a domain name through meaningful word segmentation to improve the detection effect of such AGDs. For English word segmentation methods, Wordinija [46] and WordSegment [47] are commonly used, and based on our experiments, we believe that Wordinja performs better in this scenario.  
The process of word distribution feature extraction is shown in Figure 3. We first use Wordinja to obtain the word sequence of the domain name and use the ELMo [39] model to extract a 1024-dimensional context-sensitive word embedding feature vector for each word. For benign domain names, the words contained in a domain name usually have a certain semantic correlation, while for dictionary-based AGDs constructed by random concatenate words, the semantic correlation is relatively low. Therefore, we use  
Figure 2: Structure of the AGD detection method.  
the correlation between words to detect the dictionary-based AGDs. Since the output of the ELMo model is a 1024-dimensional feature vector, we calculate the standard deviation (Std) of each dimension of the word features, which obtains 1024-dimensional Std features to measure the correlations. In addition, we extract the mean value of each dimension of word features to represent the distribution of the features, which is the same as the Koh method [20]. Since the original ELMo model is trained based on natural languages, it is difficult to fully adapt to the words in domain names, but if the ELMo model is iterated, it will result in a very large training time cost. Therefore, we add a fully-connected layer after ELMo to transform the output features of ELMo. After that, the mean features and Std features are calculated and concatenated to obtain 2048-dimensional distribution features. Last, through a fully-connected layer, the features are transformed into 128-dimensional feature vectors, which are regarded as the final word distribution features.