esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Analysis of Forest Penetration of L-Band SAR Signal  
### Introduction of L-Band SAR Satellites  
In 1978, the United States launched the first Ocean Satellite (Seasat) with the L-band SAR sensor. Then, Japan launched the JELLS-1 satellite in 1992 and the Advanced Land Observing Satellite (ALOS) in January 2006 with L-band SAR sensors and a maximum positioning accuracy of 10 m. In November 2009, the European Space Agency launched the Soil Moisture and Ocean Salinity Satellite (SMOS), which can emit L-band radiation energy to the ground and provide 9 km spatial resolution products. In January 2015, the United States launched the Active and Passive Soil Moisture Monitoring Satellite (SMAP) with an L-band SAR sensor, which can provide global moisture products with a resolution of 40 km \(\times\) 40 km. In October 2018, Argentina launched the SAOCOM 1A satellite with L-band and a spatial resolution of 10 m \(\times\) 10 m to 100 m \(\times\) 100 m. In January 2022, China launched the Land Probe 1 Group 01 A/B satellite (LT-1A/1B), also known as the L-band differential Interferometric SAR satellite, with spatial resolutions of 3 m \(\times\) 3 m, 6 m \(\times\) 6 m, 12 m \(\times\) 12 m, 20 m \(\times\) 20 m and 30 m \(\times\) 30 m. A summary of the launched satellites with L-band is shown in Table 1.  
\begin{table}
\begin{tabular}{l l l l} \hline
**Serial Number** & **Launch Time** & **Country or Region** & **Satellite Name** \\ \hline
1 & February 1978 & Global Positioning System & NAVSTAR GPS \\ \hline
2 & June 1978 & American Seasat satellite & Seasat \\ \hline
3 & February 1992 & Japan Earth Resource Satellite & JERS-1 \\ \hline
4 & January 2006 & Japan Advanced Land Observing Satellite & ALOS/ALOS-2 \\ \hline
5 & November 2009 & European Space Agency Soil Moisture and Ocean Salinity Satellite & SMOS \\ \hline
6 & January 2015 & United States Active and Passive Soil Moisture Monitoring Satellite & SMAP \\ \hline
7 & October 2018 & Argentine Microwave Observation Satellite 1A & SAOCOM-1A \\ \hline
8 & September 2019 & China Yunhai-1 02 satellite & Yunhai-1 02 \\ \hline
9 & August 2020 & Argentine Microwave Observation Satellite 1B & SAOCOM-1B \\ \hline
10 & January 2022 & China Landexplorer-1 Group 01A satellite & LT-1A \\ \hline
11 & January 2022 & China Landexplorer-1 Group 01 B satellite & LT-1B \\ \hline \end{tabular}
\end{table}
Table 1: SAR satellites with L band in the world.  
Compared with the number of other remote sensing satellites, L-band SAR satellites are very few, and even fewer are in orbit. Thus, it is relatively difficult to obtain L-band data. The emergence of LT-1 has started to change this situation.
# Pan-Cancer Analyses Reveal Genomic Features of FOXM1 Overexpression in Cancer
## 2 Results  
### FOXM1 Expression in Pan-Cancer  
FOXM1 is over-expressed in many human cancers [31]. Previous studies of FOXM1 expression in cancer have used different methods, are limited to small sample sizes, and/or have focused on a single or limited number of cancer types. To provide a more comprehensive evaluation of FOXM1 expression in cancer, we compared _FOXM1_ mRNA expression across 32 TCGA cancer types (Table S1) and in TCGA and GTEx normal tissues, using UCSC TOIL to correct for batch effects and to allow for sample merging [27]. _FOXM1_ mRNA expression was increased in all TCGA cancer types compared to GTEx and TCGA normal (Figure 1A). Comparing across cancer types, there was a broad spectrum of _FOXM1_ expression, suggesting that high _FOXM1_-expressing cancers may have unique genetic features that drive increased _FOXM1_ expression (Figure 1A). Furthermore, based on the interquartile range, the spread of _FOXM1_ expression varied in some cancer types more than others; e.g., breast cancer (BRCA) has a wide spread while testicular germ cell tumors (TCGT) have a narrow spread (Figure 1A), which could be due to some cancers consisting of more than one clearly defined subtype and therefore having more genetic diversity. We also compared FOXM1 protein expression across the same TCGA cohorts. Similar to _FOXM1_ mRNA, FOXM1 protein expression varied across tumor types and showed a broad spectrum of expression (Figure 1B). Importantly, FOXM1 mRNA and protein expression were highly correlated in pan-cancer (Figure 1C), suggesting an important role for gene expression regulation in determining FOXM1 protein expression and its functional status in cancer.
## 5 Energy-Efficient Resource Matching Algorithm for D2D Relay Communications
### Two-Dimensional Matching
#### 5.1.2 Matching Algorithm  
In the matching process, D2D TR pairs can propose their most preferred RBs based on the established preference lists. However, it is possible that more than one D2D TR proposes the same RB. To solve this problem, we design a pricing strategy to resolve the conflict, which utilizes the concept of price to represent the matching cost for D2D TRs. The prices of RBs are virtual, without any physical significance, and are set to zero at the beginning. Let \(\hat{\mathcal{F}}=\{\hat{F}_{1},\cdots,\hat{F}_{cs},\cdots,\hat{F}_{C}\}\), \(\forall c_{s}\in\mathcal{C}\) denote the price set of RBs for the second hop D2D links. The matching algorithm proceeds iteratively. In each iteration, the actual preference of D2D TR \(m\) on \(c_{s}\) is updated as \(V_{m,c_{s}}-\hat{F}_{cs}\), and the preference list \(\hat{\mathcal{O}}_{m}\) should be updated accordingly. Each D2D TR proposes its most preferred RB in \(\hat{\mathcal{O}}_{m}\). If any RB receives more than one request, there are conflicts in the matching process. Then, the conflicting RBs raise their prices with step \(s\) until they receive no more than one request. The algorithm ends if there exist no new requests from D2D TRs. We summarize the two-dimensional matching algorithm in Algorithm 1 and provide an illustration of the preference establishment process and the derived one-to-one matching result for the second hop D2D link in Figure 2; the flowchart is given in Figure 3.  
Figure 2: An illustration of the two-dimensional matching in the second hop D2D link.
# Comparative Study of Fresh and Frozen Broiler Neck Skin Sampled for Process Hygiene Purposes
###### Abstract  
The objective of the study was to determine the effect of freezing broiler neck skin samples before their microbial analysis, compared to freshly examined samples regarding total viable count (TVC) and Enterobacteriaceae count (EC). For this, 300 neck skin samples were taken at a German commercial broiler abattoir and each neck skin sample was cut into two parts. One randomly selected part underwent microbial examination after storage at 4 \({}^{\circ}\)C overnight; the other part was frozen at \(-\)30 \({}^{\circ}\)C for eight weeks before analysis in the same laboratory. Log cfu/g values of TVC and EC were separately compared between the fresh and frozen neck skin samples. A difference up to 0.5 log values was set as acceptable, i.e., fresh and frozen samples with counts that differed by this amount were considered as not different. The differences between the grouped samples of fresh and frozen broiler neck skin regarding both TVC and EC levels were less than 0.5 log values. Thus, it can be assumed that broiler neck skin samples, both fresh and frozen for eight weeks, are suitable for microbiological examination, as the TVC and EC results showed equivalence. Therefore, freezing broiler neck skin samples can be an option to maintain viable bacteria levels in broiler neck skin samples taken for microbiological examination in process control, when freezing and later examination is necessary due to insufficient laboratory capacity for the examination of fresh neck skin samples.  
total viable count; Enterobacteriaceaeae count; equivalence test; microbial comparison +
Footnote †: journal: _Applied Sciences_
## 8 Vulnerability Assessment of NRF to API Injection Tests  
This section provides an in-depth analysis of the security vulnerabilities and potential risks associated with the NRF components of both frameworks, highlighted by API injection tests. In more detail, the 3 GPP standard defines several services offered by the NRF, including NF Service Management, NF Service Discovery, and Access Tokens. The primary focus of our testing is on the NF Service Management, specifically the NRF NF Management (nnrf-nfm) interface. This interface offers various methods for managing subscriptionsand instances of NFs. The imported schema for the nnrf-nfm interface is depicted in Figure 4: the various available services and methods include creating, updating, deleting, and retrieving NF subscriptions and instances.  
Recall that Open5 GS and OAI employ different communication protocols for their Core Network deployment; however, it is important to note that both frameworks do not support TLS. While OAI supports HTTP/1.1 and HTTP/2 SBA communications, Open5 GS only supports HTTP/2. As already pointed out, this disparity in protocol support leads to distinct testing approaches for the two frameworks:  
* OAI Testing Approach: we utilized Burp Suite, which can receive input (via the 5 GC API Parse plugin) in the form of a definition file containing the OpenAPI 3.0 schema of the API interfaces, and it delivers requests in the format prescribed by the 3 GPP standard. The parameters of Burp Suite were configured to maximize the number of tests performed with the imported API schema, including the activation of the \"HTTP smuggler\" and \"ActiveScan++\" extensions to enhance the diversity of tests.
* Open5 GS Testing Approach: Starting with the requests that were exported and executed on OAI, we employed the custom tool 5 GHTTPModifier. This tool takes as input the path containing all the tests exported from Burp Suite and modifies the parameters, configuring them for the new 5G core. It then sends these requests after transforming them into the HTTP/2 format. Using this tool, we were able to execute identical tests as those performed on OAI, facilitating an objective comparison of the security levels between the two frameworks.
## 3 Dynamic-Static Conversion Relationship  
### Methods  
Establishing a dynamic-static conversion relationship is essential for accurately evaluating the rock mechanical characteristics of the formation using logging data and seismic survey data, which in turn provides a reliable petrophysical basis for petroleum engineering design and oil/gas exploration in the region. Based on the results of the P-wave and S-wave velocities, measurements obtained from the dynamic method test and combined with the bulk density were obtained from the weighing method of the rock specimens. The dynamic elastic modulus and dynamic Poisson's ratio can be calculated by using Equations (3) and (4), and the static elastic modulus and static Poisson's ratio were measured by the indoor stress-loading experiments. Through static parameters (stress loading experiment) and dynamic parameters (acoustic testing), using the principle of least squares, a polynomial regression analysis was performed to find the best fitting curve between the dynamic and static parameters of different rocks based on the significance of the polynomial.
## 2 Results
### Peptides bind to Cellular GAGs with Differential Capacity  
Based on the IFT data, four peptides were selected to investigate their binding to cellular GAGs on CHO cells. A fixed dose (0.24 nmol) of TAMRA-labeled peptide (KKAK-2 mer, KKQK-3 mer, KKKQQK-2 mer or CXCL9(74-103)) was added to the cells. Histograms resulting from the flow cytometry analysis are shown in Figure 7A,B for HS\({}^{+}\) and HS\({}^{-}\) CHO cells, respectively. The fluorescence intensity on the HS\({}^{-}\) CHO cells is shifted to the left compared with the HS\({}^{+}\) CHO cells, indicating a decreased binding of the TAMRA-labeled peptides to the HS\({}^{-}\) CHO cells. Remarkably, the binding of the TAMRA-labeled peptides to the HS\({}^{-}\) CHO cells was not completely abolished in comparison with the HS\({}^{+}\) CHO cells. The median fluorescence intensity (MFI) values were calculated by subtracting the MFI of the unstained sample from the MFI of the sample with TAMRA-labeled peptide, per cell type (HS\({}^{+}\) or HS\({}^{-}\) CHO cells) and compared for the different TAMRA-labeled peptides. All four peptides that are shown in Figure 7 bound significantly better to HS\({}^{+}\) CHO cells in comparison with HS\({}^{-}\) CHO cells (data not shown; Wilcoxon test; KAK-2 mer, \(p<0.05\); KKQK-3 mer, \(p<0.001\); KKKQQK-2 mer, \(p<0.0001\); CXCL9(74-103), \(p<0.0001\)). In addition, in order to compare the different peptides, the MFI values of the TAMRA-labeled peptide bound to the HS\({}^{-}\) CHO cells were subtracted from those of the HS\({}^{+}\) CHO cells, per peptide. The resulting values (MFI ([HS\({}^{+}\)]-[HS\({}^{-}\)])) for each peptide are shown in Figure 7C. The binding capacity of CXCL9(74-103) for cellular GAGs was significantly higher (\(p<0.0001\)) compared with that of the three other peptides (KKAK-2 mer, KKQK-3 mer and KKKQK-2 mer). Furthermore, the capacities of both the KKKQQK-2 mer and the KKQK-3 mer for binding to cellular GAGs were significantly higher (\(p<0.05\)) in comparison with the binding capacity of the KAK-2 mer. To summarize, the order of increasing capacity of the TAMRA-labeled peptides for binding to cellular GAGs is: KAKAK-2 mer \(<\) KKQK-3 mer \(<\) KKKQK-2 mer \(<\) CXCL9(74-103).  
Figure 6: Subcloning of pgsB-618 cells results in HS\({}^{+}\) (pgsB-618 clone 19) and HS\({}^{-}\) (pgsB-618 clone 16) cells. The expression of HS on pgsB-618 clone 16 (blue) and pgsB-618 clone 19 (pink) cells was measured using flow cytometry with a primary anti-HS antibody and a secondary PE-labeled antibody. A histogram is shown with the cell count on the y-axis and the fluorescence intensity of the secondary antibody on the x-axis.
# Inertial Parameter Identification in Robotics: A Survey
## 5 The Output Error (OE) and Input Error (IE) Identification Approaches
### Parameter Identification Using an Adaline Neural Network (AdaNN)  
The Adaline (ADAptive LInear NEuron) uses stochastic gradient learning to converge to the IDIM-OLS estimate. The estimator has the following dynamic equation  
\[\mathbf{\hat{\theta}}_{AdaNN}=-\eta\
abla(\mathbf{e}^{\top}\mathbf{e}) \tag{49}\]  
where \(\mathbf{e}\) is the error defined in Equation (18). The parameter estimate \(\mathbf{\hat{\beta}}_{AdaNN}^{k}\) at epoch \(t_{k}\) can hence be expressed recursively as  
\[\mathbf{\hat{\beta}}_{AdaNN}^{k}=\mathbf{\hat{\beta}}_{AdaNN}^{k-1}+\eta\mathbf{\chi}^{k \top}\Big{(}\mathbf{\tau}(t_{k})-\mathbf{Y}^{k}\mathbf{\hat{\beta}}_{AdaNN}^{k-1}\Big{)} \tag{50}\]  
where \(\mathbf{Y}^{k}=\mathbf{Y_{\beta}}(\mathbf{\hat{q}}(t_{k}),\mathbf{\hat{q}}(t_{k}),\mathbf{q}(t_{k}))\) is the observation matrix of the real system at epoch \(t_{k}\), \(\mathbf{\tau}(t_{k})\) is the corresponding torque, and \(\eta\in\mathbb{R}\) is referred to as the learning rate. A single \"neuron\" thus allows full model identification (c.f. Figure 5a). As several _passes_ over the hole dataset may be necessary to achieve proper convergence, the sequence of observations\(\Upsilon^{k}\) and \(\tau(t_{k})\) must be randomly reshuffled to avoid cycles [86]. Applications of the Adaline method to robot identification was investigated in [13,14]. The results suggest that the main advantage of Adaline over IDIM-OLS could be its ability to run online.
## 1 Introduction  
The introduction of fuzzy set theory by [PERSON] [1] triggered rapid development in one of the most commercially successful areas of artificial intelligence. As the theory evolved, many authors contributed to developing arithmetic systems that enabled operations on fuzzy numbers [2, 3, 4, 5]. Probably the best known among these is the LR arithmetic proposed by [PERSON] and [PERSON] [6, 7], largely due to the popularity of its authors. An alternative approach, known as constrained fuzzy arithmetic (CFA), was proposed by [PERSON] [8] and further developed by [PERSON] [9], as well as by [PERSON] and [PERSON] [10]. Another notable contribution is the RMD arithmetic introduced by [PERSON] and [PERSON] [11]. The OFN arithmetic, proposed by [PERSON] et al. [12, 13, 14] and further developed by them and their collaborators [15, 16, 17], is the most relevant for this paper.  
This arithmetic has gained many advocates due to the completeness of its computational system, which constitutes a ring in algebraic terms. Nevertheless, it has also faced skepticism and criticism, mainly focused on two aspects. The first concern involves the occurrence of non-convex numbers in OFN, whose shapes are difficult to interpret for researchers accustomed to classical calculus [18, 19, 20]. The second issue relates to the curvilinear characteristics of rising and falling edges during multiplication operations, which result from changes in the degree of polynomial functions describing these edges [21; 22; 23; 24; 25; 26].  
Another challenge encountered by researchers concerns the feasibility of comparing OFNs. However, the problem of comparing fuzzy numbers is neither new nor exclusive to OFN notation [27; 28]. Research in this domain began in 1977, with contributions from [PERSON] [1], [PERSON] [29], [PERSON] [30], [PERSON] [9], and [PERSON] [31]. In the following decades, [PERSON] [32] and [PERSON] [33] reviewed various comparison methods and proposed the Partial Comparison Method (PCM). Subsequently, [PERSON] and [PERSON] developed a set of principles and axioms designed to structure the methods for comparing fuzzy numbers, thereby allowing for a comprehensive assessment of various fuzzy ranking techniques [34; 35; 36; 37].  
In recent years, novel methodologies for fuzzy number comparison have emerged. For example, [PERSON], [PERSON] et al. [38] proposed utilizing multiple original defuzzification techniques to enhance the process of comparing fuzzy numbers within the framework of Ordered Fuzzy Number (OFN) notation [27].  
This paper addresses the aforementioned challenges and limitations of OFN arithmetic by introducing a redefinition called Vectorial Ordered Fuzzy Numbers (VOFNs),discussed in the next section.
## 3 Methodology  
### Description of Anomaly Detection Methods  
The anomaly detection method for traffic flow data based on prediction and multi-domain feature extraction proposed in this paper consists of two main parts: the construction of the prediction model and error analysis for anomaly detection. The overall framework is shown in Figure 1.  
The construction of the prediction model primarily consists of two modules: the time-domain feature extraction module and the frequency-domain feature extraction module. The time-domain feature extraction module uses a Bidirectional Long Short-Term Memory network (Bi-LSTM) to capture the temporal dependencies in the data and a Graph Attention Network (GAT) to extract spatial dependencies, enabling the learning of both temporal and spatial characteristics in the time domain. The frequency-domain feature extraction module first applies Fast Fourier Transform (FFT) to convert the raw time-domain signal into a frequency-domain signal. Then, a Transformer model is used to separately extract the amplitude and phase features in the frequency domain, facilitating the learning of frequency-domain characteristics. The prediction model is constructed by integrating the features extracted from both the time and frequency domains.  
In this paper, the traffic flow \(X=\left\{x^{1},x^{2},x^{3},\ldots x^{t}\ldots,x^{N}\right\}\in R^{M\times N}\) is taken as an input, where \(x^{t}=\left\{x^{t}_{1},x^{t}_{2},x^{t}_{3},\ldots,x^{t}_{M}\right\}\in R^{M}\) is denoted as the observation measured at time \(t=\left\{1,2,\cdots N\right\}\). The prediction model predicts the traffic flow for the time interval H at each node based on the observations in a historical time window K at that node, so the problem of traffic flow prediction at a particular node can be formulated as follows: \(\left[x^{t_{0}-K+1},x^{t_{0}-K+2},\ldots x^{t_{0}}\right]\overset{F}{\to} \left[\hat{x}^{t_{0}+1},\hat{x}^{t_{0}+2},\ldots\hat{x}^{t_{0}+H}\right]\),where F is the prediction model.  
The error analysis and anomaly detection section uses Chebyshev's inequality to determine the threshold value of error \(e_{i}\), and analyzes whether the error \(e_{t}\) between the observed value \(x^{t}\) at a point in time \(t\) and the predicted value \(\hat{x}^{t}\) at that point is within the threshold value to detect whether the data at that point is anomalous or not.
# Asphalt Pavement Transverse Cracking Detection Based on Vehicle Dynamic Response
###### Abstract
[PERSON]'s team focus on monitoring the road network through a low-cost system based on vehicle vibration, which reached the ideal effects for evaluating the pavement conditions [16; 17; 18]. Apart from that, some excellent dynamic vehicle models have been established to illustrate the basic principle between the pavement condition and the vehicle [19]. Most studies concentrate more on time or frequency domain analysis. The features extracted from the time or frequency domain are better when passing by some obvious distress, such as potholes and rutting. However, the features caused by transverse cracking are limited in the time or frequency domain, making them hard to detect. Pavement transverse cracking hardly affects the threshold of vehicle acceleration amplitude because of its limited length along the driving direction. Similarly, the Fourier transform just reflects the whole frequency distribution of the vehicle's vibration signal. It is challenging to express small signal mutations caused by transverse cracking, including its location. Apart from that, vehicle parameters are also important. Different locations can generate different dynamic responses. Different vehicle types, including fuel-driven and electric-driven, also affect the response amplitude [20]. So, it is necessary to select the right car and the ideal response location [21]. Consequently, the present work aims to accurately identify pavement transverse cracking damage using a vehicle dynamic response. To that end, this paper formulates a novel strategy to explore time-frequency features using a Short-time Fourier transform (STFT) and better characterization indicators. More importantly, this novel method can balance the key points, including detection accuracy, equipment cost, and efficiency. The detection method based on a vehicle's dynamics response solves the problems of low detection frequency and the high cost of existing detection methods. Through the optimized STFT algorithm, the method overcomes the lack of detection accuracy caused by traditional time-domain analysis or frequency-domain analysis. This is because the optimized STFTalgorithm focuses more on transverse cracking detection, which takes into account the accuracies of both time- and frequency-domain information recognition.