esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Model
### Decentralized Cooperative Localization Model
When no measurement update occurred, the update equations for firefighter \(i\) could be formulated as follows:  
\[\mathbf{\hat{X}}_{i}(k|k-1)=\mathbf{F}_{i}(k|k-1)\mathbf{\hat{X}}_{i}(k-1)+ \mathbf{B}_{i}(k-1)\mathbf{U}_{i}(k-1) \tag{25}\]  
\[\mathbf{P}_{ii}(k|k-1)=\mathbf{F}_{i}(k|k-1)\mathbf{P}_{ii}(k-1)\mathbf{F}(k| k-1)^{T}+\mathbf{Q}(k-1) \tag{26}\]  
\[\mathbf{P}_{ij}(k|k-1)=\mathbf{F}_{i}(k|k-1)\mathbf{P}_{ij}(k-1) \tag{27}\]  
\[\mathbf{\hat{X}}_{i}(k)=\mathbf{\hat{X}}_{i}(k|k-1) \tag{28}\]  
\[\mathbf{P}_{ii}(k)=\mathbf{P}_{ii}(k|k-1) \tag{29}\]  
\[\mathbf{P}_{ij}(k)=\mathbf{P}_{ij}(k|k-1) \tag{30}\]  
where \(\mathbf{P}_{ii}\) is the covariance matrix of firefighter \(i\), \(\mathbf{P}_{ij}\) is the covariance matrix between firefighter \(i\) and firefighter \(j\), and \(\mathbf{Q}(k-1)\) is the system noise covariance matrix. When the absolute measurement update of firefighter \(i\) occurred, the update equations for firefighter \(i\) could be formulated as follows:  
\[\mathbf{K}_{i}(k)=\mathbf{P}_{ii}(k|k-1)\mathbf{H}_{i}^{T}(k)\left(\mathbf{H} _{i}(k)\mathbf{P}_{ii}(k|k-1)\mathbf{H}_{i}^{T}(k)+\mathbf{R}_{i}(k)\right)^ {-1} \tag{31}\]  
\[\mathbf{\hat{X}}_{i}(k)=\mathbf{\hat{X}}_{i}(k|k-1)+\mathbf{K}_{i}(k)(\mathbf{ Z}_{i}(k)-\mathbf{H}_{i}(k)\mathbf{\hat{X}}_{i}(k|k-1)) \tag{32}\]  
\[\mathbf{P}_{ii}(k)=(\mathbf{I}-\mathbf{K}_{i}(k)\mathbf{H}_{i}(k))\mathbf{P}_{ ii}(k|k-1) \tag{33}\]  
\[\mathbf{P}_{ij}(k)=(\mathbf{I}-\mathbf{K}_{i}\mathbf{H}_{i}(k))\mathbf{P}_{ ij}(k|k-1) \tag{34}\]  
where \(\mathbf{K}_{i}(k)\) is the Kalman gain and \(\mathbf{R}_{i}(k)\) is the covariance matrix of the position observation noise. When the relative measurement update of firefighter \(i\) and firefighter \(j\) occurred, and firefighter \(i\) detected firefighter \(j\), the update equations for firefighter \(i\) are formulated were as follows:  
\[\mathbf{P}_{ii}(k|k-1)=\mathbf{F}_{i}(k|k-1)\mathbf{P}_{ii}(k-1)\mathbf{F}(k| k-1)^{T}+\mathbf{Q}(k-1) \tag{26}\]0  
\[\mathbf{P}_{ii}(k|k-1)=\mathbf{F}_{i}(k|k-1)\mathbf{P}_{ii}(k-1)\mathbf{F}(k| k-1)^{T}+\mathbf{Q}(k-1) \tag{26}\]1  
\[\mathbf{P}_{ii}(k|k-1)=\mathbf{F}_{i}(k|k-1)\mathbf{P}_{ii}(k-1)\mathbf{F}(k| k-1)^{T}+\mathbf{Q}(k-1) \tag{26}\]2  
\[\mathbf{P}_{ii}(k|k-1)=\mathbf{F}_{i}(k|k-1)\mathbf{P}_{ii}(k-1)\mathbf{F}(k| k-1)^{T}+\mathbf{Q}(k-1) \tag{26}\]3  
\[\mathbf{P}_{ii}(k|k-1)=\mathbf{F}_{i}(k|k-1)\mathbf{P}_{ii}(k-1)\mathbf{F}(k| k-1)^{T}+\mathbf{Q}(k-1) \tag{26}\]4  
where \(\mathbf{H}_{r}(k)=[\mathbf{H}_{ir}(k),\mathbf{H}_{ir}(k)]\) is the Jacobian matrix of the relative measurement model, \(\mathbf{K}_{ir}(k)\) is the Kalman gain of the relative measurement update, \(\mathbf{P}_{r}(k|k-1)= \begin{bmatrix}\mathbf{P}_{ii}(k|k-1)&\mathbf{P}_{ij}( k|k-1)\\ \mathbf{P}_{ji}(k|k-1)&\mathbf{P}_{ji}(k|k-1)\end{bmatrix} \) is the covariance matrix, \(\mathbf{R}_{r}(k)\) is the ranging noise covariance matrix of UWB sensors, and the subscript \(m\) is the firefighter that did not participate in the relative measurement.
## 3 Artificial Neural Network Models
Hereinafter, these Gaussian functions are referred as \(m_{1},m_{2}\)
* For each architecture, a Euclidean distance is evaluated between the functions of \(m_{1}\) and \(m_{2}\). This Euclidean distance is calculated with the properties described in Table 3. Equation (3) shows the implementation of this Euclidean distance: \[d_{ED}=\sqrt{\sum(m_{1}-m_{2})}\] (3)
* For each architecture, the maximum distance between the mean error values is considered to be zero. Equation (4) shows the implementation of this distance as the maximum absolute value of the mean property in testing and training error functions: \[d_{mean}=\max(\left|\right.mean_{m1}\left|\right.,\left|\right.mean_{m2} \left|\right.)\] (4)
* A maximum range of error functions must be calculated. This range is defined by the maximum and minimum properties for each error function (\(m_{1}\), \(m_{2}\)). \[d_{range}=\max(\left|\right.max_{m1}-\left.min_{m1}\left|\right.,\left|\right. max_{m2}-\left.min_{m2}\left|\right.)\] (5)
* To consider the skew property for \(m_{1}\) and \(m_{2}\), the distance between RMSE to MAD is calculated. Equation (6) shows the implementation of this distance as the maximum absolute value for the difference between RMSE and MAD for both \(m_{1}\) and \(m_{2}\): \[d_{skew}=\max(\left|\right.RMSE_{m1}-\left.MAD_{m1}\right|,\left|\right.RMSE _{m2}-\left.MAD_{m2}\right|)\] (6) \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Properties**} & \multicolumn{2}{c}{**Model A**} & \multicolumn{2}{c}{**Model B**} \\ \cline{2-5}  & **Training Data** & **Testing Data** & **Training Data** & **Testing Data** \\ \hline Mean square error & \(11.9\times 10^{-4}\) & \(5.8\times 10^{-4}\) & \(7.8\times 10^{-4}\) & \(6.7\times 10^{-4}\) \\ Root mean square error & \(34.5\times 10^{-3}\) & \(24.2\times 10^{-3}\) & \(28.0\times 10^{-3}\) & \(25.9\times 10^{-3}\) \\ Median absolute deviation & \(22.4\times 10^{-3}\) & \(16.7\times 10^{-3}\) & \(18.2\times 10^{-3}\) & \(16.7\times 10^{-3}\) \\ Max. value & \(0.1275\) & \(0.0811\) & \(0.0698\) & \(0.0675\) \\ Min. value & \(-0.1167\) & \(-0.0791\) & \(-0.0828\) & \(-0.0828\) \\ Mean & \(19.4\times 10^{-5}\) & \(87.0\times 10^{-5}\) & \(-8.0\times 10^{-4}\) & \(-1.8\times 10^{-4}\) \\ Standard deviation & \(34.5\times 10^{-3}\) & \(24.2\times 10^{-3}\) & \(28.0\times 10^{-3}\) & \(26.0\times 10^{-3}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Error validation example. Figure 3: Example of error density function comparison. Figure 4: Feed rate model performance; (**a**) Error density function comparison; and (**b**) Genetic algorithm performance. Figure 5: Cutting velocity model performance; (**a**) Error density function comparison; (**b**) Genetic algorithm performance.
## 2 Hypothesis
### Primary Cilia and Membrane Trafficking  
Primary cilia have also been singled out as key structures for controlling vesicle trafficking and membrane receptor expression. The base of the cilia in many cell types, including fibroblasts, are normally surrounded by a special membrane domain known as the cilium pocket region (CiPo), which has a peculiar biomolecular composition and is a hotspot for both exo- and endocytosis [111], while the tip of the cilia is a source of extracellular vesicles [101]. Cilia and the surrounding membrane area thus appear to be rich in clathrin-coated pits, clathrin-coated vesicles [112], endosomes [113; 114; 115], and possibly caveolae [116], which are controlled by the complex cytoskeletal network that underlies and surrounds cilia [101]. Vesicle endocytosis is a power tool that cells use to control the array of membrane-bound receptors exhibited on their surface. Cells can actually tune their signaling capabilities by removing available receptors from the membrane or by providing more free receptors for ligand binding [115]. Somewhat unsurprisingly, then, cilia and the periciliary area are rich with several kinds of receptors, including the BMP receptor, but also PDGFR and TGFBR [113; 117]. TGF-\(\beta\) receptors, in particular, have been shown to be localized at the tip of the primary cilia in fibroblasts, and their signaling has been proven to be regulated by vesicle trafficking, as clathrin-mediated endocytosis promotes their activation [116], whereas caveolin-mediated endocytosis downregulates TGF-\(\beta\) signaling [118].  
Although no published evidence has addressed the effect of electromagnetic fields on the turnover of TGF-\(\beta\) receptors in the ciliary and periciliary area yet, there is evidence supporting the hypothesis that EMFs can actually control clathrin-mediated endocytosis in murine B16F10 melanoma cells [119; 120; 121], autophagy in human neuroblastoma SH-SYSY cells [122], phagocytosis in mouse macrophages [123], and endocytosis at synapses [124]. There is also solid evidence that 75 Hz, 2.5 mT PEMFs upregulate the expression and membrane density of adenosine receptors in osteoblastic cells [125], as well as in other cell models [126], and this could provide a clue as to one way that EMFs could also control TGF-\(\beta\) signaling.
# Urban Vulnerability Assessment of Sea Level Rise in Singapore through the World Avatar
###### Abstract  
This paper explores the application of The World Avatar (TWA) dynamic knowledge graph to connect isolated data and assess the impact of rising sea levels in Singapore. Current sea level rise vulnerability assessment tools are often regional, narrow in scope (e.g., economic or cultural aspects only), and are inadequate in representing complex non-geospatial data consistently. We apply TWA to conduct a multi-perspective impact assessment of sea level rise in Singapore, evaluating vulnerable buildings, road networks, land plots, cultural sites, and populations. We introduce OntoSeaLevel, an ontology to describe sea level rise scenarios, and its impact on broader elements defined in other ontologies such as buildings (OntoBuiltEnv ontology), road networks (OpenStreetMap ontology), and land plots (Ontoplot and Ontozoning ontology). We deploy computational agents to synthesise data from government, industry, and other publicly accessible sources, enriching buildings with metadata such as property usage, estimated construction cost, number of floors, and gross floor area. An agent is applied to identify and instantiate the impacted sites using OntoSeaLevel. These sites include vulnerable buildings, land plots, cultural sites, and populations at risk. We showcase these sea level rise vulnerable elements in a unified visualisation, demonstrating TWA's potential as a planning tool against sea level rise through vulnerability assessment, resource allocation, and integrated spatial planning.  
sea level rise; knowledge graph; multi-perspective visualisation; vulnerability assessment; integrated spatial planning
# A 5G NR FR2 Beamforming System with Integrated Transceiver Module
###### Abstract  
This paper presents a 5G new radio (NR) FR2 beamforming system with an integrated transceiver module. A real-time operating module providing enhanced flexibility and capability has been proposed. The integrated RF beamforming system with an integrated transceiver module can be operated in 8 Tx-8 Rx mode configuration simultaneously. A series-fed structure \(8\times 7\) microstrip antenna array for compact size and improved directivity is employed in the RF beamforming module. The RF beamforming module incorporates a custom 28 GHz, eight-channel fully differential beamforming IC (BFIC). An eight-channel BFIC in a phased-array beamforming system offers advantages in terms of increased antenna density and improved beam steering precision. The RF beamforming module is integrated with an RF transceiver module that enables the simultaneous up-conversion and down-conversion of the baseband signal. The RF transmitter module consists of a transmitter, a receiver, a signal generator, a power supply, and a control unit. The RF beamforming system can scan horizontally from \(-50^{\circ}\) to \(+50^{\circ}\) with a step of \(10^{\circ}\). To achieve an optimized beam pattern, a calibration was conducted. The transmit and receive conversion gain of around 20 dB is achieved with the transceiver module. To verify the communication performance of the manufactured integrated RF beamforming system, a real-time wireless video transmission/reception test was performed at a frequency of 28 GHz, and the video file was transmitted smoothly in real time without interruption within a range of \(\pm 50^{\circ}\).  
5G; beamforming system; transceivers; phased array; RFIC; mm-wave +
Footnote †: journal: Accepted: 3 February 2024  
Footnote †: journal: Accepted: 18 March 2024  
Footnote †: journal: Accepted: 20 March 2024
## 3 Results and Discussion
### Accuracy Assessment  
We classified burned and unburned areas using RF, and the results are displayed in Table 4. Accuracy levels per class were consistently high for both burned and unburned classes, with producer accuracy of 98% to 97% and user accuracy of 97% and 98%, respectively (Table 4). The RF achieved an overall accuracy of 98%. These results are comparable with [PERSON] et al. [55], who detected burned areas with accuracies ranging from 82% to 91% within the GEE environment.  
Figure 7: **(a)** The forest fire burning in the Witfontein Plantation during October–November 2018 as recorded by the Sentinel-2 satellite; **(b)** Carbon Monoxide column density as measured by Sentinel-5; **(c)** daily Carbon Monoxide column density over the study area between 5 July 2018 and 20 May 2020.
## 2 Related Works
### Asymmetric Cryptography  
It is a cornerstone of contemporary digital security, operating on a dual-key mechanism: a public key for encryption and a distinct private key for decryption. This ground-breaking structure ensures that even if a malicious actor intercepts the encrypted data, they cannot decipher it without the closely guarded private key. Notably, this approach mitigates key distribution challenges inherent in symmetric systems, allowing for secure communication between parties who have never met. However, its computational intensity can be a  
Figure 1: Symmetric and asymmetric encryption.  
trade-off in terms of speed. While asymmetric cryptography fundamentally revolutionized digital trust by enabling secure public key exchanges and digital signatures, it necessitates rigorous private key protection to maintain its security fortress against potential intrusions.
# Bioremediation of Persistent Organic Pollutant--Oxybenzene with _Pleurotus diamor_
## 3 Results
### Identification of Biodegradation Products of Oxybenzene and Toxicity Evaluation  
In this study, extracts from in vitro cultures of _P. djamor_ were analyzed to determine the biotransformation products. The cultures were maintained with and without oxygenzone supplementation. Biotransformation products of oxybenzene were identified by comparing QToF detector chromatograms from samples incubated with (Figure 3A) and without (Figure 3B) oxybenzene, similar to previous studies on oxybenzene [25]. The protonated monoisotopic molecular masses (M+H)\({}^{+}\) and their fragmentation spectra (with fragmentation energies ranging from 10 to 25 eV) were determined for peaks corresponding to oxybenzene transformation products. The structural formulas of the identified compounds were determined using the ChemDraw Std v.20.0 with Analysis package (CambridgeSoft). Low-molecular aliphatic reaction products were not identified. The identified probable products formed after oxybenzene incubation with _P. djamor_ in vitro cultures are presented in Figure 4 and Table 3. In processes initiated by _P. djamor_, mainly acylated derivatives of oxybenzene are formed, which are products of esterification with low-molecular-weight compounds. The identified carbocations indicate that these are decomposition products of larger molecules with unstable molecular ions. Additionally, compounds with introduced thiol (sulfur) and amino (nitrogen) groups were identified. These may be condensation products of oxybenzene derivatives with medium components. Except for one product, 2_A_-dihydroxybenzophenone (product E), the identified biodegradation products were completely different from those in the analogous process conducted for _L. edodes_[25]. Demetylation of the methoxy substituent (O-demethylation) leads to the formation 2_A_-dihydroxybenzophenone--benzophenone 1. It is 'light stabilizer' and 'UV absorber, and it protects products from UV-induced degradation. 2,4-Dihydroxybenzophenone has been detected in various environmental matrices, indicating its widespread presence and potential ecological impact. In groundwater samples, BP-1 concentrations have been reported to reach up to 0.22 ug/L. Surface water analyses have shown BP-1 levels as high as 3.72 ug/L, particularly in areas downstream of wastewater discharge points. Additionally,  
Figure 2: The overall amount of oxybenzene in the dry mycelium and Oddoux liquid medium from in vitro cultures of _P. djamor_, which were supplemented with 25 mg of oxybenzene, after two weeks of growth. BP-1 has been identified in human biological fluids, including urine, placenta, and breast milk, suggesting its bioaccumulation and potential for human exposure [32,33].
# Predicting Hard Disk Failure by Means of Automatized Labeling and Machine Learning Approach
## 4 Proposed Approach
### RGF-Based Machine Learning Approach
#### 4.2.1 Training of Models
```
1:procedureDataSetCollection(\(D\))
2:for each disk \(d_{i}\in D\)do
3:\(S\longleftarrow\) Get SMART tuples daily of \(d_{i}\)
4:endfor
5: Return \(S\)
6:endprocedure
7:procedurePre-processing(S)
8:for each SMART tuple \(S_{i}\in S\)do
9:if\(S_{i}\) is corrupted OR missed OR out-of-range then
10: Remove \(S_{i}\) from \(S\)
11:endif
12: Label(\(S_{i}\)) according to Definition 1
13:if\(S_{i}\) is replaced in \([g_{1};g_{2}+k]\)then
14: Remove \(S_{i}\) from \(S\)
15:endif
16:endfor
17: Return \(S\)
18:endprocedure
19:procedureDesign of Experiment(\(S\))
20:\(T=\) TaguchiDesign(\(L_{9}\), replicates = 30)
21:for each design \(t_{i}\in T\)do
22:\(Shuffle(S)\)
23:\(S_{L},S_{T}=Split(S,70-30\%)\)
24:\(Model=RgfTrain(t_{i},S_{L})\)
25:\(LR+=RgfTest(Model,S_{T})\)
26:endfor
27: Select configuration \(C\) using \(EffectPlot(T,LR+)\)
28: Return \(C\)
29:endprocedure
30:procedureThreshold assessment(\(S,C\))
31:\(Shuffle(S)\)
32:\(S_{L},S_{T}=Split(S)\)
33: Get Definitive Model \(Model=RgfTrain(C,S_{L})\)
34: Get Probabilities \(P=RgfTest(Model,S_{T})\)
35:\(RC\longleftarrow RocCurve(P,S_{T})\)
36: Select Threshold \(T_{c}\) using \(RC\)
37: Return \(T_{c},Model\)
38:endprocedure
```  
**Algorithm 1** Classifier Training. The Regularized Greedy Forest is trained using the the training data set \(S_{\text{L}}\) and tested using the testing data set \(S_{\text{T}}\). Authors suggest at least 30 runs for each plan configuration in order to simultaneously assure statistical significance and feasible execution times. For each plan configuration, the values of Accuracy, Recall and FPR, expressed in percentage terms, are calculated as the median of the Accuracy, Recall and FPR achieved in the various runs. The final index of LP+ is calculated as the ratio of the median Recall and FPR. The RGF classifier returns a probability for each tuple of SMART measures of likelihood with respect to the two classes of healthy or broken. The last step for the user is the choice of a proper threshold beyond which discriminating whether the considered tuple identifies a Hard Disk that needs to be replaced or not. The choice of the optimal threshold turns out to be a trade-off between false and true positive rates. To drive the choice of the right threshold, the Receiver Operating Characteristic (ROC) curve has been exploited. The ROC curve, a graphic tool for the evaluation of FPR and TPR, helps the developer, in fact, to identify the best trade-off threshold, which the authors experienced close to the beginning of the curve knee.
# Lightweight Detection Method of Obfuscated Landing Sites Based on the AST Structure and Tokens
## Proposed System
### System Structure
During the structure similarity computation step, we compute the similarities between the input JavaScript to be tested and the obfuscated JavaScript sample in each obfuscation type in the syntax structure. Then, if it is obfuscated, we identify the obfuscation type. We use SimHash to calculate the similarity at this step. SimHash is a very powerful hash that is used to find duplications in crawling webpages by Google (2020). In this step, SimHash is used with a lightweight AST. At the token similarity computation step, we check whether the JavaScript contains redirection code by using the average of TF-IDF (we will explain TF-IDF in Section 3.3) of tokens corresponding to the obfuscation type. The malicious signature inspection step is in between two steps, i.e., the structure similarity computation step and the token similarity computation step. In this step, we inspect the signatures in the input JavaScript. Note that the signatures consist of strings used in advance in the redirection code. When a signature is found, we perform the token similarity computation step. Otherwise, we do not perform the next step because the input JavaScript is presumed to be benign obfuscated JavaScript. In this way, malicious signature inspection reduces the scope of the dataset to be tested in the token similarity computation step. Figure 1: Proposed system.
## 3 Adaptive TPHD Filter with Unknown Measurement Noise  
### The Extended State Space Model  
To solve the trajectory filtering in the background of unknown measurement noise covariance, we extend the noise covariance on the target set at a single time to the noise covariance sequence on the trajectory set and then build an extended state space model. We track in state space \(\mathbb{T}_{k}=\ \mathbb{\psi}_{(t,\bar{t})\in L_{k}}\left\{t\right\}\times \mathbb{R}^{\text{in}_{x}}\times\mathbb{U}^{i}\) until time \(k\) can set up on the extended trajectory set \(\overline{\textbf{X}}_{k}=\left\{\overline{X}_{1},\ldots,\overline{X}_{N_{k} }\right\}\ \in\ F(\mathbb{T}_{k})\), \(\overline{\textbf{X}}\) indicates the extended trajectory state set. Define the state element \(\overline{X}\) as measurement factors are introduced  
\[\overline{X}=\Big{(}X,R^{1:i}\Big{)}=\Big{(}t,x^{1:i},R^{1:i}\Big{)}\in T_{k}, \tag{15}\]  
where \(X\) is defined in the same way as in the Section 2, and \(R^{1:i}\ \in\ \mathbb{U}^{i}\) represents the covariance sequence of measurement noise in the extended trajectory state space, which is also different from the conventional trajectory state space. Similar to the point target, given the extended multi-trajectory density function \(p(\cdot)\) and the number of trajectories \(N_{k}\), the probability assumption of \(D_{p}\big{(}\overline{X}\big{)}\) bearing based on the extended state space can be obtained by the set integral below:  
\[D_{p}(\overline{X})=\int_{T_{k}}p(\overline{\textbf{X}})d\overline{\textbf{X} }=\sum\limits_{N_{k}=0}^{\infty}\frac{1}{\mathbb{N}_{k}!}\iint p\Big{(}\{X_{1 },\ \ldots,\ X_{N_{k}}\}\cup\Big{\{}R_{1}^{1:i},\ \ldots,\ R_{N_{k}}^{1:i}\Big{\}}\Big{)}dX_{1:N_{k}}dR_{1:N_{k}}^{1:i} \tag{16}\]