esa-sceva/satcom-chunk-collection huggingface dataset:
# Stable Analysis of Compressive Principal Component Pursuit
## 2 Notations and Main Results
### Notations  
We denote the operator norm of matrix by \(\|X\|\), the Frobenius norm by \(\|X\|_{F}\), and the nuclear norm by \(\|X\|_{*}\), and denote the dual norm of \(\|X\|_{(i)}\) by \(\|X\|_{(i)}^{*}\). The Euclidean inner product between two matrices is defined by the formula \(\langle X,Y\rangle=\text{\emph{trace}}(X^{*}Y)\). Note that \(\|X\|_{F}^{2}=\langle X,X\rangle\). The Cauchy-Schwarz inequality gives \(\langle X,Y\rangle\leq\|X\|_{F}\|Y\|_{F}\), and it is well known that we also have \(\langle X,Y\rangle\leq\|X\|_{(i)}\|Y\|_{(i)}^{*}\) (e.g., [1; 2]). \(\|X\|_{(i)}\) majorized the Frobenius norm means \(\|X\|_{(i)}\geq\|X\|_{F}\) for all \(X\). Linear transformations which act on the space of matrices are denoted by \(\mathcal{P}_{T}X\). It is easy to see that the operator of \(\mathcal{P}_{T}\) is a high dimension matrix. The operator norm of the operator is denoted by \(\|\mathcal{P}_{T}\|\). It should be noted that \(\|\mathcal{P}_{T}\|=\sup_{\{\|X\|_{F}=1\}}\|\mathcal{P}_{T}X\|_{F}\).  
For any matrix vector \(x=[X_{i}],i=1,2,\ldots,\tau\), where \(X_{i}\in\mathbb{R}^{m\times n}\) is i-th matrix. We will consider two norms of this matrix pair, which can define as \(\|x\|_{\diamond}:=\sum_{i}^{\tau}\lambda_{i}\|X_{i}\|_{(i)}\) and \(\|x\|_{2}:=\sum_{i}^{\tau}\|X_{i}\|_{F}\). In order to simplify the stability analysis of CPCP, we also define the subspaces (the common component) \(\gamma:=[\Gamma_{i}]\), \(\Gamma_{i}=(\sum_{l}^{\tau}X_{l})/\tau\;\;i=1,2,\ldots,\tau\), and (the different component) \(\gamma^{\perp}:=[\Gamma_{i}^{\perp}]\), \(\Gamma_{i}^{\perp}=X_{i}-\Gamma_{i}\)\(i=1,2,\ldots,\tau\). In order to analyze the behavior of special projection operator, we define the projection operator \(\mathcal{P}_{T_{1}}\times\cdots\times\mathcal{P}_{T_{\tau}}(x):=[\mathcal{P}_{ T_{1}}(X_{1}),\ldots,\mathcal{P}_{T_{\tau}}(X_{\tau})]\).  
we assume that \(\|X_{i}\|_{(i)}\;\;i=1,2,\ldots,\tau\) are decomposable norms. The definition of decomposable norms is below.  
**Definition 1** (Decomposable Norms).: _if there exists a subspace \(T\) and a matrix \(Z\) satisfying_  
\[\partial\|\cdot\|(X)=\{\Lambda|\mathcal{P}_{T}\Lambda=Z,\|\mathcal{P}_{T^{ \perp}}\Lambda\|^{*}\leq 1\}\,, \tag{3}\]  
_where \(\|\cdot\|^{*}\) denotes the dual norm of \(\|\cdot\|\) and \(\mathcal{P}_{T^{\perp}}\) is nonexpansive with respect to \(\|\cdot\|^{*}\). Then, we say that the norm \(\|\cdot\|\) is decomposable at \(X\)._  
**Definition 2** (Inexact Certificate).: _We say \(\Lambda\) is an \((\alpha,\beta)\)-inexact certificate for a putative solution \((X_{1,\diamond},\ldots,X_{\tau,\diamond})\) to (1.1) with parameters \((\lambda_{1},\ldots,\lambda_{\tau})\) if for each \(i\), \(\|\mathcal{P}_{T_{i}}\Lambda-\lambda_{i}Z_{i}\|_{F}\leq\alpha\), and \(\|\mathcal{P}_{T_{i}^{\perp}}\Lambda\|_{(i)}^{*}<\lambda_{i}\beta\)._
## 4 Results and Discussion
### Nanofluid Characterization
#### 4.2.2 The Dynamic Viscosity of the Nanofluids  
Figure 7a shows the dependance of the \(\mu_{NF}\) on the NP volume concentration and the temperature of the NFs. It can be seen that the NF dynamic viscosity appreciably increases over the base fluid with an increase in the NP volume concentration. At 35 \({}^{\circ}\)C, the increase in the dynamic viscosity of the NFs containing 0.2, 0.5, 1.0, and 1.5 \(v\)/\(v\)% NPs is 14%, 16%, 17%, and 22%, respectively, compared to the base fluid. Figure 7a also indicates that the \(\mu_{NF}\) decreases as the temperature increases, as is the case for a wide class of working fluids. From 25 to 45 \({}^{\circ}\)C, \(\mu_{NF}\) decreases by 26% for the NF at 0.2 \(v\)/\(v\)%, 27% for the NF at 0.5 \(v\)/\(v\)%, and 27% for the NFs at 1.0 and 1.5 \(v\)/\(v\)%.  
Figure 7b compares the measured data at 35 \({}^{\circ}\)C and predictions from the [PERSON] [44] and the [PERSON] [45] models defined in Equations (8) and (9), respectively.  
\[\mu_{NF}=(1+2.5\phi)\mu_{BF} \tag{8}\]  
\[\mu_{NF}=\frac{1}{(1-\phi)^{25}}\ \mu_{BF} \tag{9}\]  
where \(\mu_{BF}\) is the dynamic viscosity of the base fluid and \(\phi\) is the NP volume fraction in the NFs.  
Figure 6: (**a**) Experimental thermal conductivity of TiO\({}_{2}\)–water (W) nanofluids (NFs) as a function of temperature and NP volume concentration. (**b**) Comparison of the measured thermal conductivity at 35 \({}^{\circ}\)C and that predicted from two of the classical models on effective thermal conductivity.  
Figure 7b shows that the \(\mu_{NF}\) measurements are significantly greater than those predicted by Equations (8) and (9), which overlap. However, the obtained trend of the \(\mu_{NF}\) measured data is rather consistent with that reported by [PERSON] et al. [46] and [PERSON] et al. [47].
## 3 Results and Discussion
### Sieve Analysis  
The results of dust sieving are presented by cumulative curves (Figure 4). The results are expressed in weight percent of the individual fractions collected on a sieve with the appropriate mesh size. The results of the sieve analysis should be presented in accordance with the standard ISO 9276-1 [58] and [59]. Prepared Continuous Cumulative Curves are presented in Figure 4 with complete mesh size.  
The granulometric composition of the individual samples is influenced by the nature of the abrasive processes by which the samples were produced and the type of trees which were used [1; 20; 55; 56]. The examined samples of dust were produced in various technological operations, so their granulometric composition also differs.  
Spruce dust, as a product of a circular saw cutting (BOSCH GKS 190, manufactured by BOSCH, Gerlingen, Germany), had the highest proportion of 250 \(\upmu\)m to 500 \(\upmu\)m (45%) fraction, containing also small and larger chips, which was also identified by a study of its microstructure (Figure 4). Sawing is an operation where larger particles are produced (Figure 4). [PERSON] and [PERSON] [60] carried out an analysis of the shape, dimensions and granulometric composition of sawdust produced in the longitudinal sawing process of dry spruce timber by a thin cutting frame saw, type: CLASIC 150/200 (at material feed speed v = 0.5 m\(\cdot\)min\({}^{-1}\)). The results of the sieve analysis characterise dry spruce sawdust as a polydisperse bulk material with a grain size in the range of 85.38 \(\upmu\)m to 28.2 \(\upmu\)m,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Moisture**} & \multicolumn{3}{c}{**Waste Wood Dust Samples**} \\ \cline{2-4}  & **Spruce** & **Beech** & **Oak** \\ \hline (\%) & 6.428 \(\pm\) 0.025 & 5.085 \(\pm\) 0.012 & 4.793 \(\pm\) 0.020 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Granulometrically determined humidity of individual samples of waste wood dust.  
Figure 4: Continuous cumulative curve of spruce, beech and oak dust.  
and the largest fraction in the spruce sawdust with grain size in the range of d = 125 to 1000 \(\upmu\)m, which makes up 86.77-87.15% of the extracted sawdust from the thin cutting frame saws. These results are also supported by [PERSON] [61] in the study of dry spruce and oak sawdust produced in wood sawing processes.  
Beech dust had the largest fraction of 90 \(\upmu\)m to 150 \(\upmu\)m (28%). The finest dust was produced from oak (Figure 4) with the largest fraction of 71 \(\upmu\)m to 90 \(\upmu\)m.
# Multi-Modal Spatio-Temporal Knowledge Graph of Ship Management
###### Abstract
Consequently, the unified organization, management, and application of such data become and attributes to describe large-scale, multi-source heterogeneous data in a unified manner [6]. It can not only represent and processes complex semantic associations between data, but also mine and infer more knowledge for data analysis. Furthermore, the graph database also provides highly flexible and efficient query services. Recently, there has been a growing focus on the development of spatio-temporal knowledge graphs stored in graph databases, driven by the continuous improvement in computing efficiency for the attribute analysis of large interconnected datasets [7]. The robust integration capability of spatio-temporal knowledge graphs in managing temporal and spatial data resources offers effective solutions for these challenges. And spatio-temporal knowledge graph technology has found widespread application in various fields, including forest fire prediction. For example, [PERSON] et al. proposed a forest fire prediction method that combines spatio-temporal knowledge graphs with machine learning models to efficiently extract the required features [8]. However, the application of this technology in analyzing and predicting ship communication quality has been limited. To address this gap, we have developed a spatio-temporal knowledge graph based on ship communication. The contributions of this paper are briefly summarized as follows. (1) A multi-modal spatio-temporal knowledge graph is proposed to effectively address the issue of multi-source heterogeneous ship communication data management, aiming to facilitate data sharing and reuse. (2) A multi-modal spatio-temporal ontology is constructed by considering the multi-modal and spatio-temporal characteristics of ship communication data, which provides a more comprehensive and accurate data representation standard and improves the semantic interoperability between data. (3) Taking the communication scenario of fishing vessels going out to sea as an example, we validated the feasibility of using a multi-modal spatio-temporal knowledge graph for communication quality prediction and communication knowledge query tasks.
# Optical Boundaries for LED-Based Indoor Positioning System
## 4 Investigations Showing the Effect of Defined Optical Boundaries
### Accuracy of the Positioning System  
In this section, the effect of a defined optical boundary on the positioning accuracy for a given MLEM-based system is presented in terms of positioning error. To show the effect of the optical boundary on positioning error, a Monte Carlo simulation is used to calculate the positioning error of the overlap-based proximity technique introduced in Reference [75] and the process is presented in Algorithm 1.  
Firstly, one LED is used in the room, next, two LEDs are used for the investigation, and then, by replacing each LED with four LEDs uniformly distributed across the length and width of the room, the process is repeated and the results are presented in Section 5.2. Therefore, the number of LEDs increase in the progression \(1,2,8,32,...\), and for presenting the curves, an LED exponent factor is defined as  
\[n=\log_{2}(\text{number of LEDs}). \tag{23}\]  
The radius of minimal positioning error \(r_{m}\) is computed from the algorithm and this is used to determine the desired threshold angle of an LED \(\phi_{thd}\) given by  
\[\phi_{thd}=\tan^{-1}\left(\frac{r_{m}}{h}\right). \tag{24}\]  
Figure 5: Set-ups to show the effect of full angle on positioning. (**a**) Effect in a single LED positioning system, (**b**) the effect in the overlap system with two identical LEDs.
# JANNet: A U-Shape Network with Jump Attention for Semantic Segmentation of Road Scenes
## 3 Results
### Experimental Evaluation
#### 3.5.1 Ablation Experiments  
We conducted ablation experiments on the CamVid dataset for both the JAM module and the ConvLSTM module. We tested combinations of different numbers of JAM modules and ConvLSTM modules. The test results are shown in Table 4.  
Table 4 shows that the ConvLSTM module alone does not improve the MIoU much, and the effect even decreases in the metric FWIoU. In contrast, the JAM module alone has a significant improvement on both MIoU and FWIoU for the native UNet network. The segmentation accuracy of the UNet network gradually improves as more JAM modules are used. However, when we added JAM modules at the bottom layer of the network, the effect was not obvious and even degraded in the evaluation metrics, so our final network structure used only three JAM modules. In addition, after using three JAM modules and the ConvLSTM module, the JAUNet network shows a significant improvement compared with the base UNet network, which proves the good cooperation between the ConvLSTM and JAM modules.
# Low Temperature Microcalorimeters for Decay Energy Spectroscopy
## 3 History
### High Energy DES Measurements  
In 2007, the high energy (\(>\)2 MeV) measurements began. The Korea Research Institute of Standards and Science (KRISS) team used MMCs to measure the \(\alpha\)-decaying isotopes of \({}^{241}\)Am and \({}^{226}\)Ra using the method of drying a solution onto a gold foil followed by diffusion-welding [48; 49; 50; 51].  
Los Alamos National Laboratory (LANL) efforts on high energy DES spectroscopy began with \(\alpha\)-decaying isotopes \({}^{241}\)Am, \({}^{238}\)Pu, \({}^{209}\)Po, and \({}^{210}\)Po using TESs [9]. Following similar methods to those used previously, the actinide sources were dried onto a foil that was then folded and diffusion-welded. The Po source was autodeposited; however, because Po is volatile, the diffusion-welding step was omitted. Peak-splitting in the spectra from the Am and Pu measurements indicated differences in thermalization, which were eventually attributed to the material co-deposited with these solutions because these features were not seen in the Po measurements or subsequent measurements with electroplated sources [9; 52; 53]. The KRISS team also encountered this problem with later measurements and modeling of \({}^{226}\)Ra [54]. Researchers from the French Atomic Energy Commission (CEA) also found that, while their \({}^{210}\)Po measurements did not exhibit peak-splitting, there was an unusually long tail associated with the peak [55]. A 'tail' is a non-Gaussian feature in the spectrum, usually on the low-energy side of a peak, that is often described by an exponential. This can be due to incomplete thermalization within the absorbers.  
At LANL, peak-splitting was largely eliminated with mechanical kneading or alloying of the absorber to break up the crystalline structure of the co-deposits [53]. The diffusion-welding step was also found to be unnecessary. Peak broadening and tailing in these spectra was attributed to lattice damage, or kinetic energy converted to potential energy by dislocating atoms in the crystal lattice of the absorber, similar to that seen in \(\alpha\) spectroscopy with similar detectors [56]. With the method of mechanical kneading, precise measurements of the \({}^{240}\)Pu/\({}^{239}\)Pu ratio and the \({}^{241}\)Am/\({}^{239}\)Pu mass ratio could be made [53; 57; 58]. Additionally, simultaneous \(\alpha\)-decay and \(\beta\)-decay were measured with a \({}^{241}\)Am and \({}^{241}\)Pu source [52]. More recently these methods have been used for measurements of medical nuclides such as \({}^{225}\)Ac and contaminant \({}^{227}\)Ac [59].
# FAIDM for Medical Privacy Protection in 5G Telemedicine Systems
## 2 Related Works
### Telemedicine  
Telemedicine is a technology of electronic message and telecommunication related to healthcare [20, 21]. The patient will send healthcare related information, which is important, sensitive, and private, to healthcare services through public networks when using telemedicine technology [21]. Medical professionals can know users' health condition if they are able to view the information immediately [21]. Data transmission security will be discussed, such as eavesdropping, man-in-the-middle attack, data tempering attack, message modification attack, and data interception attack [22]. Technical support is not enough though Health Insurance Portability and Accountability Act (HIPAA), General Data Protection Regulation (GDPR), and Safe Harbor Laws have been made which provide personal information privacy [22, 23, 24].  
A general telemedicine system can be divided to three levels [25]. Level 1 (primary healthcare unit) consist of users with webcam, smart phone, or wearable devices; Level 2 (city or district hospital) is clinic or local hospital which patient may visit before being transferred to large hospital or medical center; Level 3 (specialty center) will take part in telemedicine in case of rare disease or incurable disease [25]. Figure 1 illustrates a remote patient monitoring system in 5G IoT architecture which can assist medical professional to monitor remote patient's biodata through specific devices [2, 25, 26]. Mobile health plays an important role on medical healthcare monitoring and alarm system and clinical data storage and maintenance system. In remote patient monitoring systems, wearable devices and mobile phones belong to a sensor layer which is responsible for gathering measured data. Measured data is transmitted to network layer, IoT gateway for example, through small base station (SBS) communication. After that, data will be transmitted out of the local area network to major base station (MBS), such as 5G link, through MBS communication. Both the network layer and communication layer are responsible for data processing. Finally, data will be transmitted to a medical services servers of clinic or local hospital called architecture layer, such as an electronic health records (EHR) system, cloud storage, and analytics. Authorized medical professionals in the main hospital can access medical services servers to monitor a patient. Authorized medical professionals in specialty center will involve and observe measured data in case of rare disease or incurable disease.  
In this paper, we introduced a cryptographic protocol which can be applied in asynchronous telemedicine and synchronous telemedicine and provide communication security and user anonymity to protect patient's privacy.
## 8 Open Research Challenges and Research Directions
### Security and Privacy Vulnerabilities Issues  
Security and privacy are crucial in healthcare services to ensure the safety and confidentiality of patient data as well as prevent the unauthorized sharing of sensitive information. Healthcare organizations must ensure that patient data remains secure and private, as mandated by HIPAA regulations [110]. HIPAA regulations require that healthcare service providers implement a variety of measures to protect patient data, such as encryption and access control systems. Healthcare providers must also have a written policy in place to ensure that patient data is protected and that any breaches of the policy are reported immediately. To ensure the confidentiality of medical information, unauthorized users are prevented from accessing it. An IoT health device uses authentication to verify the identity of its peers and ensure secure communication. A network service or resource can only be accessed by authorized nodes if they are authorized. Even if a fault occurs, a security scheme should continue to provide its respective security services.  
These challenges have a significant impact on the quality of patient care and need to be addressed through effective policies and procedures. For instance, it is crucial to store patient data in secure databases that are regularly updated with the latest security patches and monitored for any suspicious activities. However, some argue that these challenges may not be as significant as they appear and that the benefits of digital health outweigh the risks. For example, digital health improves patient outcomes by providing real-time data that is used to make informed decisions about treatment. It also enhances transparency and communication between patients and healthcare providers.
## 2 Results and Discussion
### Identification of Screened Potential XO Inhibitors  
In order to confirm the structures of the two screened potential XO inhibitors, High Performance Liquid Chromatography-Mass Spectrum (HPLC-MS) analysis was conducted and the related MS and UV data are shown in Table 1. Compound **1** exhibited two absorbance bands at 280 and 330 nm. In the negative ion mode of MS analysis, compound **1** showed the deprotonated molecular ion [M \(-\) H]\({}^{-}\) at 357 \(m/z\) and [2M \(-\) H]\({}^{-}\) at 715 \(m/z\) in the MS spectrum. According to the references, compound **1** was identified as gardenin B [26]. Compound **2** showed UV absorbance peaks at 250 nm, 275 nm and 340 nm. Meanwhile, the deprotonated molecular ion [M \(-\) H]\({}^{-}\) at 343 \(m/z\) and [2M \(-\) H]\({}^{-}\) at 687 \(m/z\) could be confirmed in the MS spectrum of compound **2** with the negative ion mode. In the positive ion mode, the protonated molecular ion [M \(+\) H]\({}^{+}\) was found at 345 \(m/z\), and [2M \(+\) Na]\(+\) at 711 \(m/z\). Therefore, the molecular weight of compound **2** could be considered as 344. Compound **2** was identified as epatorin because the UV and MS data were in agreement with previous reports [27]. The standard gardenin B and epatorin with high purities were also analysed by HPLC and the chromatograms are shown in Figure 7. It can be seen that the retention time of the two standards were in accordance with those in the extract.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Identification**} & **Retention** & **Molecular** & \multirow{2}{*}{**Proposed Ions (\(m/z\))**} & **Ultraviolet Spectrum** \\  & & **Time (min)** & **Weight** & & \(\Delta\)**max (nm)** \\ \hline \multirow{2}{*}{**1**} & \multirow{2}{*}{Gardenin B} & \multirow{2}{*}{31.90} & \multirow{2}{*}{358} & [M \(-\) H]\({}^{-}\) & 357 & \multirow{2}{*}{280, 330} \\  & & & & [2M \(-\) H]\({}^{-}\) & 715 & \\ \hline \multirow{2}{*}{**2**} & \multirow{2}{*}{Eupatorin} & \multirow{2}{*}{54.28} & \multirow{2}{*}{344} & [M \(-\) H]\({}^{-}\) & 343 & \multirow{2}{*}{250, 275, 340} \\  & & & & [2M \(-\) H]\({}^{-}\) & 687 & \\  & & & & [M \(+\) H]\({}^{+}\) & 345 & \\  & & & & [2M \(+\) Na]\({}^{+}\) & 711 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: The identification, retention time, MS and UV data of screened compounds.  
Figure 6: The chromatograms of (a) extract of Clerodendanthus spicatus, (b) eluent with Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\)-XO nanoparticles and (c) eluent with deactivated Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\)-XO nanoparticles.
## 6 Training and Simulation Results
### Computed a Gini Policing Index (GPI)  
To quantify fairness in patrol distribution, we computed a Gini policing index (GPI) comparing patrol density to actual crime rates across income-based community zones. The PPO model achieved a GPI of 0.19 (lower is better), while DDQN scored 0.26, indicating that PPO better minimized over-policing disparities. Low-income deviation was +7.2% for DDQN and +3.8% for PPO.