esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Advanced Persistent Threats  
The term advanced persistent threat (APT) is used to describe an attack in which the threat actor establishes stealth, long-term persistence on a victim's computing infrastructure. The usual goal is to exfiltrate data or to disrupt services when deemed necessary by the threat actor. These attacks differ from the typical 'hit and run' modus operandi as they may span from months up to years. The attacks are launched by high-skilled groups, which are either a nation state or state-sponsored.  
As noted by [PERSON] et al. [6], APT attacks consist of six phases: (1) reconnaissance and weaponization; (2) delivery; (3) initial intrusion; (4) command and control; (5) lateral movement; and (6) data exfiltration. Complimentary to this model, other works [7, 8] consider attack trees to represent APTs as different paths may be used in parallel to get the foothold on the targeted resources. Thus, information flows are often used to detect APTs [9], along with anomaly detection, sandboxing, pattern matching, and graph analysis [10]. The latter implies that EDRs may serve as excellent means to counter APT attacks.  
In many such attacks, threat actors use _fieless malware_[11], a particular type of malware that does not leave any malicious fingerprint on the filesystem of the victim as they operate in memory. The core idea behind this is that the victim will be lured into opening a benign binary, e.g., using social engineering, and this binary will be used to execute a set of malicious tasks. In fact, there are plenty of binaries and scripts preinstalled in Windows or later downloaded by the OS and are either digitally signed or whitelisted by the operating system and enable a set of exploitable functionalities to be performed. Since they are digitally signed by Microsoft, User Account Control (UAC) allows them to perform a set of tasks without issuing any alert to the user. These binaries and scripts are commonly known as _Living Off The Land Binaries and Scripts (and also Libraries)_, or LOLBAS/LOLBINS [12].
## 3 Methodology for Precise Code Positioning
### Doppler-Smoothed-Code Method  
To reduce pseudo-range noise and address code multipath effects, a practical approach is to use carrier-smoothed code (CSC) or Doppler-smoothed code (DSC). However, CSC is not suitable for smartphones due to several reasons. Firstly, the low-cost antenna on smartphones often results in numerous cycle-slips in the observed carrier phase observations [35; 46]. Secondly, as mentioned earlier, most smartphones cannot obtain carrier phase observations. The DSC method is expressed as follows [47]:  
\[\overline{P}_{(k)}=\frac{1}{n}P_{(k)}+\frac{n-1}{n}\cdot[\overline{P}_{(k-1) }+\frac{1}{2}\lambda\Delta t(D_{(k)}+D_{(k-1)})] \tag{6}\]  
where \(\overline{P}\) is the smoothed pseudo-range, \(k\) represents the \(k\) epoch, \(\Delta t\) is the interval between \(k-1\) and the \(k\) epoch, and \(D\) is the Doppler observation. \(n\) needs to satisfy the following condition:  
\[n=\left\{\begin{array}{l}k,k\leq M\\ M,k>M\end{array}\right. \tag{7}\]  
where \(M\) represents the maximum smoothing window and is device dependent, typically falling within the range of 50 to 100 epochs. It is important to note that when employing the DSC method, data preprocessing is a crucial initial step to eliminate outliers. If outliers are detected, the smoothing window is reset.
## 3 Results and Discussion
### Spectroscopic Studies of Ligand (L1) Cu (II) Complex
#### 3.1.3 Powder X-Ray Diffraction Analysis  
XRD spectral study of the Cu (II) complex is shown in Figure 2b. The XRD pattern of the metal complex shows a small peak at 10.13\({}^{\circ}\) and a broad peak at 24.02\({}^{\circ}\), indicating that the metal complex has a low crystalline nature and is expected to have a solid amorphous state.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Compounds** & **\(\mathbf{\upsilon}\)(N-H)** & **\(\mathbf{\upsilon}\)(C-H)** & **\(\mathbf{\upsilon}\)(C=O)** & **\(\mathbf{\upsilon}\)(C=N)** & **\(\mathbf{\upsilon}\)(C=S)** & **\(\mathbf{\upsilon}\)(C-O)** & **\(\mathbf{\upsilon}\)(C=C)** \\ \hline Ligand(L1) & 3459 & 2865–2964 & 1640 & 1569 & 1328 & 1170 & 1275 \\
[Cu(L1)\({}_{2}\)(L2)] & 3249 & 2850–2982 & 1638 & 1556 & 1319 & 1172 & 1266 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristic IR bands (cm\({}^{-1}\)) of the organic ligand (L1) and its Cu (II) complex with KBr pellets as reference.  
Figure 2: (**a**) FTIR spectra of organic ligand (L1) and Cu (II) complex and (**b**) powder XRD plot of mixed ligand Cu (II) complex.
## 3 System Model
### Environment Model
Their multiplication models how effectively the sensor detects radiation from different directions at its current position. The sensing table \(ST\) is constructed by concatenating all individual sensing results \(ST_{i}\) from each UAV. When a UAV receives signal strength exceeding the predetermined threshold within its sensing range, the area is marked as potentially containing a radiation source. To ensure localization accuracy, the system employs a multi-UAV cooperative verification mechanism: the three-point localization process is triggered only when three or more UAVs simultaneously detect suspected radiation sources within their respective sensing ranges. This design ensures detection reliability while avoiding false judgments from individual UAVs.
## 5 Experiment
### Experiment Results  
In this subsection, we conduct the hopping experiments to compare the hopping performance of the two attachment geometries. Figure 6 shows the images of the vertical hopping of the one-legged robot under the DAG and FMAG captured by a high-speed camera. The initial picture of the hopping locomotion is chosen when the robot leg has a slight movement. As shown in Figure 6a,b, the take-off phase of the robot under the DAG and FMAG last about 0.3 s and 0.24 s, respectively. And the corresponding hopping heights of the robot body are about 1.122 m and 1.241 m. Therefore, the FMAG can achieve a shorter takeoff time and a higher hopping height. Thanks to the high friction force of the elastic tape at the toe, there is no foot sliding during the take-off phase. The swing of the leg after the take-off phase is mainly caused by the pulling of the pipeline.  
The simulation results and the experiment results of the cylinder pressures and the knee joint angles are illustrated in Figure 7. The difference between the simulation and  
Figure 5: Hydraulic and test system. (**a**) Sketch of the hydraulic system; (**b**) Physical layout of the hydraulic and test system.  
Figure 6: Images of hopping locomotion under the two attachment geometries recorded at 100 fps. (**a**) Hopping locomotion under the DAG; (**b**) Hopping locomotion under the FMAG.  
experiment results is mainly caused by the friction force at the cylinder and vertical rail and the delay of the control valve. With the opening of the control valve, the driving pressure of the cylinders both under the DAG and FMAG quickly rise from the initial pressure, and the knee joint angle begins to accelerate from 40\({}^{\circ}\). After reaching the highest pressure, the driving pressure begins to decrease continuously, and the return pressure begins to increase correspondingly. In Figure 7a, the decrease of the driving pressure accelerates gradually from about 0.04 s to 0.23 s. After that, the speed of the driving pressure decline begins to slow down from about 0.23 s to 0.32 s. In Figure 7c, the duration of the accelerated decline of the driving pressure is shorter (from about 0.035 s to 0.16 s). And the duration of the slowdown of the driving pressure decline is longer (from about 0.16 s to 0.27 s). Moreover, the final pressure drop under the DAG (5.4 MPa) is larger than that under the FMAG (4 MPa). The hopping results of the DAG and FMAG are listed in Table 4.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Maximum Hopping** & **Take-Off Speed** & **Take-Off Duration** & **Maximum Pressure Drop** \\ \hline DAG & 1.122 m & 2.69 m/s & 0.32 s & 5.4 MPa \\ FMAG & 1.241 m & 3.04 m/s & 0.27 s & 4 MPa \\ \hline \hline \end{tabular}
\end{table}
Table 4: Hopping results of the kangaroo inspired one-legged robot.  
Figure 7: Simulation and experiment results of the cylinder pressures and knee joints under the DAG and FMAG. (**a**) Cylinder pressures of the DAG; (**b**) Knee joint angle of the DAG; (**c**) Cylinder pressures of the FMAG; (**d**) Knee joint angle of the FMAG.
# Validating a 5G-Enabled Neutral Host Framework in City-Wide Deployments
## 6 Validation of Use Cases
### Results Analysis
Regarding the User Experienced Data Rate, measured values report the cumulative DL throughput achieved by end-users in the considered multitenant scenario with three concurrent active slices, sampled every second during a period of 60 s. As for the Data Plane Delay, values included in Table 1 were measured against a remote server located in the edge computing hosts of each testbed. Therefore, obtained results show the network latency incurred between user equipment and edge computing instances, without including the processing time of network functions. These measurements may be impacted by several factors, such as the existing traffic load, distance from radio nodes, and propagation conditions. Overall, the conducted trials and performed evaluations validate the feasibility of the proposed neutral host framework by demonstrating the correct operation and benefits of its technology components. Furthermore, regarding the slicing and orchestration (main research focus of our solution), related results demonstrate that the developed platform performs well, achieving the 5G PPP programmatic KPI for _Service Creation Times in minutes instead of hours_.
## 5 Classification of Smart Grid Cyberattack
### Data Secrecy Attacks
#### 5.2.1 Eavesdropping  
Eavesdropping is a type of passive attack in which the attacker taps into communications between the nodes on a channel. They resemble MitM and impersonation attacks, where a malicious party secretly disrupts lawful peers' communication on a network and steals their important data. The impact of eavesdropping attacks can affect the integrity and accountability of the system as well. In smart grid communication, eavesdropping attacks affect data confidentiality either by intercepting wireless communication in a home area network or sniffing IP packets on the LAN. Eavesdropping intrusion occurs when an unauthorized user obtains access to a cyber system and gains unauthorized access to crucial backend servers. There are several countermeasures for eavesdropping attacks: message encryption, access control, anti-virus programs, firewall, VPN, and IDS. Encryption reduces eavesdropping attacks substantially. Many existing authentication schemes and encryption algorithms are adopted in a smart grid [90; 91].
# Phase Shift Design in RIS Empowered Wireless Networks: From Optimization to AI-Based Methods
## 2 RIS Resource Allocation Examples and General Formulation
\(\beta_{m}\) is an unknown variable and is independent of \(\theta_{m}\)[40, 41]. This model leads to a convex set \(\mathcal{F}\), described by \(|e_{m}|^{2}\leq c\) for some constant \(c\);
* C3. \(\beta_{m}\) is a function of \(\theta_{m}\). This is a relatively new model and takes the hardware properties into consideration. For example, one of the recent models [42] states that \[\|\mathbf{w}\|^{2}\leq P_{\max},\]5 where \(\beta_{\min}\), \(\phi\) and \(\alpha\) are known constants related to the specific circuit implementation. * **Discrete phase shift**: Each RIS coefficient \(e_{m}\) can only take one of the \(L\) possible phase shift values. Among the three continuous phase shift models, C2 is a convex set, and thus its treatment is similar to conventional resource allocation problems. Another way to view C2 is by treating the optimization of \(\beta_{m}\) and \(\theta_{m}\) separately, so that C2 is equivalent to \(0\leq\beta_{m}\leq\sqrt{c}\) and \(|e^{i\theta_{m}}|^{2}=1\). If we regard the optimization of \(\beta_{m}\) as part of conventional resources, the remaining constraint \(|e^{i\theta_{m}}|^{2}=1\) reduces to model C1. For C3, although it is non-convex, it can be handled by gradient descent on \(\theta_{m}\) (to be detailed in the next section). For C1, even though \(\beta_{m}\) is known and fixed, due to the modulus requirement, its handling is non-trivial, and there are a number of methods with different solution qualities for tackling this constraint. On the other hand, for the discrete phase shift case, the corresponding problem (5) is an integer nonlinear program and is NP-hard (i.e., the optimal solution cannot be found in polynomial time). However, the most prevalent way for handling this model is to relax the discrete variables to their continuous counterparts. Then, each of the obtained continuous phase shifts (by any methods for solving continuous phase shift model) is quantized to its nearest discrete value. Since the resolution of discrete phase shifts increases with the number of allowable phases, the quantization loss will be insignificant when the number of allowable phases is large [43]. Since C1 is the most fundamental model, in this paper, we focus on reviewing the optimization methods for model C1, with some of the reviewed methods also applicable to C2 and C3. The AI-based methods will be covered in Section 4 with reinforcement learning also suitable for the discrete phase shift model. Further emerging approaches for handling the discrete phase shift case will be discussed in the section of future challenges.
# Architectural Patterns for Blockchain Systems and Application Design
## 4 Pattern Description
### Structural View Patterns
#### 4.1.3 Distributed Off-Chain Pattern  
**Summary:** This pattern addresses concerns regarding the decentralized communication of back-end components within the external environment of the blockchain.  
**Context:** A blockchain application utilizes off-chain services as a distributed system.  
**Solution:** Off-chain back-end servers provide their services through distributed servers to distribute access to web apps and off-chain data, as shown in Figure 3. For example, a blockchain application may use an off-chain P2P file system, such as the BitTorrent File System (BTFS) and Interplanetary File System (IPFS), for web hosting and data storage. Instead, the application may utilize a P2P file-sharing system for either web hosting or data storage and use a centralized server for the other as a three-tier client-server architecture.  
**Known uses:** Aragon is an Ethereum-based service for the creation of Decentralized Autonomous Organizations (DAOs) that uses IPFS for their data. Augur is an Ethereum-based marketplace that uses IPFS for client application hosting and distribution. Dtube is a Steem-based video-sharing platform that uses IPFS and BTFS for users' video hosting.  
**Related patterns:** This pattern complements the P2P on-chain and on-chain-off-chain patterns.
## 3 Energy-Sensitive X-ray and Gamma-ray Imaging in Wide FoV
### Spatial and Angular Resolution  
The developed device is configured for X-ray/Gamma-ray imaging. The spatial and angular resolution for detection and directional imaging of X-rays and Gamma-rays is given by the angular acceptance and resolution of the collimator/optics architecture coupled to the detector spatial resolution. At the detector level, single photons are detected with spatial resolution at the pixel pitch size (55 \(\upmu\)m) with no directional response. The directional sensitivity of the Timepix detector itself is applicable to energetic charged particles to which spectral-tracking analysis is implemented [26]. For X-ray and Gamma-ray photons the collimator/optics element is used to provide directional sensitivity. The angular resolution for the 2D and 1D quadrant elements is 0.5 deg (ROI 1, 3, and 4) and for the open quadrant element is 3 deg (ROI 2) (Section 2.1). The angular resolution essentially does not vary in the given acceptance range.  
The resulting sensitivity of the assembled device, in the examined region (below few hundred keV), has only small variability on the photon energy. This is due to the partial decrease in the detection efficiency of the pixel detector to high-energy photons. The angular response, given namely by the optical/collimator element, has essentially no variability on the photon energy.
# Tomographic Diffractive Microscopy: A Review of Methods and Recent Developments
## 2 Theoretical Background
### Born Approximation with Linear Inversion  
In far-field cases, if the observation position \(\mathbf{r}\) is sufficiently far away from the sample, or if the sample is weakly scattered enough, typically the sample with small permittivity contrasts, i.e., \(\Delta\varepsilon<0.1\); the amplitude of the scattered field is tiny compared to that of the reference field; an approximation termed Born approximation was commonly used in TDM [35; 39; 40; 41; 42; 43]. The scalar Green function that approximates the direction given by the wave vector \(\mathbf{k}\) in far field is:  
\[\mathbf{G}(\mathbf{r},\mathbf{r}^{{}^{\prime}})=\frac{\mathrm{e}^{\mathrm{i} \mathrm{k}_{0}\mathbf{r}}}{4\pi\mathrm{r}}\mathrm{e}^{\mathrm{i}\mathbf{k} \cdot\mathbf{r}} \tag{7}\]  
where \(\mathbf{k}=\mathrm{k}_{0}\frac{\mathbf{r}}{\mathbf{r}}\). Defining \(\mathbf{E}_{\text{ref}}(\mathbf{r})\) as a plane wave with incident wave vector \(\mathbf{k}_{\text{inc}}\), we obtain the scattered field as:  
\[\mathbf{E}_{\text{sca}}(\mathbf{r})=\mathrm{k}_{0}^{2}\frac{\mathrm{e}^{ \mathrm{i}\mathrm{k}_{0}\mathbf{r}}}{4\pi\mathrm{r}}\int\limits_{\mathrm{V}} \chi(\mathbf{r}^{{}^{\prime}})\mathrm{e}^{-\mathrm{i}(\mathbf{k}-\mathbf{k}_ {\text{inc}})\cdot\mathbf{r}^{{}^{\prime}}}\mathrm{d}\mathrm{V} \tag{8}\]From Equation (8), in far field and the Born approximation conditions, the field scattered \(\mathbf{E}_{\mathrm{sca}}(\mathbf{r})\) along the wave vector \(\mathbf{k}\) for an illuminating wave vector \(\mathbf{k}_{\mathrm{inc}}\) and the 3D Fourier transform of \(\chi\) taken at \(\mathbf{k}-\mathbf{k}_{\mathrm{inc}}\) are proportional:  
\[\mathbf{E}_{\mathrm{sca}}(\mathbf{k}-\mathbf{k}_{\mathrm{inc}})\propto\overline {\chi}(\mathbf{k}-\mathbf{k}_{\mathrm{inc}}) \tag{9}\]  
Hence, the dielectric constant contrast map of the object can be retrieved by a simple linear inverse Fourier transform of the scattered field recorded in the far field. [PERSON] et al. reported several successful 3D biological sample reconstructions by TDM, shown in Figures 3 and 4[41; 44].  
The resolution of TDM under the Born approximation is determined by the accessible Fourier domain, which depends on the configuration of the illumination and detection [45], which we will discuss in the following section.  
Figure 4: _Betula_ pollen grain image reconstructed by tomographic diffractive microscopy (TDM) under Born approximation. (**a**,**b**) Volumetric cuts of a refraction image and an absorption image, respectively. Scale bar: 10 \(\upmu\)m. (**c**) Outer view of the pollen: Image of the absorption component. (**d**) Outer view of the pollen: Image of the complex index of refraction. (**e**) (\(x\)-\(y\)) cut through the pollen [44].  
Figure 3: A 3D view of a _Coscinodiscus_ sp. diatomeme using tomographic diffractive microscopy (TDM) with Born approximation. The pictures depict an 18-step, 0\({}^{\circ}\)–180\({}^{\circ}\) rotation of the specimen [41].
# Integration of the 3D Environment for UAV Onboard Visual Object Tracking
## 3 Single Visual Object Tracking Pipeline for UAV  
The designed framework is intended to be modular, allowing us to easily substitute different components or add other methods. The essential architecture of our approach is presented in Figure 4.  
On an incoming frame, the visual tracker defines a search area that is based on the previous estimated position and size of the object. The visual tracker then produces a similarity score map along with estimating an initial position and size of the object \((x_{i}^{o},y_{i}^{o},w_{i},h_{i})_{t}\) in the current frame \(i\) at time step \(t\). The _3D Context_ component estimates the location of the object in the 3D scene space through the similarity score map, the depth map approximation, and a state estimator--i.e., a particle filter. This allows the framework to distinguish the object from distractors and also to identify occlusions. Finally, the 3D position estimated by the framework is projected back in the image space as \((x_{i}^{n},y_{i}^{n})_{t}\), corresponding to the final estimated position of the object. It should be noted that no semantic information from the scene--i.e., the road--is used to facilitate the tracking process.  
Section 3.1 describes the visual tracker component. An overview of the mapping from the 2D image space to the 3D scene space is given in Section 3.2. The particle filter is described in Section 3.3. Lastly, we outline the details of our framework in Section 3.4.
## 2 Background and Related Work
In such cases, ontology alignment approaches have been investigated to support semantic interoperability. Several states of the art methods have been explored, such as [PERSON] et al. [36], provides the alignment of ontologies related to the biomedical domain. This work follows one to one alignment among ontology concepts and their relationships. This system uses structure and terminology based alignment and some external information for similarity-based alignment. An automated ontology alignment method proposed by [PERSON] [6], handles complex ontologies with RDFs and Ontology Web Language (OWL). The method processes alignment in three steps--first is to divide the ontologies, second is to provide alignment of the divisions, and the third is to learn the alignments. An agent-oriented alignment approach has been provided by [PERSON] [37], where agents provide initial closely accurate alignments which are further improved after the integration. Another approach that has been developed for semantic alignment is based on multi-policy dynamic alignment with multiple matching tasks [38]. Also, in the bioinformatics domain, one study [39] provides an automatic semantic similarity-based alignment and integration. Moreover, several other approaches that support data interoperability make use of common data models. These approaches provide a shared representation of data through standard vocabulary and shared schema. In data interoperability provisioning process common representation of data is generated so that it can be understood by several heterogeneous applications which provide a highly useful way to support integration and sharing of information. To support healthcare applications analytics, Observations on Healthcare Data Sciences, and Informatics (OHDSI) [40] has introduced a common data representation model. Some similar types of data models have also been developed in previous research studies such as [41], where the purpose was to build a common abstract data model to handle neuroscience data. Another shared data model introduced by [PERSON] [42] was derived from the Mini-Sentinet Common Data Model (CDM), follows a common data transformation.