esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Perception of the User in the Environment: Methodologies and Technologies for Localization
### Methodologies for Indoor Localization
Time Difference of Arrival (TDoA) employs more transmitter (three at least) and calculates the differences of the distances with the receiver. In this case, strict synchronization is still required but only among transmitters [19]. The Return Time of Flight (RToF) is based on the measurement of the round trip propagation time meaning that it considers the path transmitter-receiver-transmitter. In principle, it is similar to ToF but a less strict synchronization is required. On the other hand, sampling rate and bandwidth are more critical since the signal is transmitted and received twice [19]. The Phase of Arrival (PoA) is based on the phase difference between the transmitter and the receiver to compute the distance. Localization is performed by TDoA based algorithms. This method can be used RSSI, ToF, TDoA to improve the localization accuracy [20; 21]. As concerns disadvantages, it requires line-of-sight for high accuracy.
## 4 Augmented Reality Devices
### Projection-Based Systems  
Projection-based AR devices work by projecting artificial light onto real-world surfaces and then sensing the human interaction with that projected light. The projection can be done on any type of surface and can be interactive depending on the sophistication of the system. This type of AR technology does not require the user to wear or hold any equipment, which differentiates it from other AR methods [65]. Some main domains where projection-based AR devices are used are: industrial design and manufacturing, medical and healthcare, education and training, gaming and entertainment, and retail and advertising [65]. Projection-based AR can be found in a variety of devices, ranging from advanced setups used in industrial applications to smaller, consumer-focused products [56]:  
* **Projection Keywords:** Devices like the Celluon Epic project a virtual keyboard onto a flat surface, allowing users to type as they would on a physical keyboard. While not AR in the traditional sense, these devices use similar principles of light projection and interaction detection.
* **Interactive Projectors:** These are advanced systems that can project images onto any surface, turning them into an interactive display. Examples include the Sony Xperia Touch and the Lightform LFI, which can scan the environment to create a 3D map and project interactive AR content.
* **AR Sandboxes:** These are educational tools that project a topographic map onto a sandbox. As users move the sand around, changing the landscape, a sensor detects the changes and the projector adjusts the map in real-time. While these are often custom-built for educational environments, the principles could be applied in other AR projection systems.  
* **Industrial AR Systems:** Companies like DAQRI and MetaVision have developed projection-based AR systems for industrial applications. These often involve projecting instructions or data onto work surfaces or components.
* **Medical Systems:** Certain advanced surgical systems use projection-based AR to provide visual guidance during procedures. For example, the VeinViewer system projects an image of a patient's veins onto their skin, aiding in procedures like blood draws or IV placements.
# Recent Progress in Fast Distributed Brillouin Optical Fiber Sensing
## 2 Operation Principle
\(g_{o}\) and \(\mathrm{g}_{a}\) are the coupling coefficients of the optical wave and the acoustic wave, respectively. \(\Gamma=\Gamma_{B}/2+i(\Omega_{\mathrm{B}}-\Omega)=1/\tau_{\mathrm{P}}\) where \(\tau_{p}\) is the phonon lifetime. For the probe wave, it experiences not only the Brillouin amplification but the Brillouin phase-shift [11] (i.e., the vector SBS effect), which is shown in Figure 1b. The BGS and BPSS can be depicted by the equations below. Figure 1: The classical BOTDA scheme. (**a**) The time sequence: the probe wave with the frequency sweeping is switched segment-by-segment as the pump pulses are launched into FUT in the opposite direction maintaining the same time interval \(T_{round\_trip}+T_{switch}\). (**b**) The frequency domain relationship. BGS: blue line. BPSS: magenta line. Pump wave: red line. \[g_{SBS}(\
u_{S},z) =g_{p}\frac{\Delta\
u_{B}^{2}}{4\Delta\
u^{2}+\Delta\
u_{B}^{2}} \otimes P_{P}(\
u_{P},z) \tag{4}\] \[\varphi_{SBS}(\
u_{S},z) =g_{p}\frac{2\Delta\
u_{B}\Delta\
u}{4\Delta\
u^{2}+\Delta\
u_{B} ^{2}}\otimes P_{P}(\
u_{P},z) \tag{5}\]  
where \(g_{p}\) is the gain factor at the line center, \(\Delta\
u_{B}\) is the Brillouin linewidth, \(\Delta\
u=\Delta\
u_{P}-(\Delta\
u_{S}+\Delta\
u_{B})\) is the frequency detuning of the probe wave from the BFS, \(P_{P}(\
u_{P},z)\) is the power spectrum of the pump wave, and the sign \(\otimes\) represents the convolution operator.
# ScriptBlock Smuggling: Uncovering Stealthy Evasion Techniques in PowerShell and.NET Environments
## 3 ScriptBlock Smuggling
### Real-World Case Study  
As of this publication, ScriptBlock Smuggling represents a novel and theoretical attack vector, with no known instances of exploitation by malicious actors. Exploiting ScriptBlock  
Figure 8: Example code of ScriptBlock Smuggling using C#.  
Smuggling presents several adversarial concerns and possible uses for malicious actors, particularly when bypassing ScriptBlock logging. As of this publication, no empirical evidence exists that a malicious actor is exploiting this technique. Due to this lack of evidence, we will explore a hypothetical scenario that an adversary could pursue using Figure 9.  
The attack scenario unfolds through a carefully crafted ScriptBlock Smuggling payload. The attacker will adapt the code demonstrated in Section 3.5 into an attack path that many APTs use. Initially, an attacker constructs two distinct ScriptBlocks: a benign one designed to spoof ScriptBlock logs and bypass antivirus scans, and an Remote Access Trojan (RAT), embedded with second-stage AMSI bypass and ScriptBlock logging bypass techniques, shown in Figure 10. An RAT is a type of malware that allows an attacker to control a system remotely, often used for espionage, data exfiltration, or, as in this case, a launchpad for further attacks. Upon execution, the malware presents a legitimate program through the benign segment, thereby evading initial security scrutiny. Subsequently, the concealed malicious segment activates, effectively disabling the AMSI and ScriptBlock logging defenses, creating a covert pathway for further nefarious activities.  
In the aftermath of these initial bypasses, the attacker progresses to the payload delivery phase. Utilizing the compromised system's PowerShell environment, a second ScriptBlock fetches and executes a remote payload from a specified attacker-controlled server, known as a Command and Control (C2) server. This payload, typically a script or executable, is then executed directly on the target system to deploy a post-exploitation implant, such as Empire [24]. Empire is a C2 framework that allows for extensive control over the target system. By fetching and executing a remote payload, the attacker establishes a foothold, enabling them to execute commands, exfiltrate data, and maintain persistent access without triggering logging mechanisms or antivirus detection, as depicted in Figure 11.  
Figure 9: Theoretical attack path by an APT using ScriptBlock Smuggling.  
and the ability to detect malicious activities earlier. However, this strategy poses challenges with the complexity of implementing such changes in the PowerShell engine, the potential performance overheads due to the integrated scanning process, and ensuring backward compatibility with existing scripts and tools.
# Real-Time Detection of Insulator Defects with Channel Pruning and Channel Distillation
## 3 Materials and Methods  
### YOLOv7-Tiny Algorithm  
As one of the most prominent object detection algorithms, the YOLO series effectively balances detection speed and accuracy. YOLOv7 is available in multiple versions, each varying in terms of the width and depth of the network model, yet all sharing a similar structure. Given the importance of speed in insulator defect detection using drone aerial photography, this paper selects the smallest and fastest version: YOLOv7-tiny. The specific structure is illustrated in Figure 1.
## 3 Results
### Qualitative Assessment  
Figure 3 shows the SEM morphologic changes characterizing the specimens of the different groups during different experimental stages. SEM images are representative of the specimen portion closest to the cervical region of the tooth. Irrespectively from the considered group, surfaces appeared smooth at T0, polished and homogeneous. The acid challenge at T1 caused a deep erosion of the enamel surface partially exposing dentin tubules in all the considered groups. Among groups, a different surface morphology appeared at T2, correlated to the used treatment: G0 specimens showed a slight surface remineralization, not sufficient to mask the dentin tubules; G1 specimen surfaces were more compact and homogeneous, similar to the baseline at T0 of the same group, with the dentin tubules efficiently occluded; G2 specimens showed a well remineralized surface, with complete occlusion of the dentin tubules, CPP-ACP cluster can be recognized onto the surface. At T3, the G0 specimen surfaces were drastically eroded, with dentin tubules exposure and visible roughness increase; G1 specimen surfaces showed to resist the additional erosion thanks to an effective protective layer which prevented dentin tubules exposure; in the G2 specimens the CPP-ACP clusters previously visible onto the surfaces were mostly removed by the acid solution, although the sub-surface layer resisted, protecting the underlying dentin tubules from exposure.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Sa [\(\mu\)m] \({}^{*}\)**} \\ \cline{2-5}  & **T0** & **T1** & **T2** & **T3** \\ \hline G0 & 0.129 \(\pm\) 0.018 \({}^{\rm Aa}\) & 0.502 \(\pm\) 0.085 \({}^{\rm Ab}\) & 0.494 \(\pm\) 0.098 \({}^{\rm Ab}\) & 0.748 \(\pm\) 0.101 \({}^{\rm Ac}\) \\ \hline G1 & 0.127 \(\pm\) 0.022 \({}^{\rm Aa}\) & 0.498 \(\pm\) 0.126 \({}^{\rm Ab}\) & 0.286 \(\pm\) 0.040 \({}^{\rm Rc}\) & 0.390 \(\pm\) 0.092 \({}^{\rm Bbc}\) \\ \hline G2 & 0.142 \(\pm\) 0.006 \({}^{\rm Aa}\) & 0.520 \(\pm\) 0.098 \({}^{\rm Ab}\) & 0.258 \(\pm\) 0.039 \({}^{\rm Rc}\) & 0.455 \(\pm\) 0.110 \({}^{\rm Bb}\) \\ \hline \hline \end{tabular} \(\mu\)m, square micrometer; 5a, roughness parameter values of area.\({}^{*}\) = Sa are expressed as mean values \(\pm\) standard deviations; N = 8 specimens per group. Means followed by the same superscript capital letter indicate no statistical differences in column (\(p>0.05\)); means followed by the same superscript lowercase letter indicate no statistical differences in row (\(p>0.05\)).  
\end{table}
Table 2: Surface roughness parameter obtained by means of profilometry at the different stages of the experimental protocol.  
Figure 3: SEM assessment of the changes in surface appearance of the three groups (G0, G1, G2), during the different experimental stages (T0, T1, T2, T3). Scale bar 10 \(\mu\)m.
## 4 Discussion
### Discussion on False Alarm Experimental Results  
This section discusses the results obtained in Section 3.2.2.  
It can be seen from Figures 14 and 15 that the calculated values are very concentrated around the expected value, and the error between the two fluctuates within a small range. The calculation error of G09 is slightly larger than that of the G04 satellite, which is caused by the lower elevation angle of G09. The observation error accuracy is related to the elevation angle, and a lower elevation angle may lead to an increase in the observation error. In extreme cases, wrong calculations may even occur, and a reasonable elevation angle threshold can be set when the number of satellites is sufficient. There are two possible reasons for the larger observation errors at lower elevation angles. One is that the lower elevation angles lead to lower arrival power and poorer signal quality of satellite signals. The second is that the antenna layout of the planar array limits the accuracy of DOA estimation. From Tables 2 and 3, it can be seen that the average error and standard deviation of the elevation and azimuth angles are small, the data results are stable and reliable, and the maximum error is within an acceptable range. It shows that the DOA calculation method proposed in this paper is effective and reliable.  
In Figure 16a,b, we define two status tags. We mark it as \"Real\" when the SSE test metric is smaller than \(SSE_{th}\). Otherwise, we mark it as \"Spoof\" when the SSE test metric is larger than \(SSE_{th}\), indicating that at least one spoofing signal exists. The detection threshold \(SSE_{th}\) for spoofing is related to the number of satellites and the chosen false alarm rate \(P_{FAR}\). In this paper, \(P_{FAR}\) is \(1\times 10^{-3}\). It can be seen from Figure 16a,b that the SSE test metric in each epoch is smaller than the threshold. The state tag of all epochs is judged to be \"Real\", which is consistent with the actual situation. This shows that the calculation method and the selection of \(SSE_{th}\) in this paper are reliable.
# Power Control Techniques for Interference Management-
## 4 Power Control Technique Evaluation
### Power Control Theories
The benefits are reliable connectivity and minimal power consumption in wireless communication networks. In [144], an evolutionary game theory model is proposed to adaptively adjust the transmitted power level of the users to mitigate interference among users in 5G networks. The proposed technique achieves higher efficiency regarding network spectral and energy efficiencies. Furthermore, the authors in [145] modelled non-cooperative power control in WCDMA and OFDMA systems as evolutionary games to study interference among a dense network of mobiles. Machine learning [146] techniques are suitable in a dynamic wireless environment. When applied with deep learning techniques, wireless networks can perform intelligent operations such as finding hotspots, locating interference distribution, and identifying traffic congestion points [146]. These techniques are widely discussed in [147; 148]. For instance, ref. [149] used a multi-agent _deep reinforcement learning_ approach to implement distributed resource management and interference mitigation mechanisms in wireless networks. The simulation results show that the suggested strategy is superior to decentralized models. The results were compared in terms of the average and fifth-percentile user rates.
# Fault Diagnosis of a Reconfigurable Crawling-Rolling Robot Based on Support Vector Machines
###### Abstract  
As robots begin to perform jobs autonomously, with minimal or no human intervention, a new challenge arises: robots also need to autonomously detect errors and recover from faults. In this paper, we present a Support Vector Machine (SVM)-based fault diagnosis system for a bio-inspired reconfigurable robot named Scorpio. The diagnosis system needs to detect and classify faults while Scorpio uses its crawling and rolling locomotion modes. Specifically, we classify between faulty and non-faulty conditions by analyzing onboard Inertial Measurement Unit (IMU) sensor data. The data capture nine different locomotion gaits, which include rolling and crawling modes, at three different speeds. Statistical methods are applied to extract features and to reduce the dimensionality of original IMU sensor data features. These statistical features were given as inputs for training and testing. Additionally, the c-Support Vector Classification (c-SVC) and nu-SVC models of SVM, and their fault classification accuracies, were compared. The results show that the proposed SVM approach can be used to autonomously diagnose locomotion gait faults while the reconfigurable robot is in operation.  
fault diagnosis; machine learning; Support Vector Machines; statistical features; reconfigurable robotics +
Footnote †: journal: Applied Sciences
## 5 Experimental Results
### Point Spread Function Performance According to the Spectrometer  
The difference in SD-OCT imaging performance due to the aberration of the imaging lens was more pronounced in roll-off performance than in the axial resolution. The maximum image depth was about 2.8 mm for the S-ADL and 3 mm for the S-TFL. The distance per single fast Fourier transform (FFT) bin was calculated using a gauge block and each spectrometer had values of 2.8 \(\upmu\)m/bin and 3.0 \(\upmu\)m/bin. Because of the telecentric nature of the TFL, the spectrum in the S-TFL may be measured over a relatively larger number of pixels than that of the S-ADL, which has a curved focal plane. A series of PSFs were measured at 300 \(\upmu\)m intervals up to 3 mm and the results are shown in Figure 9. Normalization was based on the maximum amplitude among the PSFs. At the OPD at 2.5 mm, the axial resolutions for the S-ADL and the S-TFL were 12 \(\upmu\)m and 7.9 \(\upmu\)m, respectively. The S-TFL maintained better axial resolution than S-ADL at the deep imaging depth.
# Nanofiber-Based Materials for Persistent Organic Pollutants in Water Remediation by Adsorption
## 2 Moderately Hydrophobic Pesticides
### Nanofibers for Pesticide Adsorption
#### 2.2.2 Impact of Adsorbent Pore Size and Pore Volume  
Figure 3 shows adsorption capacity related to the adsorbent pore volume in the removal of Atrazine. A global trend of increasing capacity for an increased pore volume up to 1.1 cm\({}^{3}\)/g can be noticed (Table 4, \(N9\)). This overall trend may be explained by the higher number of atrazine molecules diffusing in a higher number of surface pores across larger pore channels. However, several factors can imply lower performance for a higher pore volume. Adsorbent surface pore size is a critical factor for pesticide diffusion. For instance, surface pores twice as large as the adsorbate molecule cross section could still allow pesticide diffusion if one molecule was adsorbed along one pore wall, and could even allow two molecules to adsorb side to side. Yet, a larger pore channel would not favour the adsorption along pore walls due to a lower promiscuity. Furthermore, a higher pore volume could also translate to a higher proportion of pores with a smaller size distribution falling below the pesticide molecule size or cross section, hence hindering the adsorbate diffusion. Also, dead-end pores would affect the determination of pore volume. Nevertheless, the overall trend indicates that a pore volume around 1.0 \(\pm\) 0.1 cm\({}^{3}\)/g is preferential for increased performance in the removal of Atrazine.  
Concerning the adsorbent pore size, not enough data were found in the literature to enable a study of a preferential pore diameter for a specific pollutant among the ones considered in this section. In the case of Alachlor adsorption, organosilane-grafted beideillite clay and alumina nanofibers were reported to have a pore diameter of 6.44 nm and 11.4 nm, respectively, and showed an efficiency of 80% and 58% (Table 2, \(N1\), \(N5\)) [28, 29]. These results may suggest that an 11.4 nm wide pore channel does not offer enough promiscuity for the adsorption of Alachlor molecules, combined with a possible decrease in the efficiency of silane grafting along the adsorbent pore walls, thus impeding adsorption  
Figure 3: Adsorption capacity (mg/g) related to the adsorbent pore volume against Atrazine.  
Figure 2: Adsorption capacity (mg/g) related to the adsorbent diameter against Atrazine.  
via hydrophobic interactions. Indeed, organosilane compounds considered for alumina nanofiber grafting include CPTES (3-chloro-propyl-triethoxysilane) and OTES (octyl-triethoxysilane), both of a weight comparable to Alachlor, 241 g/mol and 277 g/mol, of single-molecule orientation and longer carbon chain.
# Difference-Based Mutation Operation for Neuroevolution of Augmented Topologies +
## 2 Materials and Methods
### Encoding Scheme Used in NEAT  
One of the core concepts of NEAT, which makes it different from many other approaches, is the initialization procedure. NEAT initializes all the structures to be minimal, i.e., only inputs and outputs are connected, and the weights are assigned randomly. All the further search is performed by increasing (augmenting) the structure of the network. This growth is performed by mutation and crossover operators. To encode an individual NEAT uses two sets: a set of nodes and a set of connections, where each element has several properties. An example of encoding in NEAT for a problem with two input variables and one output is shown in Figure 1.  
The upper part of Figure 1 shows the nodes and connections, where numbers are assigned to each of them. The node with number 57 is added after mutation, and it is the 57-th innovation since the beginning of the search process. Here, only the hidden node has some specific operation, while all others perform no transformation on the available data, and in node 57 the input values are squared. The connection genes also have innovation markers, which are assigned during mutation operations. Each connection node keeps the source and destination nodes, marked by innovation numbers, as well as weight  
Figure 1: NEAT encoding example.  
coefficient. In addition, the activation flag is set for every connection to allow simple deactivation of them. The three added connections have innovation numbers 84, 107 and 152, and connection 107 makes a loop, i.e., it connects the output from node 57 to itself. The lower part of Figure 1 shows the graphical representation of the encoding as a directed multigraph.  
The described encoding scheme, proposed in [9] allows minimal starting for all networks, and thus performs the search in the space of small dimensionality, which is different from TWEANNs or genetic programming (GP), where solutions could be quite complicated even at the first generation. The structure size has no strict limitations in terms of the number of nodes and connections, making NEAT an algorithm with varying chromosome length, which requires specific mutation and crossover. A specific output estimation procedure should be used, for example, in this study the output calculations are repeated until all reachable nodes are activated and the signal is propagated to the outputs. The next section contains the description of mutation and crossover operators used for NEAT in this study.
# Heterologous Production of Flavour and Aroma Compounds in _Saccharomyces cerevisiae_
## 3 Heterologous Compounds Derived from Intermediates of the Mevalonate Pathway
### Isoprenoids
#### 3.1.2 Sesquiterpenes
As production of squalene is essential for the survival of yeast, they implemented elegant system in which the degradation of the native Erg9 protein was enhanced, thereby decreasing its cellular half-life and enzymatic activity and increasing the production of _trans_-nerolidol by 86% (105 mg/L) [79; 80].