esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Result Analysis and Comparative Discussions
### Performance Analysis of the Feature Selector Boruta  
Raw AE signals have no intrinsic information to reveal different health conditions. Therefore, the HBoF is designed and Boruta is applied to get the most intrinsic feature information. From Boruta, the six most important features are calculated (i.e., F1, F3, F4, F5, F10, and F11). These six features are collected from AE analysis, time domain, and frequency domain. Figure 6 demonstrates the Boruta-features in a 2D feature space. For the explanatory purpose, the selected six features are first constricted into two-dimensional space by principle component analysis (PCA) and then mapped into Figure 6.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Health Condition**} & \multicolumn{3}{c}{**Crack Size (mm)**} & \multirow{2}{*}{**Channels**} & \multirow{2}{*}{**Number of Samples**} \\ \cline{2-2} \cline{5-6}  & **Length (mm)** & & & **Width (mm)** & **Depth (mm)** \\ \hline Normal Condition (NC) & N/A & N/A & N/A & 4 & 400 \\ Faulty Condition 01 (FC1) & 3 & 0.5 & 0.4 & 4 & 400 \\ Faulty Condition 02 (FC2) & 6 & 0.7 & 0.5 & 4 & 400 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Details of the considered dataset.  
Figure 5: Block overview of k-NN algorithm.
# Fault Analysis of Shearer-Cutting Units Driven by Integrated Importance Measure
## 2 Fault Analysis Method of Shearer-Cutting Unit Driven by IIM  
To better analyze the key fault of shearer-cutting units based on the IIM, the process of the fault analysis method can be summarized as follows, and the framework of the fault analysis method based on IIM is shown in Figure 1.  
* Structure analysis of shearer-cutting units: analyze the composition of cutting units and explore the functions of key subsystems.
* General fault identification of shearer-cutting units: determine the failure causes through the failure phenomenon and determine the location of the failure.
* Fault tree construction of shearer-cutting units: determine the top event, middle events, and bottom events; establish the logical relationships among these events; evaluate the occurrence probability of bottom events.
* The key fault causes analysis of shearer-cutting units: quantify the fault causes based on IIM and rank the fault causes to determine the key ones.
* Maintenance decision implementation based on IIM ranking: under the limited maintenance resources, the fault with higher IIM should give priority to performing maintenance.
## 3 Cognitive Mobile BTS
### Spectrum Sensor Module  
The SSM explores the frequency bands of interest (the GSM bands) to detect active radio-frequency transmissions. In GSM communications, each band is duplexed into two different sub-bands, for uplink (phone to BTS) and downlink (BTS to phone) traffic. The BTS transmits control information in the downlink, which is in turn subdivided into different channels, each identified by an ARFCN (Absolute Radio Frequency Channel Number). The number of channels varies depending on the GSM band. For example, GSM1800 has 374 channels (512 to 885) and GSM900 has 124 (1 to 124).  
Several possibilities for spectrum sensing are described in the literature [30], such as energy detection sensing, waveform-oriented sensing, cyclostationarity-based sensing, radio technology identification-based sensing, multitaper spectral estimation, wavelet transform-based estimation, Hough transform or time-frequency analysis. For our approach we have chosen energy detection based on  
Figure 3: System behavior.  
periodogram computation, since it is a low complex method that can be easily implemented on SDR systems, and has been reported previously to effectively sense GSM spectrum [31].  
Thus, the SSM provides accurate information about activity detected in downlink channels (_i.e._, in each ARFCN), tagging them as busy or idle, in order to select candidate channels for communications. In the next step, the resulting information (a list sorted by channel energy level) is provided to the DM.  
The SSM consists of the following sequence of processing blocks (Figure 4):  
1. USRP source: provides I/Q baseband samples from the band of interest.
2. DC removal filter: removes undesired DC signals generated by the USRP.
3. Frequency response flatness processing: equalizes the incoming signal.
4. \(N\)-point FFT: calculates the \(N\)-point Fast Fourier Transform of the incoming data.
5. Squared magnitude: calculates an estimate of bins power.
6. Bin average: averages successive bin blocks of GSM channels.  
Once the DM is launched, it activates the SSM, which senses the spectrum. The number of bins of the Fast Fourier Transform (FFT) block determines sensing resolution. The maximum sensed bandwidth per snapshot will generally depend on hardware capacity, and will typically be less than the total target bandwidth. For example, in order to cover the whole GSM900 downlink, the DM must launch the SSM several times, changing the center frequency and storing the result for all the corresponding sub-bands, until the whole 25 MHz bandwidth has been swept. Once the complete band of interest has been scanned, the DM stops the SSM and the collected information is processed. The different bins are grouped by ARFCN. From each group, the system estimates the average power of each GSM downlink channel. When the sensing stage is complete, the detected carriers are sorted by measured power.
## 2 Materials and Methods
### Calibration Curves  
The standard solutions were serially diluted to produce calibration curves. The area of the calibration curve's standard peak was indicated by the Y-axis values (AU), while the X-axis values (\(\mu\)g/mL) represented the standards' concentration. The calculation of the total content of the standards (mg/g) involved multiplying the concentration of the standard (C), the total volume of the test solution (V), the dilution factor (D), and the standard purity (P), then dividing the result by the sample weight (W).
## 3 Q2: What Are the Challenges of the Current UIoT System
### Technical Challenges of UioT
#### 3.2.5. Dynamic Topology  
Node mobility was discussed in Section 3.3.2. As shown in Figure 3, the UUVs and mobile nodes are automatically moving from one place to another or by external forces. Node mobility can form a new topology by modifying the existing topology. Therefore, node mobility is the major cause of dynamic topology formation in UIoT networks. This causes routing problems in the UIoT environment [40].
# The Potential of LEO Satellite-Based Opportunistic Navigation for High Dynamic Applications
## 4 Performance Characterization
### Scenario Description
#### 4.1.2 Inertial and Magnetometer Data  
The Balco software provides IMU and magnetometer data consistent with the six DOF trajectory of the vehicle. We used the following error model [30] to perturb the IMU and magnetometer data  
\[\widetilde{\boldsymbol{y}}=(\boldsymbol{I}_{3}+\boldsymbol{M}_{3})\times \boldsymbol{y}+\boldsymbol{b}_{\boldsymbol{y}}+\boldsymbol{n}_{\boldsymbol{y}} \tag{32}\]  
where \(\boldsymbol{y}\) is the \(3\times 1\) true sensor output in the body frame, \(\boldsymbol{M}_{3}\) is a \(3\times 3\) matrix that represents the misalignment error (off-diagonal terms) and sensitivity error (diagonal terms), \(\boldsymbol{b}_{\boldsymbol{y}}\) is the \(3\times 1\) sensor bias and \(\boldsymbol{n}_{\boldsymbol{y}}\) is the \(3\times 1\) sensor noise (assumed zero mean Gaussian). The sensors errors (\(1-\sigma\)), considered of consumer-grade, are given in Table 1. We generated two hundred inertial and magnetometer data files by changing randomly the sensors error values.
# Evaluation of Snowboarding Helmets in Mitigation of the Biomechanical Responses of Head Surrogate
## 2 Materials and Methods  
### Head Surrogate  
The blunt impact experiments were conducted using a head surrogate. The head surrogate (Figure 1) consisted of skin, skull, dura mater, and brain. The materials for each head constituent were chosen to reasonably match the density and elastic/shear modulus of the corresponding human head substructures (Table 1). These properties govern the dynamic response. The geometry of the head surrogate was based on the Global Human Body Model Consortium's (GHBMC) 50 th percentile adult male, version 1.5 [39; 40; 41]. The head surrogate was integrated with the Hybrid III neck (Humanetics, Farmington Hills, MI, USA) using a target holding fixture (Figure 1a). The head-neck assembly had a total mass of 5.5 kg.  
Figure 1: (a) Photographs of the 3D-printed head surrogate integrated with Hybrid III neck (the skin is not shown for the clarity of the photographs), (b) a midsagittal view of the head surrogate for the visualization of the skin, skull, dura mater, and brain simulant.  
Silicone (shore hardness: 30A, Dragon Skin(r) Smooth-On, Inc., Macungie, PA, USA) was used as a skin simulant. To prepare the skin simulant, two-part silicone was mixed in a 1:1 ratio (by weight) and poured into the skin mold cavity. The mixture was cured at room temperature for 24 h. The thickness of the skin simulant was \(\sim\)8 mm. The skull and the dura mater were 3D-printed using a BCN bilayer 3D printer with an infill density of 100%. Polylactic acid (PLA) and thermoplastic polyurethane (TPU) were chosen to mimic the skull and dura mater, respectively. The thickness of the skull and dura mater were \(\sim\)10 mm and \(\sim\)2 mm, respectively.  
Gelatin (10% by weight) was used as a brain simulant (Figure 1b). Following the protocol proposed by [PERSON] et al. [42], the brain simulant was prepared by mixing 10% of gelatin powder (bloom strength of 250) with 90% of distilled water. The gelatin-water mixture was heated at 80 \({}^{\circ}\)C. To prevent clotting, the gelatin was continuously stirred during the preparation. The gelatin was allowed to settle at room temperature and kept in a vacuum chamber to remove air bubbles (if any). The gelatin was then poured into the skull cavity. Note that pressure sensors (described in Section 2.4) were secured at the desired position before pouring the gelatin into the skull cavity. The skull-brain assembly was kept in the refrigerator and gelatin was allowed to solidify at 4 \({}^{\circ}\)C for 24 h. Prior to the experiments, the skull-brain assembly was pulled out from the refrigerator, and the skin simulant was securely fitted to the skull-brain assembly using glue. In many blunt and ballistic investigations [43; 44; 45], 10% gelatin has been widely used as a brain simulant.  
The head surrogate was validated by comparing the experimentally measured brain simulant pressures of the head surrogate against the intracranial pressures measured in post-mortem human subjects [46]. The details of this comparison are provided in the Supplementary Material.
# Designing a Reliable UAV Architecture Operating in a Real Environment
## 4 Collision Avoidance Algorithm
### Modifications to the Collision Avoidance Algorithm
#### 4.3.1 Time and Distance Limitations  
The original algorithm does not consider the problem of detecting collisions of two UAVs at very long distances (Figure 8). If we consider the situation of one UAV flying in a straight line, while the other UAV turns with a constant turning radius around a single point very far from the first UAV but of the same altitude, the algorithm has a high probability of detecting a collision. This indicates a need to define an additional set of conditions to limit launch cases for emergency situations.  
The following additional condition (5) is proposed as a solution to this issue.  
\[\begin{split}\|r\|<(\max\left(UAV_{A}.r_{safe}+UAV_{B}.r_{safe} \right),\\ \Delta T\cdot\left(\|V_{A}\|+\|V_{B}\|\right))\right),\end{split} \tag{5}\]  
where \(k_{mar}\) is the safety margin coefficient, proposed value is \(k_{mar}=2\), \(\Delta T\) is assumed time of safety margin, proposed value is either 60 [s] or twice the time required for \(180\left[\deg.\right]\) turn of the UAV. These proposed values are suggested minimal values. Higher distances will increase the number of algorithm launches and the smoothness of avoidance maneuvers.  
566 To further limit the number of calls on the systems, additional conditions regarding time (\(\tau\)) and distance (\(V_{A}\cdot\tau\)) to the collision can be added after the first calculation steps of the algorithm. A very long time to collision may suggest that no intervention is needed. However, the distance between UAVs must be monitored as there is a possibility that they will be very close and running in almost parallel straight lines. This can break safety zones, especially for airplanes with minimal vertical speed or low-altitude flight situations where lowering the flight level is impossible.  
Figure 7: Example of the working principle of the algorithm with visualization of a dynamic environment. The red circles show the UAV’s safety zone. The numerical values in the figures show the height of each UAV. (**a**) First detection of a long-distance collision of two UAVs flying in almost parallel trajectories. (**b**) Slight target waypoints adjustment after 30 iterations. The trajectories are corrected for the first time. (**c**) The end of the collision avoidance maneuver with visible target points. A change in the trajectory of each UAV is presented.
# Toward Integrated Satellite Operations and Network Management: A Review and Novel Framework
## 2 State of the Art
### Communication Constellations Architectures
#### 2.1.1 Sporadic Connectivity Architectures  
The potential for extending terrestrial IoT networks with satellite-based NTN is a relevant topic in the literature. A general reference IoT integrated architecture is presented in [25]. The main identified challenges for the integration of satellite-based communications with IoT technologies are: (1) routing in sparse LEO constellations [26], (2) the heterogeneous infrastructure, (3) mobility management, (4) core functionality placement, and (5) compliance with 5G standard architectures.  
The challenges of an interrupted connection between UE and eNB when using 3 GPP regenerative payloads are presented in [27]. The design of a fully functional NB-IoT over satellite for a smart agriculture scenario is addressed, and the NB-IoT technology and standardization efforts of 3 GPP on NTN are further detailed. The viability of NB-IoT with a low density LEO CubeSat constellation at link budget level and architecture is studied in [28; 29], with the latter presenting a testbed for NB-IoT over satellite. The studied scenarios use a regenerative payload and a distributed core network. The necessary adaptations for these scenarios are outlined: cell detection, frequency synchronization, physical rate, random access, timers, paging occasions, and discontinuous feeder link, among others.  
In [30], authors present a testbed for nanosatellite communication constellations to evaluate a SaF communication network. This testbed platform is envisioned to support the research and development of autonomous constellation management, including scheduling and routing algorithms. Finally, a general architecture for a network control system with satellites is introduced in [31] at a theoretical level. They present the possible architectures and elements for the autonomous operation of a smallsat network. Key terms like asset profiling, heterogeneous systems, and the multi-costumer nature of the system are defined, and challenges of network control systems for upcoming constellations are identified(e.g., heterogeneity and multi-customer nature). The management of these architectures is further discussed in Section 2.2.1.
## Result and Discussion
### Thermal Stability
In addition, the improvements in thermal stability are related to the relatively strong interfacial interactions between the clay layers and the polymer chains for the PS-OMMT-3 sample due to existence of a large number of OMMT  
Figure 5: TGA curves of (**A**) MMT and (**B**) OMMT. single layers [40]. Meanwhile, the insufficient number of clay layers caused the presence of a barrier at low OMMT loadings (PS-OMMT-1) that could act as thermal insulators and enhance the thermal properties of the materials, although exfoliated structures are favorable at low OMMT contents. However, a slight decrease in T\({}_{10}\) and T\({}_{\text{max}}\) was observed in the PS-OMMT-5 sample. Excessive OMMT aggregation might have destroyed the effective dispersion and interaction between the clay layers and the PS matrix, resulting in a lower thermal stability [41]. Therefore, the thermal stability of the PS-OMMT nanocomposite was not only dependent on the clay loading but also closely related to the clay dispersion and degree of exfoliation, and the interactions between the OMMT and PS matrix also played an important role in improving the thermal performance [42; 43].
## 1 Introduction
Figure 1 presents some examples of segmented tide gauge records originating from perturbing events that occurred during the recording of the same tide gauge in the same location. In these cases, crustal movements, in the specific earthquakes, have affected the instrument reading. In Figure 1a is the MSL of Seward, AK, with no correction introduced to account for an earthquake in the mid-1960s that moved up the instrument. The correct rate of rise [16] is \(-0.11\) mm/yr pre-earthquake, and \(-1.74\) mm/yr post-earthquake. The image suggests above \(+14\) mm/yr of the rate of rise. This issue has been now corrected in the PSMSL database. In Figure 1b is the MSL of Apia B, Samoa. The effect of the earthquake of 2011, shown by [7], is still unaccounted for in the PSMSL database. [7] revealed that the high rate of rise and high acceleration are only artifacts of the crustal movement connected to the earthquake. The almost \(+9\) mm/yr sea level rate of rise is still used as proof that the sea level is rising faster in the Pacific Islands. This rate of rise practically vanishes to zero when the crustal motion is accounted for. Figure 1: _Cont._  
Figure 1c,d shows the MSL of Guam. The effect of two earthquakes is clear--one in the early 1990s, the other, which was much larger, at the end of the 1990s. However, NOAA placed a breakpoint about the first earthquake, the one with minor effects, and forgot the second one. By considering the short record starting from the time of the first earthquake, and ignoring the effect of the second earthquake, NOAA claim for Guam prior to the first earthquake a \(-1.05\) mm/yr sea level rate of rise, and post the first earthquake a \(+8.58\) mm/yr. [16]. This is also almost \(+9\) mm/yr. The sea level rate of rise has been used as proof the sea levels are accelerating. Accounting for the effect of both earthquakes, the sea levels of Guam are also stable, as shown in [17]. Figure 1 explains how the problem of segmented records is widespread, also affecting records originated by the same tide gauge in the same location. The aim of this work is to use breakpoints alignment techniques like those adopted for the global positioning system (GPS) time series [18; 19; 20] to detect suspicious alignments of segmented tide gauge  
Figure 1: Sample segmented tide gauge records available in databases of tide gauges. MSL (mean sea levels) of Seward (**a**), Apia B (**b**), Guam (**c**,**d**). Images (**a**) to (**c**) are reproduced and modified from www.sealevel.info. Image (**d**) is reproduced and modified from tidesandcurrents.noaa.gov. records. Data mining and consistency with complete non-segmented tide gauge records are also used to address the misalignment issues. The case study selected to test the procedure is the Indian Ocean.
# Secure Outsourced Blockchain-Based Medical Data Sharing System Using Proxy Re-Encryption +
## 5 Discussion
### Security Analysis  
In this subsection, we analyze the proposed system with respect to security. When the EHR is created at the hospital, as shown in Figure 3, it is encrypted, and the key used for that encryption is encrypted with the manager's public key; hence, the path between the hospital and the server can be regarded as secure. Next, we discuss the case in which a patient and a third party require the EHR, where the privacy-sensitive record may be leaked to the outsourcing company.  
First, we consider the case in which a patient or a third party requests an EHR to the server. Initially, when the EHR is stored on the server, information that allow access to the EHR is transmitted and saved in the blockchain. Moreover, the contents in the blockchain cannot be changed, and hence the access information cannot be modified. Therefore, the EHR is delivered to users who can access the record. In addition, owing to the proxy server, the encrypted symmetric key is transformed to one that can be decrypted by the requested data without decryption; hence, the plaintext of the symmetric key or the EHR will not be exposed between the file server and the end user.  
In the Introduction, we argued that the EHR can be leaked to the outsourcing company because the company may disregard the smart contract in the blockchain. To prevent this in our system, we adopted the PRE scheme; therefore, the symmetric key is not exposed to the outsourcing company because it can be decrypted by either the system manager or end user. Nonetheless, the system manager should be separated from the blockchain outsourcing company.
# Repurposing Historic Drugs for Neutrophil-Mediated Inflammation in Skin Disorders
## 3 Colchicine
### Mechanism of Action  
Colchicine exerts its effects primarily by inhibiting microtubule polymerization, which interferes with cell division and modulates intracellular signaling pathways (Table 2). This mechanism is particularly impactful on rapidly proliferating cells, such as those in the bone marrow and gastrointestinal tract. Colchicine preferentially accumulates in leukocytes, especially neutrophils, due to their low expression of P-glycoprotein [57] and inhibits neutrophil activation, degranulation, and migration, thereby reducing inflammation [58]. Colchicine also has antifibrotic effects, which may be beneficial in conditions involving fiber deposition, such as scleroderma and cirrhosis [59].  
Colchicine demonstrates good oral bioavailability, with absorption occurring primarily in the jejunum and ileum. Peak plasma concentrations are achieved approximately 2 h post-administration, and the drug has a half-life of about 4 h [60]. Its lipophilic nature facilitates effective absorption. Colchicine is metabolized in the liver by cytochrome P450 3A4 (CYP3A4) and is predominantly excreted via biliary and fecal routes, with approximately 10-20% of the drug eliminated through the kidneys. Colchicine should not be administered in patients with a creatinine clearance rate below 10 mL/min and in those with severe hepatobiliary dysfunction. Colchicine is generally well-tolerated at moderate doses, but higher doses are associated with a significant increase in gastrointestinal side effects, including diarrhea, nausea, and vomiting. Less common adverse effects include myelosuppression, myotoxicity, alopecia, and hepatotoxicity [61; 62].  
In dermatology, colchicine has been employed in both oral and topical forms for various indications. Although the FDA has approved colchicine solely for managing acute gout attacks and familial Mediterranean fever [57], it has shown efficacy in treating various dermatological conditions, especially those involving neutrophil inflammation. Nevertheless, the supporting evidence for most of these alternative uses is inconsistent, with the notable absence of RCTs to firmly establish their effectiveness.