esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Evaluation of Constellations  
The results from the individual sensor experiments illustrate how no single sensor can adequately measure all types of snow at all locations across the hemisphere. That is, it is clear that a constellation of different sensors is required to achieve this goal. Table 7, therefore, illustrates the effective coverage of the tested constellation cases.  
The shortcomings of any single sensor are compensated for by the other sensors in the constellation. For example, the PMW sensor obtains a regular, short duration repeat of observations of the tundra, prairie, and ephemeral snow classes, while the SAR sensors can collect information regarding maritime and alpine snow. Further, the LiDAR sensors help provide important information about snow with overlaying vegetation. Although the viewable area is limited, the LiDAR information could potentially help cross-calibrate other sensor retrievals in other areas. The comparison between constellation (a) and (b) shows the impact on viewing coverage by replacing two C-band SARs with one Ku-band SAR. The constellation containing two C-band SARs achieves more coverage within a 1-day integration period but less coverage within 3-day and 30-day integration periods as compared to the constellation containing one Ku-band SAR. The difference in viewing coverage between the constellations are not significant, but the higher quality and relatively fine-resolution observations from the Ku-band SAR could potentially improve the snow retrieval quality of the tundra, prairie, and ephemeral snow classes. With a wide-swath LiDAR, constellation (c) viewed a larger portion of snow in the taiga regions relative to constellation (b). In constellation (d), all candidate sensors are introduced; hence, the viewing coverage is maximized.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & & \multicolumn{4}{c}{**Snow Class**} \\ \hline
**Sensor ID** & **Tundra** & **Taiga** & **Maritime** & **Ephemeral** & **Prairie** & **Alpine** \\ \hline PMW & 1.03 & - & - & 1.54 & 1.31 & - \\ Ku-band SAR & 2.12 & - & 3.40 & 4.61 & 3.92 & 3.07 \\ C-band SAR & 4.15 & - & 6.76 & 9.11 & 7.77 & 6.08 \\ Wide LiDAR & 64.1 & 61.3 & 179 & 75.1 & 93.1 & 85.0 \\ Narrow LiDAR & 172 & 159 & 450 & 172 & 221 & 210 \\ Low-inclination LiDAR & 390 & 400 & 332 & 136 & 138 & 292 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Domain-average repeat intervals (in units of days) for different snow sensors as a function of snow class and sensor efficacy (see Table 3).
## 3 Verification of the Implemented Model
### Zimmermann Method Verification  
The Zimmermann method is a one-dimensional analytical method of a railway structure that simplifies a beam supported by continuous support (soil's subgrade or foundation) to analyze the track deflection. This method is still extremely effective for assessing a simple design and analysis of railway track systems. Unfortunately, this method cannot do a complex analysis as it is only considered a one-dimensional system and ignores the actual discrete support supplied by sleepers and the substructure elements. Nowadays, researchers and engineers depend on numerical analysis and have, thus far, used the Zimmerman method as a reference. Figure 7 shows the deformation of the modeled rail due to the effect of the wheel load. The deformation results were verified and compared with the Zimmermann method. The Zimmermann method was developed for situations when the wheel was directly loaded between two adjacent sleepers. However, it was developed in a static state, without wheel motion, where the symmetry of the deformed rail can be seen before and after the wheel load, as shown in Figure 8. Hence, Equation (1) was used to get the deflection values (\(w\)) of the rail under the effect of the loaded wheel [32; 36]. Table 3 introduces the calculations of the rail deflection depending on Equation (1), starting from the wheel/rail contact point (\(x=0\)) until the last point (\(x=6000\) mm):  
\[w(x)=\frac{QL^{3}}{\delta E_{r}I_{x}}*\eta\left(x\right) \tag{1}\]  
where \(Q\) is the wheel load, \(L\) is the length of transverse sleeper, \(\eta\) is the influence factor of deflection, \(E_{r}\) is the elastic modulus of rail, \(I_{x}\) is the moment of inertia of the rail, and \(x\) is the distance from the point of wheel/rail contact. The FE model results of rail deflection, describing the dynamic effect of the wheel, were compared with the calculated values from the Zimmermann method in Figure 8. In the FE model, the maximum value of deflection was registered to be 2.3 mm; however, the maximum value of deflection owing to the Zimmermann method was registered to be 2.241 mm. Consequently, there is a high degree of agreement between the Zimmermann method and the FE model--97.3%. Note that the element size of the mesh has a basic  
Figure 8: Comparison of the deflection between the Zimmermann method and FEM.
# LoRa, Zigbee and 5G Propagation and Transmission Performance in an Indoor Environment at 868 MHz
## 5 Measurement Results and Discussion
### BER Measurements and Relation with the FI Path Loss Model
#### 5.2.2 Zigbee BER  
Following a similar procedure to the previous one but with the BER value measured with Zigbee, four vectors can be composed in the same way, where the abscissa axis is the received power predicted by the model for a specific point, and the ordinate axis is the empirical Zigbee BER measured at that point. This relationship between the predicted power for each model and the measured Zigbee BER is shown in Figure 16. It is observed that to have a Zigbee BER higher than \(5\%\), it is necessary to have a power less than \(-56\) dBm in narrowband transmission, \(-94\) dBm of LoRa RSSI, \(-97\) dBm of Zigbee RSSI and \(-98\) of SS-RSRP. The second row of Table 4 shows these results.
# PUF and Chaotic Map-Based Authentication Protocol for Underwater Acoustic Networks
## 5 Informal Security Analysis
### Mutual Authentication  
In our scheme, only the legitimate user with the correct identity and password can pass the verification. In the authentication and key agreement phase, \(U_{i}\) transmits message \(\{K_{u},M_{5},T_{1}\}\) via the public channel, and only \(GWN_{k}\) can recover the encryption key \(K_{U-G}\) to decrypt \(M_{5}\) and obtain \(\{id_{i},\ Sid_{j},\ rug,M_{4}\}\). If \(GWN_{k}\) verifies \(M_{4}\) successfully, the user can authenticate \(GWN_{k}\) by checking the correctness of \(M_{12}\), so our scheme achieves mutual authentication between \(U_{i}\) and \(GWN_{k}\). In the same way, \(GWN_{k}\) transmits the encrypted data \(M_{7}\) to the sensor node, and only the sensor node can decrypt the message and verify the correctness of \(GWN_{k}\) to achieve mutual authentication between \(GWN_{k}\) and \(Sn_{j}\). Thus, it could provide mutual authentication among the user, the gateway, and the sensor node.
## 2 Methodology
### IPSO-LSSVM Model
#### 2.2.4 Establishment of the IPSO-LSSVM Model  
In this paper, the radial basis kernel function was selected as the kernel function of LSSVM. Therefore, the regularization parameter \(\gamma\) and the kernel width parameter \(\sigma\) are two important hyper-parameters of the LSSVM model. \(\gamma\) controls model complexity and penalties for exceeding errors. \(\sigma\) affects the training and prediction speed of the model. The IPSO algorithm was used for hyper-parameters tuning of the LSSVM model, and the IPSO-LSSVM model was established.  
The steps were as follows:  
Step 1: Divide dataset into training set and testing set in a certain proportion.  
Step 2: Normalize the data of the training set and testing set.  
Step 3: Set the parameters of the IPSO algorithm.  
Step 4: Tune the \(\gamma\) and \(\sigma\) using the IPSO algorithm.  
Step 5: Input the hyper-parameters into the LSSVM model and establish the IPSO-LSSVM model.  
Step 6: Evaluate the accuracy of the IPSO-LSSVM model.
# Study on Local Vibration Control of the 100 m X-BOW Polar Exploration Cruise Ship
###### Abstract  
A finite element model of a 100 m X-BOW polar exploration cruise ship has been developed. The ship's frequency response analysis was conducted, with the simulated results closely matching the test data. The maximum discrepancy was 22%, equating to a negligible 0.24 mm/s difference in terms of comfort. This indicates that the simulation meets the standards of engineering precision and validates the model's accuracy. Utilizing a global modal equivalent mass solution approach, in conjunction with the spatial distribution of local modal mass, a method for calculating the equivalent mass of a single local mode in mixed modes has been devised. This method was applied to determine the equivalent mass of the local vibration region of the 100 m X-BOW ship. Tuned mass dampers (TMDs) were then designed based on this equivalent mass. Analysis reveals that the TMDs achieve a 31 dB vibration absorption effect at a frequency of 13.4 Hz with a mass ratio of 0.05. They also provide a control effect at 10 Hz and 18.8 Hz, corresponding to 3 dB and 2 dB reductions, respectively. The control frequency band is broad, flat, and robust, indicating the effectiveness of the TMDs in mitigating vibrations across a wide range of frequencies.  
polar exploration cruise; frequency response analysis; modal equivalent mass; tuned mass dampers; mass ratio +
Footnote †: journal: Applied sciences
# Q-Pandora Unboxed: Characterizing Resilience of Quantum Error Correction Codes Under Biased Noise
## 2 Overview of Quantum Error Correction  
### Fundamentals  
Quantum error correction (QEC) employs quantum error correction codes (QECCs) to safeguard quantum states from errors during computations or transmissions. QECCs disperse quantum information across qubits and work in three phases: encoding, detecting errors, and correction. This section delves into rotated and unrotated surface codes, their structures, and error management methods. We will also discuss the significance of error rates and the concept of a threshold in QEC. Some core principles of QECCs are as follows:  
1. First, Pauli operators (X, Y, Z) and their anti-commuting properties are fundamental for error identification and rectification. X and Z are anti-commuting, implying that \((X\otimes I)(I\otimes Z)=-(I\otimes Z)(X\otimes I)\). Consequently, error detection and correction hinge on this relationship, as X-type stabilizers are used to identify Z-type errors, and vice versa.
2. Second, ancilla (or helper) qubits help in detecting errors without measuring and potentially collapsing the quantum states of data qubits.
3. Third, syndromes, derived from ancilla qubit measurements, help pinpoint errors that specify the nature of the error and the affected qubits.
4. Fourth, a round in a QECC represents a full cycle of error detection and correction. Maintaining a balance between the number of rounds and the code distance is essential.
5. Finally, QECCs distinguish between 'logical' and 'physical' qubits. A physical qubit is the original state that is being encoded into multiple logical qubits to form a logical state. In general, QECCs are represented as \([[n,k,\delta]]\), where \(n\) is the number of logical qubits in the logical state post encoding, \(k\) is the original number of qubits, and \(\delta\) is the distance of the code.
# Middleware for Plug and Play Integration of Heterogeneous Sensor Resources into the Sensor Web
## 1 Introduction
### Related Work
It hides the specific technological details by matching sensor-specific commands to generic SAL commands. However, it still relies on a plug-in approach to integrate new sensors [23]. Similar to SAL, the Sensor Interface Descriptor (SID) model extends SensorML 1.0 to formally describe a sensor's protocol. The generated sensor interface description is used as a platform independent sensor driver which contains the necessary information to integrate a sensor on demand by translating between sensor protocol and Sensor Web protocols. A benefit of SID is the availability of open-source components to generate SID descriptions [24] as well as a SID interpreter middleware based on Java. Several applications of SID can be found at the literature [5; 25; 26]. However, a still remaining open challenge is to practically include such a universal approach in the Sensor Web middleware of marine observatory platforms. A Java-based middleware is a good approach to abstract on the operating system level, however, it may not suitable to be deployed in resource-constrained platforms. Furthermore, the SID model extension defines the whole Open Systems Interconnection (OSI) model stack for each sensor command, resulting in very verbose XML files, which are rather difficult to interpret. In this work the focus is put on semantic interoperability, emphasizing the need of standard middleware components compatible with different observation platforms. The rest of this paper is structured as follows: Section 2 discusses the requirements to achieve a true plug and play sensor integration into existing data infrastructures. Section 3 presents an SWE-based universal acquisition chain. Section 4 presents Sensor Deployment Files (SDF) as a standard way to organize all the metadata related to a sensor deployment and how it can enhance interoperability. In Section 5 the SWE Bridge middleware is presented, a universal plug and play sensor data acquisition middleware. Finally in Section 6 different use cases of the acquisition chain are presented.
## 5 Critical Aspects of FSS Technology
### Applications of FSS Technology  
FSS technology has found a broad range of applications across industries, but its suitability for deployment in various environments depends on specific design requirements. In telecommunications, FSS structures are used in antenna systems and radomes to enhance signal transmission while blocking unwanted frequencies, which is particularly beneficial in aerospace and satellite applications. In building design, FSSs can be integrated into windows or facades for electromagnetic shielding or energy efficiency by controlling the passage of electromagnetic waves. However, its implementation in all types of buildings may not be feasible due to factors like cost, design complexity, and aesthetic considerations. High-performance buildings such as data centers, hospitals, and military facilities stand to benefit most from FSS technology, whereas residential or smaller commercial buildings may not require such specialized solutions.  
There are many studies on electromagnetic attenuation or isolation due to traditional building materials: stone, bricks, concrete, wood, glass, and so on. Even these studies indicate that there is a frequency dependence in their electromagnetic behavior; this is more related to an increasing attenuation as frequency grows than to a filter effect. This means that, in general, all traditional building materials attenuate the electromagnetic waves, with stronger or lighter effects on them, and that they provide deeper attenuations at higher frequencies. But they are not selective in frequency: they do not block some frequency bands, while allowing others to cross from one side to the other without attenuation. And this is, precisely, the electromagnetic behavior studied when dealing with FSS design. The frequency-selective characteristic makes these materials interesting for insulation applications, as they could be designed for reducing the waves at specific frequencies (i.e., your neighbor's WiFi) and allowing the reception of other bands (i.e., the emergency alerts).
## 3 Data and Strategies
### Processing Strategy  
In our study, the Geodetic Spatio-Temporal data Analysis and Research (GSTAR) software independently developed by Beihang University was used for data processing [28]. More specifically, we opted for the post-processing mode when determining satellite orbits, with the arc length specified as 24 h. The a priori accuracy of ISL measurement was set to 0.3 m, whereas that of the SLR observations was set to 0.003 m. In addition, the errors of the observation and perturbing force were corrected with the universal models. It is crucial to note that, due to the influence of the hardware in the transmitting and receiving devices, both the ISL and SLR observations are subject to hardware delays. Consequently, it is imperative to solve these hardware delay together with the satellite orbits and dynamics parameters simultaneously [29; 30]. For the hardware delay of the ISL, we estimate it as a constant in one arc. However, owing to the sparse SLR observation and the ambiguous characteristic of its hardware delay, we initially disregarded this aspect and plan to conduct an in-depth investigation in subsequent studies. The data and configuration, SLR error model, dynamic model, and strategies to estimate parameters are shown in Table 3.
## 6 Conclusions
### Importance of LSA and GSA  
The overlapping window partitions in the local self-attention module (LSA) and the multi-scaled dilated pooling operations in the global self-attention module (GSA) are the key designs in our network. To further explore the importance of our proposed LSA and GSA, we conduct two more experiments. One is to use LSA in all LGViT blocks without GSA, and the other is to take GSA in all LGViT blocks without LSA. Quantitative results on three visual tasks are shown in Table 11. We observe that the proposed LSA outperforms GSA for most visual tasks. Specifically, for object detection and semantic segmentation, the performance gaps between LSA and GSA are much larger than image classification. This could be attributed to the dense prediction tasks more rely on low-level localization information which mainly exists in local features.
# A Review of the T-Stub Components for the Analysis of Bolted Moment Joints
## 2 Analytical Model
### Proposed Analytical Model for One-Side Bolted T-Stub Based on EC3  
The one-side bolted T-stub has five failure modes, where the Mode 1, Mode 2a, Mode 3a were analysed by the EC 3, and the other two Mode 2b and Mode 3b were proposed by et al. (2016); [PERSON] et al. (2017); [PERSON] et al. (2018), see Figure 14.  
Figure 12: Analytical model of T-stub connection: (**a**) Standard model; (**b**) Modified model for hollow-bolt (2016). “Reproduced with permission from [PERSON], [PERSON], [PERSON], Engineering Structures; published by Elsevier, 2010”.  
Figure 13: Assemble of hollow-bolt connection: (**a**) Hollo-bolt assemble; (**b**) Schematic representation of mechanical interlocking load acting on the flaring sleeve (2016). “Reproduced with permission from [PERSON], [PERSON], [PERSON], Engineering Structures; published by Elsevier, 2010”.  
Figure 14: Failure modes of one-side bolted T-stub: (**a**) Mode 1; (**b**) Mode 2a; (**c**) Mode 3a; (**d**) Mode 3b; (**e**) Mode 2b [17]. “Reproduced with permission from [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], Engineering Structures; published by Elsevier, 2018.  
1. Mode 1: This type of failure mode is the same than the EC 3, and is calculated by Equation (1), but the plastic moment, see Equation (4), is determined by Equation (41), where \(f_{t}\) is the ultimate strength of the T-stub flange.
2. Mode 2a: This failure is the same as the EC 3 Mode 2, see Equation (3), but plastic moment is calculated as for Mode 1, see Equation (41).
3. Mode 3a: The bolt failure characterises this mode, and it is the same as EC3, see Equations (5) and (6)
4. Mode 3b: The hole thread failure characterises this failure mode. This tension strength could be calculated by Equations (42)-(48).
5. Mode 2b: The flange yielding accompanied with hole thread failure characterize this failure mode, and the tension strength could be calculated by Equations (49) and (50):  
\[M_{pl,i,\ Rd}=\frac{1/4\sum beff_{i}\times tf^{2}\times f_{t}}{\gamma_{M0}}, \tag{41}\]  
\[F_{T,3b}=\sum F_{s,Rd}, \tag{42}\]  
\[F_{s,Rd}=\min\bigl{(}\sum F_{s,1,Rd},\sum F_{s,2,Rd}\bigr{)}, \tag{43}\]  
where \(F_{s,1,Rd}\) is the shear strength of one thread and \(F_{s,2,Rd}\) is the bending strength of one thread.  
\[F_{s,1,Rd}=\text{A}_{\text{v}}f_{yv,p}, \tag{44}\]  
\[\text{A}_{\text{v}}=\pi\text{D}h_{s}, \tag{45}\]  
\[F_{s,2,Rd}=\frac{\text{W}_{\text{p}}f_{y,p}}{b_{s}}, \tag{46}\]  
\[\text{W}_{\text{p}}=0.25\pi\text{D}h_{s}^{2}, \tag{47}\]  
\[f_{yv,p}=f_{y}/\sqrt{3}, \tag{48}\]  
\(f_{yv,p}\), \(f_{y,p}\) and \(f_{y}\) are the shear strength and yield strength of the T-stub flange and the yield strength of the steel. \(\text{A}_{\text{v}}\) is the efficient shear area of one circle of threads. \(D\), \(b_{s}\) and \(h_{s}\) are the external diameter of the bolt thread, the height and width of the internal thread on flange, respectively, which depends on the type of the thread:  
\[F_{T,2b}=\frac{2M_{pl,2b,\ Rd}+n\sum F_{s,Rd}}{m+n}, \tag{49}\]  
\[M_{pl,2b,\ Rd}=\frac{1/4\sum beff_{2b}\times tf^{2}\times f_{y}}{\gamma_{M0}}, \tag{50}\]
## 3 Results
### Genetic Polymorphism of NR6A1 Gene in Dezhou Donkey
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Position**} & \multirow{2}{*}{**Location**} & \multirow{2}{*}{**Sample**} & \multirow{2}{*}{**Genotype**} & \multicolumn{2}{c}{**Genotype**} & \multicolumn{2}{c}{**Al allele Frequencies**} & \multicolumn{2}{c}{**HW**} & \multicolumn{2}{c}{**Genetic Parameters**} & \\  & & & & **Frequencies** & & & **Wild** & **Mutant** & \multicolumn{2}{c}{**Chi-Square**} & \multicolumn{2}{c}{**_P_ Value} & \multicolumn{2}{c}{**Obs-Het**} & \multicolumn{2}{c}{**Pred-Het**} & \multicolumn{2}{c}{**PIC**} & \multicolumn{1}{c}{**Ne**} \\ \hline g.18093100G \(\mathrm{\SIUnitSymbolics}\) & \multirow{2}{*}{Intron-2} & 455 & GG & 0.64 & 291 & 0.7915 & 0.2085 & 2.966 & 0.107 & 0.303 & 0.33 & 0.417 & 1.5 \\  & & & GT & 0.303 & 138 & & & & & & & & \\ g.18094587C \(\mathrm{\SIUnitSymbolics}\) & \multirow{2}{*}{Intron-2} & 455 & CC & 0.477 & 217 & 0.688 & 0.312 & 0.118 & 0.774 & 0.422 & 0.429 & 0.495 & 1.75 \\  & & & CG & 0.422 & 192 & & & & & & & & \\ g.18106043A \(\mathrm{\SIUnitSymbolics}\) & \multirow{2}{*}{Intron-6} & 455 & AA & 0.455 & 207 & 0.686 & 0.314 & 2.345 & 0.166 & 0.462 & 0.431 & 0.478 & 1.76 \\  & & & AC & 0.462 & 210 & & & & & & & & \\ g.18108764A \(\mathrm{\SIUnitSymbolics}\) & \multirow{2}{*}{Intron-6} & \multirow{2}{*}{455} & CC & 0.083 & 38 & & & & & & & & \\  & & & AG & 0.519 & 236 & & & & & & & & \\ g.1810516G \(\mathrm{\SIUnitSymbolics}\) & \multirow{2}{*}{Intron-7} & \multirow{2}{*}{455} & GG & 0.123 & 56 & & & & & & & & \\ g.1810516G \(\mathrm{\SISymbolics}\) & \multirow{2}{*}{Intron-7} & 455 & GG & 0.455 & 207 & 0.6715 & 0.3285 & 0.141 & 0.749 & 0.433 & 0.441 & 0.505 & 1.79 \\  & & & GC & 0.433 & 197 & & & & & & & & \\ g.18112007 \(\mathrm{\SIUnitSymbolics}\) & \multirow{2}{*}{Intron-7} & \multirow{2}{*}{455} & CC & 0.112 & 51 & & & & & & & & \\ g.1812007 \(\mathrm{\SIUnitSymbolics}\) & \multirow{2}{*}{Intron-7} & 455 & TT & 0.101 & 46 & 0.6955 & 0.3045 & 0.682 & 0.444 & 0.407 & 0.423 & 0.494 & 1.74 \\  & & & GT & 0.407 & 185 & & & & & & & & \\ g.18114954C \(\mathrm{\SIUnitSymbolics}\) & \multirow{2}{*}{\
\begin{tabular}{} \end{tabular} } & GG & 0.492 & 224 & & & & & & & & \\ g.18114954C \(\mathrm{\SIUnitSymbolics}\) & \multirow{2}{*}{
\begin{tabular}{} \end{tabular} } & CC & 0.444 & 202 & 0.3245 & 0.6755 & 1.603 & 0.264 & 0.464 & 0.438 & 0.488 & 1.78 \\  & & & TC & 0.463 & 211 & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Genotype, allele frequency and genetic parameters of seven SNPs in the _NR6 AI_ gene in Dezhou doorkeys. In order to reveal the linkage relationship between the seven mutation sites of _NR6A1_ gene, D'and r2 were calculated to measure the linkage disequilibrium between the seven mutation sites. From Figure 2a (D') and Figure 2b (r2), we can see that there is a strong linkage between g.18094587C > G, g.18106043A > C, g.18108764A > G and g.18110615G > C (r2 > 0.33), g.18112000T > G and g.18114954C > T (r2 = 0.81).