esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Expected Curing Behavior  
The main anticipated general effects on the registered OBR-signal during the hardening of the epoxy are schematically shown in Figure 3. In the liquid, epoxy strains do not exist, thus no strains are transferred to the fiber initially. This is referred to as the zero strain state and it is shown as a horizontal grey line in Figure 3.  
Figure 2: Schematic of the experimental setup for temperature measurements along the length of the optical fiber (upper image). Photo of the same empty 40 cm long steel mold as in Figure 1 but with the temperature probes in place (middle image). Cross section of the solid epoxy block that was formed in the experiment, the setup of which is shown in the middle picture (lower image). The same temperature sensor marked in the middle picture is marked there, too. In the cross section image, the fiber(s) can be seen, too.  
Due to the ongoing exothermic reaction, the fiber experiences an increased temperature. This leads to a positive signal along the fiber (black curve in Figure 3). This positive signal however is not to be interpreted as actual strain onto the fiber but as a temperature. To compare the actual strain measurements and assess the influence of the temperature easier for the reader, we have chosen not to re-label and re-scale the ordinate but to rather denominate this signal as apparent strain (cf. Figure 4 and accompanying text). We think this is justified since the influence of the temperature onto the signal is much smaller than the final actual strain (cf. Appendix A). As more chemical bonds form, the viscosity of the epoxy increases and it gradually solidifies. The epoxy adheres to the optical fiber and transfer forces and strain onto it at some point during the curing process. [PERSON] et al. determined this point to be when gelation starts [PERSON] et al. (2018). This should be around 60% degree of cure in the epoxy system used in this work. We refer to the Appendix C for the calculation of the conversion at the point of gelation. From the point of gelation onwards, the strains changes substantially. The strain over the length of the fiber reaches large negative values (cf. red curve in Figure 3). Eventually, the sample has to cool down to room-temperature. This will lead to thermal contraction and even higher negative strains (blue curve in Figure 3).  
It is expected that one of these mechanisms is always dominating during the epoxy cure. However, one has to keep in mind that cure shrinkage and the thermally induced (apparent) strain changes also take place at the same time. In these cases, the contribution of each mechanism may not be separable from the actually measured strain. Such occurrences are discussed below.  
Figure 3: A general sketch of the anticipated strain behavior of hardening epoxy. A positive signal due to elevated temperatures is not to be interpreted as actual strain.
## 3 Results and Discussion  
### Free and Bound Cl Contents  
Figure 3 shows the measured contents of free and bound chloride in the FSW and SSW samples. In this case, the bound chloride content of the FSW sample increased from 59% to 69%, while that of the SSW sample did from 42% to 91% between 7 and 28 days. Although SSW did not show any noticeable advantage in immobilizing free chloride, compared to FSW, at 7 days, it clearly did at 28 days.
## 3 Results and Validation
### Parameterization Results
#### 3.3.2 Effects of the Position of the Piezo Device on Transmission Force and Deformation  
As shown in the plot in Figure 19, the angular location of the piezo transducer does not have any appreciable impact on the \(k\) factor for piezo angular positions ranging from 10 to 50 deg. In line with this, no variations can be observed in terms of the transmitted axial strain. This would allow us to dispose of more than one piezo device along the beam (assuming the curvature to be constant) without losing anything in terms of performance. Therefore, instead of using a long piezo patch (we will see that this will bring to lose some performance), a constellation of piezo patches can be used. This is also in agreement with what [PERSON] and [PERSON] stated in their work.  
Figure 19: The \(k\) factor vs. the angular position of the piezoelectric patch.  
Figure 18: The strain transmission factor vs. the curvature.
## 2 Creation of Signals Database Measurement Conditions
### Wheel Profile  
The spectrum of wheel profiles (**W**) on the railways is very high. The number of configurations may be reduced based on a standard whose use is confirmed by the relevant Infrastructure Manager. Technical Specification for Interoperability (TSI) for Railways (TSI) in Europe is one of them and defines the parameters of the rolling stock for interoperable lines. The parameters of the wheels and wheel sets are defined in doc.: ERA/ERTMS/033281 [16]. The document defines, among others, dimensions of wheels such as a diameter, a rim width, a flange height and a flange thickness (Figure 5) for different track gauges irrespective of the rail profiles.  
The flange height (S\({}_{\text{h}}\)) and thickness (S\({}_{\text{d}}\)) (see Figure 5) are described in [16] as a function of wheel diameter D. The gauge of 1435 mm, which covers also the wheels for gauges 1524 mm and 1668 mm is treated as the reference for Railways in Europe. Such wide tracks as 1520 mm, very popular in the East, and 1600 mm gauge (that is used for instance in Ireland), require preparing additional samples of wheels.  
The set of wheel samples \(\textbf{W}\in\{\text{ k2, k4, k5, k6, k8, k10, k11, k13, k15, k16}\}\) presented in the Table 1 covers the profiles specified in [6]. Minimum dimensions of a wheel flange are most critical for operation of the inductive sensor and thus are used to specify the samples for signal database creation.
# Detecting and Mitigating Attacks on GPS Devices
## 1 Introduction
Other reviews focus on niche topics related to GPS security or alternative non-GNSS positioning and navigation methods. There are no existing comprehensive reviews of GPS security issues in the literature. This paper fills that gap by providing a comprehensive treatment of GPS security. We provide an overview of the factors that can degrade or disrupt GPS signals in this paper, particularly those related to cybersecurity. Section 2 summarizes previous surveys and systematic reviews related to GPS security. Section 3 introduces GPS and establishes the performance baselines of GPS systems in benign conditions. Section 4 discusses the various disruption scenarios, attack types, and threat systems that can affect successful GPS operations. Section 5 provides an overview of GPS jamming and spoofing detection methods. Section 6 discusses countermeasures to GPS jamming and spoofing, including an analysis of the state-of-the-art in positioning and navigation in a GPS-disrupted environment. Finally, Section 7 provides conclusions and open research directions. This paper focuses on GPS positioning and navigation aspects and does not cover the timing aspects of GPS performance or alternative timing approaches in GPS-denied environments. Furthermore, this paper considers generalized GPS performance, focusing on positioning and navigation within UAS applications. Many modern devices do not rely solely on GNSS systems such as GPS; they rely on multiple sensors working in conjunction with GNSS for positioning and navigation. These sensors can sense the system's attributes, such as with inertial sensors, or the surrounding environment's attributes, such as with cameras. Data from these sensor systems are fused with GNSS-based data using methods such as a Kalman filter. Other systems utilize multiple GNSS systems, including GPS, GLONASS (Russian GNSS), Galileo (European GNSS), and Beidou (Chinese GNSS). The loss of GPS does not represent a complete loss of navigation data in multi-sensor or multi-GNSS systems but rather represents the loss of a single data source that could cause performance degradation to positioning and navigation solutions. This paper extensively discusses various sensors and associated algorithmic methods used for positioning and navigation instead of providing a comprehensive examination of all components within a complex multi-sensor or multi-GNSS navigation system; however, its primary focus lies in GPS-denied scenarios, exploring these sensors and methods as strategies to counter GPS vulnerabilities and ensure reliable positioning and navigation even under compromised GPS conditions.
# An Efficient Parallel Algorithm for Polygons Overlay Analysis
## 3 Methods
### Active-Slave Spatial Index Decomposition for Intersection
(2) Sending of the subtree index  
The primary node sends the index entries to the child nodes according to a certain replication and allocation policy. The data structure uses an in-memory spatial index. Unlike the traditional hard disk-based spatial index, the in-memory spatial index has the characteristics of dynamic construction and fast query. (3) The master node sets up the Master-R-tree  
The master node builds a new spatial index into all non-leaf nodes in the original entire R-tree index tree in computer memory. The index tree records the index item information allocated by each computing node, and its function is equivalent to an overall index controller. The intersecting query of the active overlay layer is first filtered by the overall index tree. (4) Computing node to build a Client-tree  
According to the agreed STR-tree node organization rule, the index items sent by the master node are built into the client terminal tree. The entity object stored in the computer node is associated with the index item of the subtree, and the entity space data is extracted according to the index item MBR. The parallel R-tree index decomposition based on the STR rule is analyzed. The essence is to replace the random partition in the form of elemental fid with the domain decomposition partition considering the neighboring spatial rule. The master node controls the overall spatial index R-tree of a large node, where the id information of the spatial data domain decomposition block in the cluster is recorded. The active layer first performs a query operation with the overall index tree. If one or more intersecting subdomains are queried, the master node notifies the child nodes having the subdomains to extract the active layer locally. The element is further intersected with the subtree, and the process will be analyzed in the parallelization of the intersection. Figure 2: Sort-Tile-Recursive (STR)-tree data decomposition.
## 4 Discussion and Analysis  
Pure rotational transitions of the ground state benzophenone were accurately measured by the cp-FTMW with a quadrature detection design in this work. The new assignment between 2 to 8 GHz bridged the measuring gap in the lower microwave range for this molecule. A combined fit covering 2-14 GHz allowed for more precise determination of the rotational constants. Comparing the rotational constants with that from higher microwave frequency data fitting [8; 9], the accuracy of our combined fitting is significantly improved, as shown in Table 2. The RMS deviations of the fit of 2.6 kHz is less than two percent of the \(\sim\)150 kHz experimental linewidths, revealing that the effective Hamiltonian employed here has accurately described the observed spectral features in the microwave range. However, for certain centrifugal distortion constants, such as \(d_{2}\), a global fitting from higher microwave frequency data [8; 9] can definitely provide more accurate results due to the high \(K_{a}\) and \(K_{c}\) transitions available.  
Including this work, plenty of computational investigations have been performed on the benzophenone [8; 9; 23; 24]. In summary, ab initio and Density Functional Theory methods were mostly employed to optimize its structure, undoubtedly providing the paddle-wheel-like C\({}_{2}\) geometry for a global minimum with a single component of the dipole moment along the b principle axis. For the hindered internal rotation of the two phenyl groups, the latest MP2/cc-pVQZ structure from the van Wijngaarden group [8] by the second moment calculation determined a 32.9\({}^{\circ}\) torsional angle of the phenyl groups out of the a, b-principle plane in the gas-phase. In addition, two higher energy  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Combined** & \multicolumn{2}{c}{**Previous Work a**} \\ \hline Frequency range (GHz) & 2.0–14.0 & 8.0–14.0 & 60.0–73.0 \\ A (MHz) & 1692.889219(31) & 1692.88929(23) & 1692.916(35) \\ B (MHz) & 412.644660(11) & 412.644594(92) & 412.620(17) \\ C (MHz) & 353.874564(11) & 353.874501(88) & 353.884(17) \\ D\({}_{\rm J}\) (kHz) & 0.010635(19) & 0.01053(14) & 0.0122(17) \\ D\({}_{\rm JK}\) (kHz) & \(-\)0.043444(83) & \(-\)0.04372(54) & \(-\)0.0517(60) \\ D\({}_{\rm K}\) (kHz) & 0.41417(59) & 0.4195(43) & 0.462(62) \\ d\({}_{1}\) (kHz) & \(-\)0.0014406(23) & \(-\)0.001442(17) & \\ d\({}_{2}\) (kHz) & \(-\)0.00099(15) & \(-\)0.0003385(68) & \\ \(\sigma\)(kHz) & 2.6 & 8 & 110 \\ N & 304 & 166 & 54 \\ \hline \hline \end{tabular}  
* Constants are from Ref. [8; 9].  
\end{table}
Table 2: Fit spectroscopic parameters for benzophenone.  
conformers regarding such hindered rotation with one T-like C\({}_{8}\) symmetry and one C\({}_{2\
u}\) symmetry were predicted to stay 1084 cm\({}^{-1}\) and 4267 cm\({}^{-1}\) above the global minimum, respectively, which excluded resolvable tunneling splitting associated with the internal rotations in the microwave spectrum, and was confirmed by the free-jet experiments from our group and others [8; 9].
## 2 Theoretical Foundation
### Mathematical Formulation  
Let,  
\(S=\{1,2,...,n\}\) be the set of depots,  
\(I=\{1,2,...,m\}\) the set of clients that are depots,  
\(V=\{0,l\}\) the union of \(S\) and \(I\) (set to \"0\" because there is only one depot and limited to \(I\), because each vehicle starts at origin zero and goes to the first vaccination center; when it leaves the first to go to the second, that first center becomes an origin. This logic repeats through all vaccination centers, thus being limited to the set of clients \(I\)),  
\(v_{s}\) the set of customers served by vehicle \(s\),  
\(c_{ij}\) (\(c_{ij}\geq 0\), \(i,j\in V\)) the distance between the points of the customers,  
\(C\) the maximum load,  
\(g_{i}\) (\(i\in V\)) the required load capacity of each customer required,  
\(x^{s_{ij}}\) indicates whether vehicles will serve customer \(j\) after having served customer \(i\) (\(x^{s_{ij}}=1\) for service, \(x^{s_{ij}}=0\) for no service),  
\(y^{s_{i}}\) indicates whether customer \(i\) is served by vehicle \(s\) (\(y^{s_{i}}=1\) should be served, \(y^{s_{i}}=0\) should not be served),  
\([a_{i},b_{i}]\) the timeout window of customer \(I\),\(a_{i}\) the allowed service start time of customer \(i\),  
\(b_{i}\) the end time of customer \(i\), and  
\(s_{i}\) the initial service time of customer \(i\)[14].  
The mathematical model used is represented in the following formulation [10]:  
\[min\{F_{1},\ F_{2}\}=min\left\{\sum_{i\in V_{s}}\sum_{s\in S}x_{0j}^{s},\ \sum_{s\in S}\sum_{i\in V_{s}}\sum_{j\in V_{s}\setminus\{i\}}c_{ij}x_{ij}^{s}\right\} \tag{1}\]  
where \(F_{1}\) is the number of vehicles and \(F_{2}\) is the total distance run by all vehicles, subject to:  
\[\sum_{j\in V_{s}\setminus\{i\}}x_{ij}^{s}=\gamma_{i}^{S},\ \forall i\ e\ V,\ \forall s\ e\ S \tag{2}\]  
\[\sum_{i\in V_{s}\setminus\{j\}}x_{ij}^{s}=\gamma_{j}^{s},\ \forall j\ e\ V,\ \forall s\ e\ S \tag{3}\]  
\[\sum_{i\in V_{s}}q_{i}\ \gamma_{i}^{S}\leq\ C,\forall s\ e\ S \tag{4}\]  
\[\sum_{i\in I}x_{0j}^{S}=\sum_{i\in N}x_{i0}^{S}=1,\ \forall s\ e\ S \tag{5}\]  
\[\sum_{s\in S}\gamma_{i}^{S}=1,\ \forall i\ e\ I \tag{6}\]  
\[\sum_{i,j\in V_{s}}x_{ij}^{S}\leq|V_{s}|-1,\ \forall s\ e\ S \tag{7}\]  
\[a_{i}\leq\ s_{i}\leq\ b_{i},\ \forall i\ e\ I \tag{8}\]  
where \(x_{ij}^{S}\in\{0,1\}\), \(\forall i,j\in V,\ \forall s\ e\ S;\ \forall_{i}^{S}\in\{0,1\}\), \(\forall i\in I\), \(\forall s\ e\ S\)  
Equations (2) and (3) represent, respectively, that the vehicle directly serves customer \(j\) after serving customer \(i\), and the vehicle only serves customer \(i\) before serving customer \(j\). Equations (4) and (5) indicate, respectively, that each vehicle must not exceed the maximum load capacity, and the vehicle must return to its origin after completing the service. Equation (6) means that each customer is only served by one single vehicle. Equation (7) allows for avoiding the generation of subloops in the service process. Equation (8) represents the hard time window restriction [14].  
This mathematical formulation of the VRPTW that will be used in this study is, according to [PERSON] et al. [14], a heuristic method.
# Meniscal Tear Management Associated with ACL Reconstruction
## 3 Management of Specific Types of Meniscal Lesions Associated with ACL Reconstruction
### Meniscal Root Tears  
Meniscal root tears are defined as bony or soft tissue attachment avulsion injuries or radial tears within 1 cm from the anterior or posterior tibial attachments of the menisci [95; 96; 97]. Meniscal root tears (10-21% of all meniscal ruptures) were historically frequently overlooked or treated with meniscectomy [95].  
Due to the important role of the menisci as secondary stabilizers of the knee, the rupture of one of their attachments has a strong impact on their biomechanical role in the joint and, if left untreated, leads to the rapid development of knee OA [97]. Functionally, this type of meniscal rupture is equivalent to a total meniscectomy [97; 98].  
Therefore, repair of meniscal root tears should always be performed if possible, the gold standard being the trans-tibial pullout technique with anatomic reduction and fixation of the meniscus. A horizontal suture may be an option in some radial tears [99].  
Meniscectomy in case of root tears is an option to consider only if the patient has diffuse Outerbridge grade 3 or 4 (a very rare condition in patients with ACL reconstruction), osteoarthritis of the ipsilateral compartment, non-symptomatic chronic meniscal root tears, and/or significant limb malalignment unless concurrently corrected [100; 101].  
The incidence of meniscal root tears at the time of ACL reconstruction is high \(-15.5\)-\(31\%\) and even higher in revision cases, with four times more tears on the lateral than the medial side [101, 102, 103].  
Postero-lateral meniscus root tears are frequently associated with ACL tears in a traumatic context and should be repaired concomitant with ACL reconstruction because it plays an important role in stabilizing the knee in both anterior tibial translation (ATT) and during pivoting activities [104, 105, 106, 10]. However, root refraction has some limitations, such as a low cure rate evidenced by MRI, poor reduction of meniscal extrusion, and limited evidence of long-term efficacy to prevent osteoarthritis [106].  
Medial meniscus root tears are usually degenerative, and their prevalence has been reported to be \(9.1\%\)[107, 108, 106]. Even so, they should also be repaired because of the better results and slower progression of OA compared to meniscectomy [1, 108].
# Bacterial Aerosol in Ambient Air--A Review Study
## 3 Discussion
### Interactions with Meteorological Factors
#### 3.2.2 Relative Humidity (RH)  
RH plays a critical role in modulating bacterial concentrations, with correlations exhibiting considerable variability. In general, elevated RH fosters bacterial growth, as bacteria can absorb moisture from the environment to facilitate metabolic processes (Figure 4b). Furthermore, high RH can promote the aggregation of bacterial cells, potentially enhancing  
Figure 4: Graphical summary of statistically significant correlation between bacterial aerosol and (**a**) temperature and (**b**) relative humidity.  
their survival [32]. A single raindrop can generate over 100 bioaerosol droplets with diameters smaller than 10 \(\upmu\)m. Moreover, soil bacteria such as _Pseudomonas syringae_, _Bacillus subtilis_, and _Corynebacterium glutamicum_ have been found to remain culturable for up to one hour following aerosolization [92]. Heavy rainfall has been associated with an increase in both airborne bacterial diversity and humidity, which promotes the activity and survival of airborne bacteria [8].  
In contrast, very low relative humidity (RH) inhibits microbial activity, as dry conditions suppress the metabolic and physiological functions of microorganisms [93]. Additionally, [PERSON] et al. [57] observed a negative correlation between RH and bacterial diversity, likely due to increased moisture facilitating particle deposition through the enlargement of particle sizes, while wet soil surfaces reduce the likelihood of aerosolization.  
The relationship between RH and bacterial concentrations exhibits considerable variability across studies. Some research studies report positive correlations, ranging from 0.291 to 0.529 [45], suggesting that higher RH may be associated with increased bacterial concentrations. However, other studies have identified negative correlations, such as \(-0.720\) (\(p<0.05\)) [69], indicating that elevated RH could correspond with lower bacterial levels. For terrestrial bacteria, negative correlations have been observed, with values as low as \(-0.416\) (\(p<0.05\)) [68], highlighting the inverse relationship between RH and bacterial concentrations. Seasonal variations further affect these dynamics, with correlations between RH and bacterial levels being weakly positive, ranging from 0.351 (\(p<0.05\)) in winter [4] to 0.53 (\(p<0.05\)) in spring [42].
## 2 Review of PV Model and MPPT Control Methods
### Review of MPPT Control Methods
#### 2.2.2 Incremental Conductance (INC)  
Another well-known method that is prevalent in the literature is incremental conductance, which is based on the control objective given in (5):  
\[\frac{dP_{PV}}{dV_{PV}}=0, \tag{5}\]  
It is clear from the usual \(P\)-\(V\) curve that the system operating point is on the left side of the MPP when the \(dP_{PV}/dV_{PV}\) is greater than zero. The operational point is on the right side of the MPP when the value \(dP_{PV}/dV_{PV}\) is negative.  
Figure 3: Flowchart of P&O algorithm.  
From (5), considering the power (given as \(P_{PV}=V_{PV}I_{PV}\)), (6) is obtained. This result relates the PV panel conductance to its incremental value.  
\[\frac{dI_{PV}}{dV_{PV}}=-\frac{I_{PV}}{V_{PV}}, \tag{6}\]  
As a result, the duty cycle should be increased if \(dI_{PV}/dV_{PV}>-I_{PV}/V_{PV}\). However, the duty cycle should be reduced when \(dI_{PV}/dV_{PV}<-I_{PV}/V_{PV}\). At the optimal point \(dI_{PV}/dV_{PV}=-I_{PV}/V_{PV}\), the duty cycle is maintained to reduce the ripple in panel voltage and current. This algorithm can be seen in Figure 4.  
Incremental conductance also faces issues when the irradiance changes. During some irradiance changes, the algorithm may compute the \(dI_{PV}/dV_{PV}\) value with an incorrect signal, resulting in a delayed transient reaction and, as a result, a momentary loss of power [39].  
To lessen issues related to variations in irradiance and transient responses, some adaptive and modified variants of incremental conductance have been introduced [15; 16].  
Figure 4: Flowchart of incremental conductance algorithm.