esa-sceva/satcom-chunk-collection huggingface dataset:
# Application and Assessment of Cooperative Localization in Three-Dimensional Vehicle Networks
## 5 Vehicle Model  
### Vehicle Pose  
Each vehicle is assigned a body-fixed reference frame \(X_{v}Y_{v}Z_{v}\). Figure 1 depicts the pose of the vehicle, which is then represented by the position of the origin of \(X_{v}Y_{v}Z_{v}\) and its orientation with respect to the inertial reference frame \(X_{0}Y_{0}Z_{0}\). Hence, the vector \(r_{0}^{\gamma}=[x_{0}^{\gamma}\;\;y_{0}^{\gamma}\;\;z_{0}^{\gamma}]^{\top}\) represents the position of the vehicle, and the orthogonal rotation  
\begin{table}
\begin{tabular}{p{85.4 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Algorithm** & **Advantages** & **Limitations** \\ \hline \multirow{3}{*}{SLAM} & Obtain localization of multiple vehicles, mapping of an environment & Requires knowledge of the environment and designated landmarks, needs assigned “leaders” and “followers” or a central processing station \\ \cline{2-3}  & Nonlinear applications, low error in ideal situations, no linearization required & Requires individual agent identification, not robust to false-positive detections \\ \hline Decentralized EKF with Euler angles & No designated central processing center needed & Prone to singularity issues due to gimbal lock, requires linearization (Jacobian) \\ \hline Current Method & Singularity-free, computationally effective & Quaternions cannot be measured directly, requires linearization \\ \hline \hline \end{tabular}
\end{table}
Table 1: Limitations of related works.  
matrix \(R_{0}^{v}\) that transforms the coordinates of any vector in \(X_{v}Y_{v}Z_{v}\) to \(X_{0}Y_{0}Z_{0}\) determines its orientation. \(R_{0}^{v}\) can be calculated using quaternions, which are favored over Euler angles since they do not suffer from singularities and require less computational overhead. The quaternion vector of the vehicle reference frame \(\vec{q}^{v}\) can be presented as:  
\[\vec{q}^{v}=\begin{bmatrix}q_{0}^{v}\\ q^{v}\end{bmatrix},\quad q^{v}=\begin{bmatrix}q_{1}^{v}\\ q_{2}^{v}\\ q_{3}^{v}\end{bmatrix} \tag{1}\]  
where there is redundancy due to the four scalar values representing three rotational degrees of freedom. The constraint relating the quaternions is \(\vec{q}^{v^{\top}}\vec{q}^{v}-1=0\), indicating that \(\vec{q}^{v}\) is a unit vector.  
The orthogonal rotation matrix is given in terms of the quaternions as [48]:  
\[R_{0}^{v}(\vec{q}^{v})=[2(q_{0}^{v})^{2}-1]I_{3}+2[q^{v}q^{v^{\top}}+q_{0}^{v} S(q^{v})] \tag{2}\]  
where \(S(q^{v})\) defines a skew-symmetric matrix of a vector such that  
\[S(q^{v})=\begin{bmatrix}0&-q_{3}^{v}&q_{2}^{v}\\ q_{3}^{v}&0&-q_{1}^{v}\\ -q_{2}^{v}&q_{1}^{v}&0\end{bmatrix} \tag{3}\]  
and \(I_{3}\) is the \(3\times 3\) identity matrix. Given the rotation matrix in (2), we can calculate \(\vec{q}^{v}\), as described in [46].
## 4 Discussion
Our findings further highlight the significant shortcomings of PSA-based decision-making in PCa, despite the fact that PSA measurement remains common practice at various stages of PCa management, including screening or assessing potential risk of developing PCa, detection of recurrence after initial therapy, and for informing the management of advanced disease [37]. Interestingly, accruing evidence indicate that \"PSA screening does lead to over-diagnosis, over-treatment, and treatment-associated morbidity\" [38], and, consistent with the European Association of Urology (EAU) PCa guidelines panel recommendations, the PSA-based \"BCR after primary treatment of localized prostate cancer does not necessarily lead to clinically apparent progressive disease\" [39]. This study prefers an alternative multi-gene tool (HSD17B2\({}^{\text{lo}}\)HSD17B3\({}^{\text{hi}}\)SHBG\({}^{\text{lo}}\)SRD5A1\({}^{\text{hi}}\) vs. PSA\({}^{\text{hi}}\)) for identifying patients at high risk of progression or relapse to initiate early salvage treatment, while deferring treatment for patients with low risk of recurrence or progression. Moreover, exploiting the phenomenon of oncogene addiction, we demonstrated for the first time, to the best of our knowledge, that the inhibition of SRD5A1 with metronomic Mutasteride synergistically enhances the anticancer potential of low dose Docetaxel, reduced metastatic burden, and confers survival advantage, in vivo (Figure 7). These findings bear therapeutic relevance considering that the majority of patients with PCa develop resistance to therapy, including Docetaxel, and progress, despite initially responding well to treatment [40]. Resistance or reduced sensitivity to Docetaxel is a complex biological phenomenon that largely depends on the activation of several intratumoral and paramutoral molecular signaling pathways, including the androgenic/steroidogenic signals. Outasteride-induced pharmacological withdrawal of SRD5A1, with its associated mechanistic underpinnings, effectively elicits an impressive preclinical therapeutic activity consistent with those recently observed in clinical practice \"following treatment with so-called 'rationally-targeted' agents\" [32; 33]. The preclinical response was remarkable, and reminiscent of clinical responses to such single agents in patients with metastatic PCa that were largely refractory to standard chemotherapy [41; 42].
## 4 Example of Application: Performance Simulations using the EIPS framework
### Performance Simulations Analysis Using EIPS Framework
#### 4.3.1 Channel Jacobians Simulations  
Jacobians, i.e., the sensitivity of instrument channel frequencies with respect to temperature and humidity, has been calculated for all MW instrument configurations (A, B and C) using the HT-FRTC model of EIPS, but Jacobians for only two configurations A (ATMS-type) and B (AMSU-A-type) is shown in Figure 6. Temperature Jacobians have been calculated (\(dT_{b}/dT_{l}\), where \(T_{b}\) is the brightness temperature and \(T_{l}\) is the temperature of level \(l\) ) and are in the units of K/K and humidity Jacobians (\(dT_{b}/dlog(q)\), where \(q\) is the humidity in Kg/Kg) are calculated in the units of K/log(kg/kg).
## 4 Results
### Viris Rst-Flood  
Figure 5 shows the anomalous pixels identified within the ROI by implementing RST-FLOOD on VIIRS data. First, it is interesting to note that, when compared to MODIS, the two RST-FLOOD indices seem to show a higher sensitivity to flooding and to not significantly differ in terms of detected anomalies, as revealed by the analysis of the numbers reported in Table 2. In detail, an averaged area of approximately 52 km\({}^{2}\) was detected as flooded, almost three times higher than the one previously identified using MODIS. Although the VIIRS VNIR bands had lower spatial resolution than the MODIS one, the implementation of RST-FLOOD on VIIRS allowed for the detection of a larger number of anomalous pixels, not only along the Basento and Lato rivers, but also close to Cavone, Agri and Sinni, most likely due to the flooding. The almost equivalent number of detected pixels in two days confirm this high sensitivity, indicating that the effect of the flood was still detectable on 5 December. Concerning the San Giuliano Lake, the effect of suspended sediments is still present, even with a reduced impact. Finally, some spurious effects are also observable in the correspondence of a few pixels not close to riverbeds, disappearing when higher confidence levels of RST-FLOOD indices are used.  
Figure 5: Anomalous pixels detected by Visible Infrared Imaging Radiometer Suite (VIIRS)-based RST-FLOOD using: (**a**) ALICE\({}_{\text{NIR-VIS}}\), (**b**) ALICE\({}_{\text{NIR/VIS}}\) on 4 December 2013 at 12:10 GMT and (**c**) ALICE\({}_{\text{NIR-VIS}}\), (**d**) ALICE\({}_{\text{NIR/VIS}}\) on 5 December 2013 at 11:50 GMT.  
The magnification of the areas mainly affected by the event as detected by VIIRS have been taken into account and reported in Figure 6. The maps shown in this figure provide a clear confirmation of the considerations discussed above for MODIS, emphasizing the advantage in coupling the detections provided by the two RST-FLOOD indices, both in detecting the definitely flooding affected areas (green pixels in Figure 6) and improving the delimitation of their extent.
## Appendix A  
The following content in Appendix A includes the detailed descriptions of the derivation of the LEO's STP equation.  
First, based on the [PERSON]'s equation of motion, the actual position of the LEOs at epoch \(t+\Delta t\) can be expressed as follows:  
\[\overset{\rightarrow}{r}(t+\Delta t)\ =\ \overset{\rightarrow}{r}(t)+ \overset{\cdot\cdot}{r}(t)\Delta t+\ \int\limits_{t}^{t+\Delta t}\int\limits_{t}^{t^{\prime}}\overset{\cdot\cdot}{r }(\tau)d\tau dt^{\prime} \tag{1}\]  
where \(\overset{\rightarrow}{r}(t)\), \(\overset{\cdot\cdot}{r}(t)\), and \(\overset{\cdot\cdot}{r}(t)\) are the LEO's position, velocity, and acceleration at epoch \(t\) and \(\Delta t\) is the time interval.  
By changing the integration order in Equation (1), we find the following:  
\[\overset{\rightarrow}{r}(t+\Delta t)\ =\ \overset{\rightarrow}{r}(t)+ \overset{\cdot\cdot}{r}(t)\Delta t+\ \int\limits_{t}^{t+\Delta t}(t+\Delta t-\tau)\overset{\cdot\cdot}{r}(\tau)d\tau \tag{2}\]  
Similarly, we can formulate the equation for the negative:  
\[\overset{\rightarrow}{r}(t-\Delta t)\ =\ \overset{\rightarrow}{r}(t)- \overset{\cdot\cdot}{r}(t)\Delta t+\ \int\limits_{t}^{t-\Delta t}(t-\Delta t-\tau)\overset{\cdot\cdot}{r}(\tau)d\tau \tag{3}\]  
Thus, based on Equations (2) and (3), the STP equation for LEOs is derived as follows [35]:  
\[\begin{array}{l}\overset{\rightarrow}{r}(t+\Delta t)-2\overset{\rightarrow} {r}(t)+\overset{\rightarrow}{r}(t-\Delta t)\\ =\ \int\limits_{t-\Delta t}^{t+\Delta t}(\Delta t-|\tau-t|)\overset{\cdot \cdot}{r}(\tau)d\tau\\ =\ \Delta t^{2}\int\limits_{-1}^{1}(1-|\tau|^{\prime}|)\overset{\cdot \cdot}{r}(t+\tau^{\prime}\Delta t)d\tau^{\prime}\\ =\ \Delta t^{2}\int\limits_{-1}^{1}\kappa\overset{\cdot\cdot}{r}(t+\tau^{ \prime}\Delta t)d\tau^{\prime}\end{array} \tag{4}\]  
where  
\[\begin{array}{l}\kappa\ =\ \left\{\begin{array}{ll}1-\tau^{\prime}&0\leq\tau^{ \prime}\leq 1\\ 1+\tau^{\prime}&-1\leq\tau^{\prime}<0\end{array}\right.,0\leq\kappa\leq 1\\ \tau^{\prime}\ =\ \frac{\tau-t}{\Delta t},\ \ \ t-\Delta t\leq\tau\leq t+\Delta t \end{array}\]  
\(\Delta\Delta(*)(t)\ =\ (*)(t+\Delta t)-2(*)(t)+(*)(t-\Delta t)\) is the second-order time-difference operator, in which \((t+\Delta t)\), \((t)\), and \((t-\Delta t)\) refer to three adjacent epochs. \(\Delta\Delta\overset{\rightarrow}{r}(t)\) is the STP.  
According to Equation (4), \(\Delta\Delta\overset{\rightarrow}{r}(t)\) is only related to the in-arc acceleration \(\overset{\cdot\cdot}{r}(t)\) and the square of the sampling interval \(\Delta t^{2}\). The STP equation Equation (4) directly establishesthe relationship between the positions at three adjacent epochs \((\overrightarrow{r}(t+\Delta t),2\overrightarrow{r}(t),\overrightarrow{r}(t- \Delta t))\) and accelerations \(\overrightarrow{r}(t)\).
## 3 Results
### Morphometric and Gravimetric Measurements
#### 3.1.3 The Sprouts' Weight  
Following Figure 3, it can be noticed that biochar, despite the source of light, achieved higher values than gauze. The highest value obtained was in the case of using natural sunlight and biochar with 0.70 \(\pm\) 0.18 \({}^{\rm a}\) g (V2). This value is followed closely by its gauze counterpart and by red LED and biochar with 0.66 \(\pm\) 0.26 \({}^{\rm a}\) g both (V1 and V8). The lowest value was reported when using white LED and gauze (0.5 \(\pm\) 0.19 \({}^{\rm b}\)--V3). The other values obtained were: 0.54 \(\pm\) 0.18 \({}^{\rm a}\) for V4, 0.58 \(\pm\) 0.17 \({}^{\rm abc}\) for V5, 0.59 \(\pm\) 0.18 \({}^{\rm a}\) for V6, and 0.61 \(\pm\) 0.25 \({}^{\rm ac}\) for V7.  
The ANOVA test applied to results obtained from _Lepidium sativum_ L. sprouts' weight (Table 3) demonstrated a significant statistical difference (\(p\) < 0.01, but higher than \(p\) < 0.001).  
Observing the weight of the sprouts, the \(p\)-value here, in comparison to the other two parameters analyzed, has a notable difference in regard to the treatment used. In the gauze variants, the \(p\)-value is between 0.05 and 0.001, while concerning the biochar variant, the \(p\)-value is lower than 0.001, only proving that the biochar treatment bested the gauze treatment. Further rejecting the null hypothesis--that the treatments are not statistically supported--are the values of \(F\) and \(F\)_crit_. Even though they are in relatively proximity to  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Substrate (Gauze or Biochar)** & \(p\)**-Value** & \(F\) & \(F\) _crit_ \\ \hline Gauze (V1, V3, V5, and V7 & & \\  experimental variants) & 2.4 \(\times\) 10\({}^{-19}\) & 44.90682596 & 2.682809407 \\ Biochar (V2, V4, V6, and V8 & & \\  experimental variants) & 3.09 \(\times\) 10\({}^{-20}\) & 47.97180576 & 2.682809407 \\ \hline \hline \end{tabular}
\end{table}
Table 2: A one-way ANOVA test for radiole lengths of _Lepidium sativum_ L. sprouts.  
Figure 3: The medium values of _Lepidium sativum_ L. sprouts’ weight on gauze (V1 control, illuminated with sunlight; V3, illuminated with white LED; V5, illuminated with blue LED; and V7, illuminated with red LED experimental variant) or biochar (V2 control, illuminated with sunlight; V4, illuminated with blue LED; V6, illuminated with blue LED; and V8, illuminated with blue LED experimental variant). Data acquired from the experiment were statistically investigated and were expressed as the mean \(\pm\) SD (standard deviation). Bars are one standard deviation.  
the other two results from above, not to be disregarded is the fact that \(F\) has a higher value than F _crit_.  
When looking at how this experiment influenced the weight and length of _Lepidium sativum_ L. sprouts, a big difference can be observed in the case of white LED light and biochar as substrate (V4). Comparing this case with its sunlight counterpart (V2) and even with the gauze and sunlight variant (V1), where the weight and the length remained high from both points of view, V4 somewhat did not abide by the normal course.
# An Efficient Grid-Based Geocasting Scheme for Wireless Sensor Networks
## 4 Simulation Results
### Average Energy Consumed versus Number of Target Regions  
We first explored the average energy consumed under a different number of target regions. The number of sensor nodes is 200 nodes, the initial power of sensor nodes is 0.25 J and 0.5 J, and the number of cells is 10 \(\times\) 10. As shown in Figure 8, we change the number of target regions and observe the average energy consumption when using GEAR, FERMA, FERMA-QL, and GB-FERMA, respectively. GEAR sends the sensing data for individual areas of interest. FERMA uses the Fermat points to find the nearest relay nodes (gateways) to gather data from the target regions and forwards it to the sink. FERMA-QL is a variant version of FERMA based on Q-learning. GB-FERMA applies a grid structure and calculates Fermat points to find optimal relay nodes (gateways) with the shortest path. It is responsible for aggregating the data from the target regions and transmitting the data to the sink. When the initial power is 0.25 J, the average energy consumption of GB-FERMA is about 53% of FERMA-QL, 37% of FERMA, and 23% of GEAR; however, when with the initial power 0.5 J, the average energy consumption of GB-FERMA is about 77% of FERMA-QL, 65% of FERMA, and 43% of GEAR. The average energy consumption of GB-FERMA is lower than that of GEAR and FERMA, which can effectively extend the network lifetime.
# Hybrid Satellite-Terrestrial Networks toward 6G: Key Technologies and Open Issues
## 4 HSTN Applications
### Rural Coverage  
One of the most important and promising applications of HSTNs is its expansion of coverage to rural, mountainous, and tough terrains. Based on their wide coverage and connectivity, HSTNs can provide services to previously unserved areas and populations. For areas with sparse users, mobile users can access the HSTN by their own terminals. Additionally, for residences or buildings in rural areas, fixed satellite antennas can be deployed to provide relatively broadband service for users inside based on 5G or Wi-Fi technologies.  
Users can access the HSTN by BSs or other access points with satellite backhauls. Furthermore, the HSTN can also provide additional links to enhance the transmission in rural areas without broadband terrestrial networks.
# A Novel Digital Signature Scheme for Advanced Asymmetric Encryption Techniques
## 3 The Proposed Signature Scheme
### The Proposed Scheme  
The proposed DS technique uses the private key \(k\) and the secret trapdoor \(p\) of the sender to generate a DS represented in two parts \(s_{1}\) and \(s_{2}\), which are used for performing the signing and verifying processes. Moreover, new base formulas are also introduced for conducting digital signature operations such as signing and verifying. Our proposed protocol is explained under three subsections including the signing, verification, and comparison processes. However, first, let us define the following primitives:  
* The sender integer random \(r\) split into two other random numbers \(r_{1}\) and \(r_{2}\) where \(r=r_{1}\times r_{2}\).
* The sender private key is \(K_{s}=(k_{s},r_{2})\),
* The sender public key is \(PK_{s}=(pk_{s},n,n^{\prime},r_{1})\), where \(pk_{s}=k_{s}+r\times p\), \(n=p\times q\) with \(p\) and \(q\) two large prime numbers; \(n^{\prime}\) is a small modulus (160 bits). To generate a secure public key, the integer \(r\) must verify \(r>q\). Furthermore, \(p\) and \(q\) must be secure primes, i.e., they are of the form \(2\times p^{\prime}+1\) and \(2\times q^{\prime}+1\), respectively, with \(p^{\prime}\) and \(q^{\prime}\) also primes:
* The plain message \(m\) is encrypted by the encryption function \(Enc\) using receiver public key \(pk_{r}\), \(Enc_{pkr}(m)=c\).
* The plain message \(m\) is signed by the signing function Sig() using sender private keys \((k_{s},r_{2},p)\).
* The receiver private key is \(k_{r}\)
* The cipher message \(c\) is decrypted by the decryption function \(Dec\) using receiver private key \(k_{r}\), \(Dec_{kp}(c)=m\).
* The received signature \(Sig\) is verified by the verifying function Verf() using sender public keys \((pk_{s},r_{1})\).  
To imply the public key (\(pk=k+r\times p\)) in the signature, the main idea of our proposal consists of two steps: First, split the signature into two parts \(s_{1}\) and \(s_{2}\); one uses \(k\) and the other uses \(r\times p\). Second, split randomly \(r\) into \(r_{1}\) and \(r_{2}\), where \(r=r_{1}\times r_{2}\); keep one secret (\(r_{2}\)), and the other is public. By this, we will obtain \(s_{1}=m^{k}\) and \(s_{2}=m^{\prime}{}^{2\times p}\). The receiver can easily first compute \(x=s_{1}\times s_{2}^{r_{1}}\), which is equal to \(m^{k+r\times p}\), and secondly compute \(y=m^{pk}\); finally, they are compared. The proposed protocol is explained below:  
Figure 1: The proposed architectural model.
# A Sensor for Characterisation of Liquid Materials with High Permittivity and High Dielectric Loss
## 3 Sensitivity Analysis
### Quality Factor  
In many cases, the permittivity is expressed using dielectric constant and imaginary part. The imaginary part of the complex permittivity of the material under test (MUT) can be expressed by loss tangent, which can be written as [19]:  
\[Q_{\mathrm{MUT}}=\frac{1}{\tan\delta}=\frac{\varepsilon_{\mathrm{r}}^{\prime}} {\varepsilon_{\mathrm{r}}^{\prime\prime}} \tag{3}\]  
where, \(Q_{\mathrm{MUT}}\) can be calculated by unloaded quality factor \(Q_{\mathrm{U}}\) and the corresponding \(S_{21}\)[36]  
\[Q_{\mathrm{MUT}}=Q_{\mathrm{U}}\bigg{[}1-10^{\frac{S_{21}(\mathrm{dB})}{2 \sigma}}\bigg{]} \tag{4}\]  
Figure 5: Comparison of sensitivity of the two resonators. (**a**) frequency and frequency shift, (**b**) sensitivity versus change in dielectric constant.  
Therefore, the loss tangent can be calculated by using the quality factor \(Q\) and the transmission coefficient \(S_{21}\). It is deduced that the larger variation in \(S_{21}\) (\(\Delta S_{21}\)), the higher the measurement sensitivity.  
In Figure 6, the quality factors \(Q\) and \(\Delta S_{21}\) of the circular CSRR structure and the rectangular CSRR structure to different loss tangent under \(\varepsilon_{\rm r}^{\prime}=60\) are plotted. It can be seen that the quality factors of the two structures are almost the same. However, the \(\Delta S_{21}\) of the circular structure is 1 dB higher than the \(\Delta S_{21}\) of the rectangular structure, increased by 9.7%. Considering Equation (4), it can be seen that the circular CSRR structure has higher measurement sensitivity when measuring the loss tangent.  
It has to be mentioned that the comparison in this section is only for the specific case between Ref. [29] and this work, where the diameter of the circular sensor equals the side length of the square sensor. More general sensitivity needs further investigation in terms of resonance, the geometrical structure of the sensor, and even the volume ratio of the sample exposed to the sensor.
## 5 Verification and Comparison of Simulation and Experimental Results  
This section describes the verification of the simulated results with experimental results. From both simulation and experimental works, by using the proposed IMC decoupled controller algorithms, the fast charging system is able to provide constant charging voltage and current charging. Besides, it can be clearly seen that the output DC fast charging voltage has a low ripple. Moreover, from both simulation and experimental results, the input current waveform is observed to be sinusoidal with minimum distortion (low THD value), and is working in phase with the input voltage, thus achieving almost unity power factor.  
However, THD value of the input current to the charger from the experimental work (1.75%) is found to be higher than the simulated value (1.55%). One of the reasons could be due to the power losses in the experimental work that were not considered in the simulation. In addition, in experimental work, due to resource limitations, the maximum input supply voltage for the battery charger was set to 100 Vrms (line to line). Whereas in simulation setup, the reference voltage is set to 650 V. Further, in the experimental work, the charging system is operated at a much lower current value compared to the simulation work. In the experimental work, the maximum input supply voltage for the battery charger is set to be 200 V, while in simulation work the maximum input supply voltage for the battery charger is set to be 480 V. Therefore, the results that were captured from the experimental work are found to be different than the simulated value. Since the magnitude of the fundamental current is low, thus a higher THD value is recorded from the experimental work.  
Figure 18: Measurements THD result of the input current of Phase A.