esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Results and Discussion
### Group Specific Discussion
#### 6.1.1 Group #1  
The characteristics of this measurements group are reported in Table 2. During this setup, 262 packets out of the 300 transmitted were received (i.e., the 87.33%). Table 5 reports the mean values, the standard deviations of the RSSIs and SNRs and the number of received packets of out 50 for each SF. It can be noticed that the maximum mean values for RSSIs and for SNRs were respectively observed for \(SF=10\) (i.e., \(\mu_{RSSI}=-97.136\) dBm) and for \(SF=9\) (i.e., \(\mu_{SNR}=9.193\) dB). However, due to the minimum difference between the mean RSSIs of \(SF=7\) and \(SF=10\) and since the standard deviation associated to the former is less than the one of the latter, it is reasonable to claim that \(SF=7\) was the most suitable SF during this measurements setup. Figures 10-13 in turn show the RSSI Probability Mass Function (PMF), the RSSI temporal trend, the SNR PMF and the SNR temporal trend dividing them by SF. Regarding the latter two, some negative SNRs can be spotted underlining the capability of LoRa to demodulate heavy noisy signals. Focusing on Figures 11 and 13 a lack of values in the horizontal axis can be noticed. This is due to the fact that their purpose is just to display how the RSSIs and the SNRs varied during the tests. Since this test group turned out to be successful, it was chosen to worsen the link by lowering down the transmitting antenna to \(h_{TX_{2}}\) (see Table 2) for the next sets of trials.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**SF** & \(\mu_{RSSI}\) **[dBm]** & \(\sigma_{RSSI}\) **[dBm]** & \(\mu_{SNR}\) **[dB]** & \(\sigma_{SNR}\) **[dB]** & **Received Packets** \\ \hline
7 & \(-97.800\) & 1.408 & 7.604 & 3.329 & 45 \\
8 & \(-100.319\) & 2.986 & 7.923 & 4.059 & 47 \\
9 & \(-100.023\) & 1.883 & 9.193 & 3.935 & 43 \\
10 & \(-97.136\) & 1.747 & 9.17  
Figure 10: Group #1 RSSIs PMFs: (**a**) \(SF=7\); (**b**) \(SF=8\); (**c**) \(SF=9\); (**d**) \(SF=10\); (**e**) \(SF=11\); (**f**) \(SF=12\).  
Figure 11: Group #1 RSSIs temporal trend: (**a**) \(SF=7\); (**b**) \(SF=8\); (**c**) \(SF=9\); (**d**) \(SF=10\); (**e**) \(SF=11\); (**f**) \(SF=12\).  
Figure 12: Group #1 SNRs PMFs: (**a**) \(SF=7\); (**b**) \(SF=8\); (**c**) \(SF=9\); (**d**) \(SF=10\); (**e**) \(SF=11\); (**f**) \(SF=12\).
## 4 Orthogonalized Maximal Projection Algorithm
Then, it follows that  
\[\mathbf{C}\mathbf{y}=\mathbf{W}^{\mathsf{T}}(\mathbf{r}_{0}- \mathbf{A}\mathbf{r}_{0}). \tag{70}\]3  
Taking the squared norms of both sides yields  
\[\mathbf{C}\mathbf{y}=\mathbf{W}^{\mathsf{T}}(\mathbf{r}_{0}- \mathbf{A}\mathbf{r}_{0}). \tag{70}\]4 \[\mathbf{C}\mathbf{y}=\mathbf{W}^{\mathsf{T}}(\mathbf{r}_{0}- \mathbf{A}\mathbf{r}_{0}). \tag{70}\]5 \[=[r_  
affine GMRES (A-GMRES) (Algorithm 8) in the affine Krylov subspace can significantly improve the performance rather than GMRES, when the orthogonality is taken into account. We remind that (6)-(8) in the following Algorithm A-GMRES are slightly more computational expensive than (6) and (7) in Algorithm GMRES. Also, (7)-(9) in the following Algorithm A-GMRES(m) (Algorithm 9) are slightly more computational expensive than (7) and (8) in Algorithm GMRES(m). ```
1: Give \(m_{0}\), \(\mathbf{x}_{0}\), and \(\varepsilon\)
2: Do \(m=m_{0},\ldots\) (\(k=m-m_{0}\))
3: \(\mathbf{r}_{k}=\mathbf{b}-\mathbf{A}\mathbf{x}_{k}\), \(r_{k}=\|\mathbf{r}_{k}\|\)
4: \(\mathbf{v}_{1}^{k}=\frac{\mathbf{r}_{k}}{\|\mathbf{r}_{k}\|}\)
5: \(\mathbf{V}_{k}=[\mathbf{v}_{1}^{k},\ldots,\mathbf{v}_{m}^{k}]\) (by Arnoldi process)
6: \((\bar{\mathbf{H}}_{k}^{\mathrm{T}}\bar{\mathbf{H}}_{k})\mathbf{y}_{k}=r_{k} \bar{\mathbf{H}}_{k}^{\mathrm{T}}\mathbf{e}_{1}-r_{k}[\bar{\mathbf{H}}_{k}^{ \mathrm{T}}\bar{\mathbf{H}}_{k}]_{1}\)
7: \(\mathbf{u}_{k}=\mathbf{r}_{k}+\mathbf{V}_{k}\mathbf{y}_{k}\)
8: \(\mathbf{x}_{k+1}=\mathbf{x}_{k}+\frac{\mathbf{r}_{k}^{\mathrm{T}}\mathbf{A} \mathbf{u}_{k}}{\|\mathbf{A}\mathbf{u}_{k}\|^{2}}\mathbf{u}_{k}\)
9: If \(\|\mathbf{r}_{k+1}\|<\varepsilon\), stop
```  
**Algorithm 9** A-GMRES(m)  
```
1: Give \(m_{0}\), \(m_{1}\), \(\mathbf{x}_{0}\), and \(\varepsilon\)
2: Do \(j=1,\ldots\)
3: Do \(m=m_{0},\ldots,m_{1}\) (\(k=m-m_{0}\))
4: \(\mathbf{r}_{k}=\mathbf{b}-\mathbf{A}\mathbf{x}_{k}\), \(r_{k}=\|\mathbf{r}_{k}\|\)
5: \(\mathbf{v}_{1}^{k}=\frac{\mathbf{r}_{k}}{\|\mathbf{r}_{k}\|}\)
6: \(\mathbf{V}_{k}=[\mathbf{v}_{1}^{k},\ldots,\mathbf{v}_{m}^{k}]\) (by Arnoldi process)
7: \((\bar{\mathbf{H}}_{k}^{\mathrm{T}}\bar{\mathbf{H}}_{k})\mathbf{y}_{k}=r_{k} \bar{\mathbf{H}}_{k}^{\mathrm{T}}\mathbf{e}_{1}-r_{k}[\bar{\mathbf{H}}_{k}^{ \mathrm{T}}\bar{\mathbf{H}}_{k}]_{1}\)
8: \(\mathbf{u}_{k}=\mathbf{r}_{k}+\mathbf{V}_{k}\mathbf{y}_{k}\)
9: \(\mathbf{x}_{k+1}=\mathbf{x}_{k}+\frac{\mathbf{r}_{k}^{\mathrm{T}}\mathbf{A} \mathbf{u}_{k}}{\|\mathbf{A}\mathbf{u}_{k}\|^{2}}\mathbf{u}_{k}\)
10: If \(\|\mathbf{r}_{k+1}\|<\varepsilon\), stop
11: Otherwise, \(\mathbf{x}_{0}=\mathbf{x}_{k+1}\); go to 2
```  
**Algorithm 8** A-GMRES
## 2 Background and Related Work
* **Environment:** The place of collecting mouse behavior data. * **Mouse action:** Characteristics of the actions received from the mouse input device for a specific user while interacting with a specific graphical user interface. * **Type of Study:** Continuous authentication, intrusion detection, or static authentication. * **Data used:** The dataset that was used for this study. * **FAR:** False acceptance rate. * **FRR:** False rejection rate. * **EER:** Equal error rate. * **Note:** Additional information. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Paper** & **\# User** & \
\begin{tabular}{c} **Data** \\ **Period** \\ \end{tabular} & **Environment** & **Action** & **Type of Study** &
\begin{tabular}{c} **Dataset** \\ **Used** \\ \end{tabular} & **FRR** & **FAR** & **EER** & **Note** \\ \hline
[26] & 30 & 1–2 weeks & Controlled & MM-PC &
\begin{tabular}{c} Continuous \\ Authentication \\ detection \\ detection \\ \end{tabular} & Collected & 0.86\% & 2.96\% & 1.3\% & 25 clicks \\
[27] & 25 & N/A & Uncontrolled & MM-PC-DD &
\begin{tabular}{c} Continuous \\ Authentication \\ \end{tabular} & Collected & 17.66\% & Not given & 8.53\% & 30 actions \\
[22] & 10 & N/A & Uncontrolled & MM-PC-DD &
\begin{tabular}{c} Intrusion \\ detection \\ authentication \\ \end{tabular} & Balabit & N/A & N/A & 0.04\% & 20 actions \\
[28] & 39 & N/A & Controlled & MM &
\begin{tabular}{c} Static \\ authentication \\ \end{tabular} & Collected & 5.26\% & 4.59\% & N/A & 4 gestures \\
[29] & 58 & N/A & Controlled & MM-PC &
\begin{tabular}{c} Anomaly \\ detection \\ \end{tabular} & Collected & N/A & N/A & 11.63\% & 16.1 s \\
[30] & 30 & 37 min & Uncontrolled & MM-PC &
\begin{tabular}{c} Continuous \\ authentication \\ \end{tabular} & Collected & 1.3\% & 1.3\% & 1.3\% & 20 clicks \\
[31] & 28 & 5/10 min & Uncontrolled & MM-PC-DD &
\begin{tabular}{c} Continuous \\ authentication \\ \end{tabular} & Collected & 7.78–2.75\% & 9.45–3.39\% & N/A & N/A \\
[32] & 31 & 1 day & Controlled & MM-PC-DD &
\begin{tabular}{c} Continuous \\ Continuous \\ \end{tabular} & Collected & 2.10\% & 2.24\% & N/A & N/A \\
[33] & 52 & One week & Uncontrolled & MM-PC-DD &
\begin{tabular}{c} Continuous \\ Authentication \\ \end{tabular} & Collected & N/A & N/A & N/A & N/A \\
[20] & 50 & 10–15 min & Controlled & MM-PC &
\begin{tabular}{c} Static \\ authentication \\ \end{tabular} & Collected & 2\% & 2\% & 0.020 & 50 strokes \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Previous characteristics of the most important existing works.
# Quality-of-Service-Linked Privileged Content-Caching Mechanism for Named Data Networks
## 5 Results and Discussion
### Content Store Size and Hit Rate
#### 5.3.1 Content Store Size and Hit Rate for QoS Traffic  
The simulation evaluates the content store hit rate corresponding to the size of a content store of QoS nodes for the entire network, considering only the priority content among all other traffic. QLPCC outperforms EQPR in the range of 6% to 10%, PRR in the range of 7% to 9%, the probability cache scheme in the range of 37% to 40%, the LFF scheme in the range of 41% to 45%, and the LFU scheme in the range of 44% to 47%. The results are consistent when the network nodes are scaled from \(|M|\) = 500 to \(|M|\) = 1000. The content store size of the QoS nodes ranges from 50 MB to 250 MB, as shown in the X axis of the graphs in Figure 5.  
Figure 5: Content store size vs. hit rate for QoS traffic. (**a**) Content store size vs. hit rate (QoS traffic) for \(|M|\) = 500. (**b**) Content store size vs. hit rate (QoS traffic) for \(|M|\) = 1000.
## 3 Temperature-Aware Routing Protocols
### RAIN (Routing Algorithm for Network of Homogeneous and ID-less Biomedical Sensor Nodes)  
RAIN [26] stands for Routing Algorithm for Network of Homogeneous and ID-less Biomedical Sensor Nodes. The main difference between RAIN and the above routing protocols is that in RAIN, the nodes are ID-less. It is argued here that the overhead of ID maintenance is very high, and in sensor networks, how to get the sensed data from the network is more important than knowing the node ID itself. That is why the approach of using and ID-less network is proposed.  
The word ID-less here does not mean it really does not use node an ID at all. It uses, instead of a static ID, a temporary ID. This ID is generated during the setup phase of protocol operation. Each node has a random number generator that generates a random number between one and (2\({}^{16}\)-1), which will be its node ID during operational lifetime. Since this protocol does not maintain global addressing, it relies on local addressing and coordination. Each node needs to know only its neighbor's ID; thus, there might be two identical node IDs in two different localities in the network. Nonetheless, this does not affect the network performance.  
In this protocol, a mechanism to prevent a node from sending a duplicate packet is implemented. The algorithm will check whether or not the received packet ID is already in the queue. If it is, then the packet will be dropped. Hop count check is also implemented to prevent packets from going around the network uselessly. This protocol deals with node temperature by using a probability to send a packet to the next hop nodes. Each next hop node is assigned a probability value that is inversely proportional with the estimated temperature of the nodes. This way, the \"coolest\" neighbor node is likely to be chosen as the next hop node.  
Another main mechanism implemented in RAIN is Status Update, a mechanism implemented to mitigate the \"Energy Hole\" problem in the network. The \"Energy Hole\" problem occurs when the nodes around the sink become depleted of energy very fast, due to receiving and forwarding packets coming from the network. To mitigate this, the sink node will broadcast a status update to its neighbors, which contains the packet ID of the received packets by the sink node. This will prevent neighbor nodes from forwarding multiple copies of the same packet to the sink node, thus saving a lot of energy.
## 4 Discussion
### Uncertainty of RS-Based GPP, ET, and eWUE Estimations
Their almost consistent performance in ET estimations could be attributed to the fact that two products calculate ET based on the Penman-Monteith algorithm logic. However, the PMLv2 and MOD16 products systematically overestimated and underestimated the observed ET, respectively. One possible reason may be that the two products use different meteorological datasets to drive the model. Take the MOD16 product for example, the MOD16 product using the GMAO meteorological inputs shows a more significant underestimation of observed ET than that using tower-based meteorological inputs [9]. The VPD from the GMAO meteorological data (with a coarse spatial resolution) may inadequately monitor water stress over smaller subregions, leading to uncertainty in the MOD16 product [35]. In addition, some previous studies attributed the general underestimation of ET in Continental U.S. (CONUS) croplands from the MOD16 product to model parameterization uncertainty [36; 37]. Re-calibrating the model parameters according to C3 and C4 crop species could enhance the accuracy of the MOD16 algorithm in CONUS croplands [38]. Note that all these problematic issues in the MOD16 product may also occur in the PMLv2 product in ET estimation. The PMLv2 product has been proven to outperform the MODIS product in eight-day eWUE estimations for both C4 maize and C3 soybean (see Figure 6). The MODIS product commonly exhibited noticeable overestimations and underestimations under low and high observed eWUE for both maize and soybean, respectively. During the peak of growing seasons (characterized by high observed eWUE), both the MOD17 and MOD16 products underestimated observed GPP and ET, respectively; however, the degree of underestimations of the observed GPP was larger than that of the observed ET. Therefore, the eWUE calculated from the MODIS product was still lower than the observed eWUE. During the start and end of growing seasons (characterized by low observed eWUE), the MOD17 and MOD16 products overestimated and underestimated observed GPP and ET, respectively, thus leading to the overestimations of observed eWUE. By contrast, the PMLv2 product achieved significantly more satisfactory performance in eWUE estimations and only slightly underestimated the observed eWUE during the peak of growing seasons for maize and slightly overestimated the observed eWUE during the start and end of growing seasons for both maize and soybean. The underestimations of observed eWUE for maize mainly stem from the underestimation of GPP from the PMLv2 product, which has been discussed above. The slight overestimations of observed eWUE for soybean can be explained by that the overestimations of GPP were more pronounced than that of ET and the small value of ET.
# A Low Sampling Rate Receiver Design for Multi-Antenna Multi-User OFDM Systems
## 2 OFDM Systems
### Precoding  
In the conventional multi-antenna OFDM systems, the precoding is generally performed on a per subcarrier basis, where \(x_{m,n}\) is merely a scaling of \(d_{n}\):  
\[x_{m,n}=d_{n}w_{m,n}, \tag{10}\]  
for \(1\leq m\leq M,0\leq n\leq N-1\). In case the \(n\)-th subcarrier is allocated to user \(k\), the signal received by user \(k\) at this subcarrier is the \(n\)-th element of \(\mathbf{Y}_{k}\), which can be expressed as:  
\[Y_{k,n}=\sum\limits_{m=1}^{M}\tilde{H}_{k,m}^{n}x_{m,n}+Z_{k,n}=d_{n}\sum \limits_{m=1}^{M}\tilde{H}_{k,m}^{n}w_{m,n}+Z_{k,n}, \tag{11}\]  
for \(n=0,1,\cdots,N-1,\ k=1,2,\cdots,K\). To maximize SNR, the precoding coefficients are set as:  
\[w_{m,n}=\frac{\left(\tilde{H}_{k,m}^{n}\right)^{*}}{\left\|\tilde{\mathbf{H}}_{k} ^{n}\right\|}, \tag{12}\]  
for \(n=0,1,\cdots,N-1,\ m=1,2,\cdots,M\), where \(\tilde{\mathbf{H}}_{k}^{n}=(\tilde{H}_{k,1}^{n},\tilde{H}_{k,2}^{n},\cdots,\tilde{ H}_{k,M}^{n})^{\text{T}}\). In other words, the maximal ratio transmission (MRT) [32] is adopted at each subcarrier.
# Finite Element Analysis and Optimization of Hydrogen Fuel Cell City Bus Body Frame Structure
## 3 Simulation Analysis of Static and Dynamic Characteristics of Bus Body Frame
### Validation of Simulation Results after Optimization  
Due to the size optimization method, the mathematical model was only designed and optimized for two critical target conditions, namely, the ultimate torsion and emergency cornering conditions. However, it did not take into account the potential impact of changes in the body frame's dimensions on the horizontal bending and emergency braking conditions, as well as the modal natural frequencies. Therefore, it is necessary to perform a static analysis for the optimized finite element model of the body frame under these two additional conditions, and also conduct modal analysis to validate the safety of the optimized model and the effectiveness of the optimization method.  
The static simulation results of the optimized body frame under the horizontal bending and emergency braking conditions are presented in Figure 12.  
Figure 12: Cont.  
As shown in Figure 12a, under the horizontal bending condition, the maximum stress value decreases to 207.3 MPa, which is lower than the yield strength of its material Q345 (345 MPa), further enhancing the strength performance of the bus body frame. Additionally, as illustrated in Figure 12b, the maximum displacement reduces to 6.116 mm, further improving the stiffness performance of the bus body frame in this condition. Moving on to Figure 12c, under the emergency braking condition, the maximum stress value decreases from 305.6 MPa to 262.4 MPa, indicating a significant improvement in the strength performance of the bus body frame after size optimization. Furthermore, Figure 12d demonstrates that the maximum displacement also decreases to 8.084 mm, enhancing the stiffness performance of the body frame during emergency braking. The static simulation analysis conducted under the other two typical conditions verifies the effectiveness of the size optimization method employed in this study in enhancing the strength and stiffness performance of the bus body frame.  
Figure 12: Stress and displacement contour plots for bus body frame under optimized horizontal bending and emergency braking Conditions: (**a**) stress contour plot under horizontal bending condition; (**b**) displacement contour plot under horizontal bending condition; (**c**) stress contour plot under emergency braking condition; (**d**) displacement contour plot under emergency braking condition.
# Synthesis of Star Nanoparticles and Their Applications for Bioactive Compound Encapsulation
## 2 SNP Preparation Method
### Sacrificial Template  
This method is especially used for the synthesis of hollow or porous particles. Up until now, hollow nanoparticles (HNPs) have been prepared by creating a void space inside of a solid precursor through a variety of physical and chemical techniques, such as the sacrificial template method, nozzle reactor processes, emulsion polymerization, and phase separation [42]. Among these, the hard templating strategy is the simplest and most widely used method [43, 44]. This approach typically consists of coating the outer surface of a material that will act as a template with a polymeric material, in this case starch, and subsequently, the material used as a template will be removed until a hollow particle is obtained [45, 46, 47] as it can be seen in Figure 6.  
For this purpose, it is necessary to add a suspension containing the particles that will act as a template to a solution in which the gelled starch is found, which will lead to the precipitation of the starch and its deposition on the template particles. Finally, a change in the medium is produced, such as a change in pH or temperature that will cause the dissolution of the material that acts as the template, thus producing the hollow SNPs. The final conditions of the hollow particles are limited by the size and shape of the starting template, while the thickness of the coating material is determined by its coating process. In 2017, [PERSON] et al. reported this method for the synthesis of hollow SNPs using gelled starch as the shell where different concentrations of CaCO\({}_{3}\) as hard templates were used. SNPs with diameters ranging from 30 to 300 nm and a shell thickness of 5-10 nm were obtained [48].
# A Game-Theoretic Rent-Seeking Framework for Improving Multipath TCP Performance +
## 5 Evaluation
### Heterogeneous Paths
#### 5.4.1 Observation 4  
The results from Scenario 4 are summarized as follows. The proposed _RSF MPTCP_ outperformed the other MPTCP algorithms when the paths had heterogeneous bandwidths.  
Next, Scenario 5 considered paths with heterogeneous delay characteristics. We set equal bandwidths for the two paths (20 Mbps) and the same random packet loss probability (3%) for both paths. We fixed the delay for one of the paths at 20 ms and then varied the delay of the other path from 20 to 100 ms. As before, we observed the RSF-DB throughput when downloading a 10 MB file.
# An Experimental Tethered UAV-Based Communication System with Continuous Power Supply
## 2 Background
### The Role of UAVs in Wireless Communications
#### 2.2.1 Emergency Services  
In emergencies caused by natural disasters such as floods, earthquakes, hurricanes, or fires, UAVs can intervene to provide a communications system that operates autonomously or as a complementary infrastructure. This alternative is crucial when the communication infrastructure becomes inoperative or is destroyed due to the impact of the disaster [21]. An alternative to solve this situation is to deploy UAVs to replace BSs.  
Another way to use UAVs in emergencies is to implement them as relay systems to connect groups of isolated or dispersed users. This approach allows UAVs to operate as UAV-BSs, facilitating communication between mobile devices without Internet access. Using a relay bridge, UAVs can extend network coverage in affected areas, allowing users to share critical information, even without terrestrial communication infrastructure [22].
# Effect of Surfactant on Water Content of Phosphogypsum
## 3 Results and Discussion
### Effect of Surfactant on Leaching Efficiency  
The effect of addition of surfactant on the leaching efficiency of phosphate was investigated and the results are shown in Table 9. The leaching efficiency was increased by about 3.32% (SDBS and SDS), 1.35% (PDMS), 3.49% (SDBS+SDS), 4.36% (SDBS+SR) and 2.49% (SDBS+PDMS), respectively. The addition of surfactant decreased the surface tension and contact angle, favoring the mass transfer during the leaching process, which improved the leaching efficiency. The addition of surfactant could increase the interfacial energy and hinder the formation of crystal nucleus, which avoided the formation of fine grain and improved the decomposition rate of phosphate ore.
## 4 Genetic Diagnosis of VCP-MSP
### Interpreting Genetic Test Results  
The general principles of interpreting genetic variants in \(VCP\) are similar to those for other diseases. Most commonly, the American College of Medical Genetics (ACMG) criteria [126] are applied to reach a determination.  
**(1)**: Positive: A positive result means that a variant in \(VCP\) known to be associated with VCP-MSP has been identified in the patient (i.e., a \"pathogenic\" or \"likely pathogenic\" variant). Thus far, all confirmed pathogenic variants in \(VCP\) are missense. A positive result may confirm the diagnosis in a person who meets clinical criteria or provide a pre-symptomatic diagnosis for an unaffected individual. If an unaffected individual has a positive result, there is a 90% chance that he or she will experience one or more of the features of VCP-MSP by the age of 45 [121]. Onset of symptoms is age-dependent and pre-symptomatic patients should be monitored by a healthcare professional on a regular basis.
**(2)**: Negative: A negative result means that no pathogenic variant has been identified in \(VCP\). Depending on the test that was performed, and the purpose of testing, the implications of this result may vary:  
**(a)**: Affected Individual: This means their condition is not caused by a mutation in \(VCP\), but it remains possible that a mutation in another gene that was not tested could be responsible.
**(b)**: Unaffected individual in a family with a known pathogenic \(VCP\) mutation: In this situation, a negative result means that the person does not have the mutation and, therefore, will not develop VCP-MSP. The individual will not require follow-up care or monitoring for symptoms as they are confirmed to be unaffected and cannot pass it on to their children.
**(c)**: Affected individual in a family having a pathogenic \(VCP\) mutation: Individuals who are symptomatic but are non-carriers of the familial mutation may also have a \"phenocopy\" syndrome that should be considered in this situation [127]. Causes of phenocopy syndromes could be due to diagnostic confusion with acquired disease, a different genetic diagnosis, or non-organic symptoms.