esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Analysis of Field Test Results
### Machine Learning Residual Estimation
#### 4.3.1 Inflation Factor Calculation for Tail-over-Bounding  
Figure 11 shows that the proportions of central area within the \(1\sigma\) and \(2\sigma\) bounds for both the normalized HPF and LPF residual distributions are similar to those of the standard normal distribution. However, the tails of the normalized residual distributions are heavier than the standard normal distribution as shown in Figure 12, potentially leading to an increased number of false alarms. To address this issue, the estimated residuals must be inflated.  
In this study, we demonstrate the computation of the inflation factor and resulting threshold to meet a false alarm requirement of \(10^{-5}\), which has been used in various previous works [41; 52; 53]. We computed the terms from the training dataset and then applied them to the test dataset to verify the false alarm probability. The coverage factor, \(k\), needed to meet the requirement of \(10^{-5}\) is 4.4172 for both sides of the standard normal  
Figure 11: Pdf for the (**a**) HPF and (**b**) LPF residuals normalized with machine learning model estimates.  
Figure 12: Zoomed in plots for the left tails of the CDF for the (**a**) HPF and (**b**) LPF. The standard deviation needs to be inflated to over-bound both the HPF and LPF normalized residuals.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & \(\pm\)**1** & \(\pm\)**2** \\ \hline ZBDD & 0.087 & 0.184 \\ \hline Elevation & 0.547 & 1.836 \\ \hline C/No & 0.990 & 1.000 \\ \hline SBDD Machine Learning & 0.731 & 0.953 \\ \hline Unit Normal Distribution & 0.686 & 0.954 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Probability bounding for \(\pm 1\) and \(\pm 2\) of each normalized model for the test data.  
distribution. If the estimated residuals accurately reflect the tail area with the given probability, then 99.999% of the normalized residuals should fall inside the bounds.  
To calculate the inflation factors for the HPF and LPF residuals, we searched for the actual residuals in the normalized training dataset that correspond to half of \(10^{-5}\) at each tail end, as expressed in (19). We chose the larger of the two values to obtain the conservative inflation factors, which were 8.31 for HPF and 7.82 for LPF. Finally, the inflation factors of HPF and LPF were computed to be 1.88 and 1.77, respectively, using (20).
## 1 Introduction  
Aluminium and its alloys have gained significant importance through their vast applications in various manufacturing industries [1]. However, with current trends in dry machining, aluminium alloys tend to adhere to the cutting tool, which results in tool wear [2]. The wear mechanisms can affect hole quality by producing rough surfaces, additional burrs around the edges, roundness deviation, cylindricity, and high cutting forces [3; 4; 5]. According to [PERSON] et al. [6], carbide tools have been proven to perform better for the dry machining of aluminium alloys than high-speed steel (HSS) tools. However, carbide tools have limited performance at high cutting speeds, which would ultimately affect productivity. Therefore, cutting tools are usually coated using materials with a high hardness to resist wear mechanisms. In this regard, different coatings and machine tools have been widely researched to make the drilling process more effective and to produce high-quality holes [4]. Some of the previous studies on evaluating different types of tool coatings on the machinability of Al2024 alloy are discussed in Table 1.  
As it can be seen from Table 1, the research on tool coating in the machining of aluminium in general and Al2024 alloy in particular is limited and most of the studies are focused only on either one-shot drilling or other machining processes. The above studies also show that the use of coating tools for drilling aluminium alloys is sometimes contradictory and still inadequate. Furthermore, no study is available in the open literature for the use of coated drills using the multi-hole simultaneous drilling approach. The multi-hole simultaneous drilling operation is performed using the multi-spindle drilling head to reduce machining time and increase productivity without compromising hole quality, which are the key factors for the machining process [12; 13; 14; 15]. Therefore, further research is required to find suitable cutting tools and coatings for optimum machining of aluminium alloys and evaluate their performance when several coated tools are used simultaneously to create multiple holes in one go. In a study by [PERSON] et al. [16], a single drilling process was compared with multi-spindle drilling using HSS drills. It was concluded that with the same drilling parameters, the multi-spindle drilling performed better than the single drilling process by providing a lower thrust force, lower surface, small burrs, short chips, and low built-up edges. In another study [17], apart from the surface, the deviation of hole from the nominal size was investigated. Additionally, the Taguchi method for optimization and the fuzzy logic approach for prediction of surface roughness \((R_{a})\) and hole size were used. The low spindle speed and feed rate were recommended for high-quality holes in the multi-spindle drilling process. Furthermore, the machinability of Al2024 was found to be better when compared with Al6061 and Al5083 during multi-spindle drilling [18]. Additionally, carbide tools with a high point angle were recommended in multi-spindle drilling of Al2024 as compared to HSS drills [19].
# Valorization of Agri-Food Waste into PHA and Bioplastics: From Waste Selection to Transformation
## 3 Production of Bioplastics from Volatile Fatty Acids
### Physico-Chemical Properties of Bioplastics and Their Applications
#### 3.3.2 Other Bioplastics
The \(\alpha\)-form is the most prevalent and forms under unstressed conditions, whereas the \(\beta\)-form emerges under stressed conditions and can revert to the \(\alpha\)-form when the stress isremoved [222; 223; 224; 225; 226]. Its melting point is between 90 and 120 degrees Celsius. Furthermore, it exhibits excellent thermal stability, enabling processing in extrusion and injection molding applications across a temperature range of 160 to 200 \({}^{\circ}\)C [227]. In terms of applications, bio-based polybutylene succinate (PBS) has a wide range of potential uses in various fields, including:  
* Agricultural much and packaging films: Its biodegradability and flexibility make it suitable for use in agricultural and packaging applications [228; 229; 230]. * Disposable tableware and utensils, including tableware [231]. * Drug encapsulation and orthopedic applications [232; 233]. Their use has been compared to that of plastics derived from fossil fuels. [PERSON] et al. [234] investigated PLA/PBS-based blends both in the laboratory and on a semi-industrial scale and showed subsequent plasticization migration [234; 235]. The films produced showed higher flexibility, elongation at break, and tear strength than pure PLA, and their performance was competitive with fossil-based polyolefins. PBS has a broad temperature range and can be processed similarly to polyolefins within the range of 160 \({}^{\circ}\)C to 200 \({}^{\circ}\)C [236]. * Poly(propylene carbonate) (PPC)  
PPC is a copolymer of carbon dioxide (CO\({}_{2}\)) and propylene oxide (PO). It is a thermoplastic aliphatic polymer with high barrier properties. Also, it is flexible and biocompatible [237]. Its thermal stability is low [238], and it is an amorphous polymer with low intermolecular forces, so its mechanical properties are poor [239]. In fact, in the literature, several studies have been carried out to enhance the thermal and mechanical properties of the PPC by physical or chemical means, resulting in modified PPC composites with improved properties [240]. Current applications include packaging films, foams and controlled drug release systems, high barrier materials including cryogenic packaging applications, and agricultural films [241; 242].
# An Efficient and Robust Topology Optimization Method for Thermoelastically Damped Microresonators
## 3 Efficient and Robust Topology Optimization Method
### Projection-Based Model Reduction Method
For the system matrix \(\mathbf{A}\), the computational complexity of solving the left and right multiplication of the basis vectors is \(4 LrN_{s}b\), where \(b\) is the half-bandwidth of the combination matrix, \(r\) is the number of basis vectors, and \(L\) is the order of eigenvalues to be solved; the half-bandwidth \(b\) is approximately proportional to \(N_{s}^{0.5}\)[41]. Therefore, the computational complexity of the complex modal analysis process for thermoelastic structures is \(O(4 LrN_{s}^{1.5})\). Thus, for large-scale problems with a large \(N_{s}\), since \(r\ll N_{s}\) and \(L\ll N_{s}\), the projection-based model reduction method based on the Neumann series subspace \(\mathcal{R}_{r}\) has lower computational cost compared to conventional solving methods. However, the projection-based model reduction method based on the Neumann series subspace \(\mathcal{R}_{r}\) cannot be directly used in the optimization process due to the _instability and convergent problems_.
## 7 Application Layer  
Techniques discussed so far constitute an adaptive infrastructure which physically delivers data around _IoT_ and hides underlying matters such as spectrum utilization and congestion control from the application layer. Accordingly, this layer is typically only concerned with the Internet services.
## 3 Natural Coordinates Formulation (NCF)
### Obtaining the Mass Matrix of the Rigid Body Using the NCF  
The mass matrix for rigid bodies can be obtained by integrating the density-weighted mass contributions throughout the body's volume using the Natural Coordinates Formulation (NCF), in which the position of points directly defines the rigid body configuration. The mass matrix is computed as follows:  
\[\boldsymbol{M}=\rho\int_{V}C^{T}\boldsymbol{C}\,dV \tag{8}\]Explicity, the mass matrix can be written as  
\[\mathbf{M}=\begin{pmatrix}\left(m+\frac{I_{x}}{d^{2}}-\frac{2m\delta_{C}}{d}\right) \mathbf{I}&\left(\frac{m\delta_{C}}{d}-\frac{I_{x}}{d}\right)\mathbf{I}&\left(m \overline{\varepsilon}_{G}-\frac{I_{y}}{d}\right)\mathbf{I}&\left(m\overline{ \varepsilon}_{G}-\frac{I_{y}}{d}\right)\mathbf{I}&\left(m\overline{ \varepsilon}_{G}-\frac{I_{y}}{d}\right)\mathbf{I}\\ \left(\frac{m\overline{\varepsilon}_{G}}{d}-\frac{I_{y}}{d}\right)\mathbf{I}& \left(\frac{I_{y}}{d}\right)\mathbf{I}&\left(\frac{I_{y}}{d}\right)\mathbf{I}& \left(\frac{I_{y}}{d}\right)\mathbf{I}&\left(\frac{I_{y}}{d}\right)\mathbf{I} \\ \left(m\overline{\varepsilon}_{G}-\frac{I_{y}}{d}\right)\mathbf{I}&\left( \frac{I_{y}}{d}\right)\mathbf{I}&\left(I_{y}\right)\mathbf{I}&\left(I_{y} \right)\mathbf{I}&\left(I_{y}\right)\mathbf{I}\\ \left(m\overline{\varepsilon}_{G}-\frac{I_{y}}{d}\right)\mathbf{I}&\left( \frac{I_{y}}{d}\right)\mathbf{I}&\left(I_{y}\right)\mathbf{I}&\left(I_{z} \right)\mathbf{I}&\left(I_{z}\right)\mathbf{I}\end{pmatrix} \tag{9}\]  
where \(m\) is the mass of the rigid body, \(\rho\) is the mass density, and \(d\) is the distance between points \(i\) and \(j\). \(\varsigma_{G}\), \(\overline{\gamma}_{G}\), and \(\overline{\varepsilon}_{G}\) are the local coordinates of the center of gravity of the rigid body. \(\mathrm{I}_{x}\), \(\mathrm{I}_{y}\), and \(\mathrm{I}_{z}\) are related to the moments of inertia of the rigid body, and \(\mathrm{I}_{yy}\), \(\mathrm{I}_{yz}\), and \(\mathrm{I}_{xz}\) are the corresponding products of inertia.  
It is important to note that the NCF-based mass matrix depends on the mass distribution and the selected coordinates. Unlike other formulations for modeling rigid body motion, no rotational parameters or time-dependent transformations are used. As shown in Equation (9), the use of the NCF results in a constant mass matrix derived from the shape function matrix of the body [17].
# Performance Evaluation of MPTCP on Simultaneous Use of 5G and 4G Networks
## 2 An Overview of The MPTCP Schedulers and Congestion Control Algorithms  
MPTCP is an extension of TCP that enables simultaneous data transfer through multiple communication interfaces. Each data transfer path between the end devices (i.e., sender and receiver) is regarded as subflow (SF). MPTCP schedulers and CCAs are the two key elements to ensure balanced network sharing, reduced latency, and high throughput. This section briefly describes some existing key MPTCP schedulers and CCAs.
# The Principle and Downhole Testing of Water Injection Exploration in Depleted Reservoirs
## 1 Introduction
### Analysis and Discussion of Water Injection Results
#### 3.4.3 General Pattern Analysis  
Based on the analysis above, whether near the goaf or near the old roadway, there is a phenomenon of decreasing water injection pressure and increasing permeability as the distance from the goaf decreases. The main reason for this phenomenon is the existence of pressure relief and disturbance damage near the goaf or old roadway.  
In the original undamaged rock segment, the ground stress is normal (minimum stress greater than 10 MPa). The characteristics of water injection in this segment are as follows: the water injection pressure can be increased to 10 MPa, but the flow rate is very small and difficult to increase. The permeability is relatively small, as expected for the permeability of the original rock.  
In the segment affected by pressure relief and disturbance near the goaf and old roadway, the surrounding rock stress decreases due to the pressure relief caused by the roadway or goaf (minimum stress less than 10 MPa, generally around 5 MPa). The characteristics of water injection in this segment are as follows: compared to the first segment, the pressure significantly attenuates, and the water injection pressure has difficult exceeding the surrounding rock pressure (5-6 MPa); the overall permeability is greater than the permeability  
Figure 10: Box plot of permeability coefficient at different positions of borehole 155.  
in the original state, and as the injection pressure increases, the natural fractures expand under the action of water injection, leading to a significant increase in permeability, even by an order of magnitude. Moreover, as the distance to the goat (or old roadway) decreases, the water injection pressure decreases and the permeability increases.  
Hole 151 indicates that within 20 m of the old roadway is an area affected by pressure relief and disturbance. Hole 153 indicates that within 34 m of the goat is an area affected by pressure relief and disturbance. Hole 156 is within the pressure relief and disturbance zone during the injection stage (10 m from hole 155).
# A Secure and Efficient Three-Factor Authentication Protocol in Global Mobility Networks
## 5 Proposed Secure and Efficient Authentication Protocol for GLOMONET
### Login and Authentication Process  
Before performing a session, the \(MU\) requests authentication to the \(HA\) in order to establish the session key. Figure 4 presents the user authentication process of our protocol. The process is described in detail as follow.  
**Step 1:**: The mobile device inputs \(ID_{MU}\), \(PW_{MU}\) and imprints biometrics \(BIO_{i}\). The \(MU\) computes \(R_{i}=Rep(BIO_{i},P_{i})\), \(RPW_{i}=h(PW_{MU}||R_{i})\), \(RID_{i}=h(ID_{MU}||RPW_{i})\), \(X_{i}=A_{i}\oplus h(RID_{i}||RPW_{i})\), and \(B_{i}^{*}=h(RPW_{i}||X_{i})\) and checks whether \(B^{*}\stackrel{{?}}{{=}}B_{i}\). If this holds, the \(MU\) generates a random nonce \(R_{MU}\) and computes \(M_{1}=X_{i}\oplus R_{MU}\), \(M_{2}=ID_{MU}\oplus X_{i}\) and \(Q_{M}=h(RID_{i}||X_{i}||R_{MU})\). After that, \(MU\) sends \(\{E_{1}\}\) to the \(FA\) over an open channel.
**Step 2:**: Upon reception of \(E_{1}\), the \(FA\) selects a random nonce \(R_{FA}\) and computes \(M_{3}=h(ID_{FA}||K_{FH}||M_{1})\oplus R_{FA}\), \(Q_{F}=h(ID_{FA}||K_{FH}||R_{FA}||M_{1})\). After that, the \(FA\) sends \(\{E_{2}\}\) to the \(HA\).
**Step 3:**: Upon reception of \(E_{2}\), the \(HA\) computes \(X_{i}=h(RID_{i}||K_{S}||R_{S})\), \(ID_{MU}=M_{2}\oplus X_{i}\) and checks the identity \(ID_{MU}\) of the mobile user. Then, \(HA\) computes \(R_{MU}=M_{1}\oplus X_{i}\), \(Q_{M}^{*}=h(RID_{i}||X_{i}||R_{MU})\) and checks whether \(Q_{M}^{*}\stackrel{{?}}{{=}}Q_{M}\). If it is valid, the \(HA\) calculates \(R_{FA}=M_{3}\oplus h(ID_{FA}||K_{FH}||M_{1})\), \(Q_{F}^{*}=h(ID_{FA}||K_{FH}||R_{A}||M_{1})\) and checks whether \(Q_{F}^{*}\stackrel{{?}}{{=}}Q_{F}\). Then,  
Figure 4: Login and authentication process of the proposed protocol.  
the \(HA\) computes \(M_{4}=R_{FA}\oplus h(RID_{i}||X_{i}||R_{M\text{U}})\), \(M_{5}=R_{M\text{U}}\oplus h(ID_{FA}||K_{FH}||R_{FA})\) and \(Q_{H}=h(R_{M\text{U}}||R_{FA}||K_{FH})\). Finally, the \(HA\) sends an authentication message \(\{E_{3}\}\) to the \(FA\).
4. Upon reception of \(E_{3}\), the \(FA\) computes \(R_{M\text{U}}=M_{5}\oplus h(ID_{FA}||K_{FH}||R_{FA})\), \(Q_{H}^{*}=h(R_{M\text{U}}||R_{FA}||K_{FH})\) and checks whether \(Q_{H}^{*}\stackrel{{?}}{{=}}Q_{H}\). If it is correct, the \(FA\) computes \(SK_{i}\)=\(h(R_{M\text{U}}||R_{FA})\), \(Q_{MF}=h(R_{M\text{U}}||R_{FA}||SK_{i})\) and sends \(\{E_{4}\}\) to the \(M\)U.
5. Upon reception of \(E_{4}\), the \(M\)U calculates \(R_{FA}=M_{4}\oplus h(RID_{i}||X_{i}||R_{M\text{U}})\), \(SK_{i}=h(R_{M\text{U}}||R_{FA})\), and \(Q_{MF}^{*}=h(R_{M\text{U}}||R_{FA}||SK_{i})\). Finally, the \(M\)U checks whether \(Q_{MF}^{*}\stackrel{{?}}{{=}}Q_{MF}\). If it holds, the \(M\)U and \(FA\) establish the \(SK_{i}\) successfully.
# Characterizing the Mechanical Performance of a Bare-Metal Stent with an Auxetic Cell Geometry
## 3 Results
### Longitudinal/Lateral Compression  
The force-displacement response of the stents under longitudinal buckling for three specimens presented in Figure 4e showed a close match for force values, while the location of local buckling differed. The force value increased up to 0.7 mm deflection for all specimens after this displacement, the force value dropped significantly, and the stent lost its stability because of local buckling effects. Auxetic stents locally collapsed; however, they kept their structure on to the buckling side, which allowed the auxetic stent to keep its opening rather than a completely collapse. The force-displacement graph for three specimens tested under lateral compression in Figure 4f showed a nonlinear behavior in the same trend. A monotonically decreasing parabolic force-displacement curve was observed for the first 0.5 mm deflection, while a monotonically increasing curvilinear behavior with much less stiffness up to 4 mm deflection was detected. As the stent was almost completely folded, stiffness increased and became steep for deflections larger than 4 mm.  
When all four loadings described above are evaluated together, a common trend for force-displacement (or torque-angle) is that the stent shows stiffness for the initial stages of loading up to a point where plastic deformations emerge, and stiffness decreases following plastic deformations. Then, after an extended plastic deformation stage, the stent loses its stability at some point and becomes collapsed. Auxetic re-entrant structure of stents brings the advantage of sustaining functionality longer even though stent suffers from an elongated plastic deformation.
## 2 Materials and Methods
### Segmentation of the Myocardium in Tagged CMR Images
#### 2.2.3 Network Architectures  
We compare the performance of two distinct network architectures widely used in medical segmentation tasks; nnUnet [8] and a segmentation Variational Auto-Encoder (VAE) based on residual networks (ResNet) [9], which we will call ResNetVAE. We used the open-source implementation of these network architectures found in the MONAI Python library [18]. Both of these architectures are based on an underlying encoder-decoder pipeline. First, the image is encoded in a latent space representation of lower dimensionality than the input. Then, this representation is decoded into a pixel-wise segmentation map.  
There are two main differences between the nnUnet and the ResNetVAE architectures. First, the nnUnet is larger, requiring approximately ten times more parameters to optimize. The nnUnet has 13 blocks (one input block, 5 downsample blocks, one bottleneck and 6 upsample blocks) and an according 26 activation functions compared to the ResNetVAE's  
Figure 3: CycleGAN architecture. Training was performed using unlabeled images from groups A and B (_cine_ and tagged MR images, respectively) for unpaired image-to-image translation. Each discriminator encourages the associated generator to transform images from one domain to outputs that are indistinguishable from the other domain. Additionally, training includes a pixel-wise cycle consistency constraint that minimizes the dissimilarity between arbitrary input images from one domain and its cyclic output.  
7 blocks and 16 activation functions. While additional capacity can increase performance, it can also lead to overfitting of the training data. The second main difference is that the latent space of the ResNetVAE is regularized as a normal distribution. This regularization leads to attractive properties in the decoding process as it constrains features in the latent space to have a low degree of inter-correlation. The initial image processing methodology consists of two steps: (1) resizing the image to 256 \(\times\) 256 (not necessary for the nnUnet) and (2) normalization of the input image using a mean of 0.456 and a standard deviation of 0.224.
## 4 Discussion
Indeed, the study found that expanding the dataset resulted in significant gains in segmentation accuracy, highlighting the importance of larger and more varied datasets in improving model performance. Furthermore, the WoundsDB 2D pictures suffered from low resolution and suboptimal framing, raising challenges for the network's training process and consequently affecting the recognition accuracy. Addressing these challenges through higher-quality image collection and improved framing is expected to significantly improve automated segmentation skills in future developments of this study. In addition, the lack of ground-truth data for the direct evaluation of 3D segmentation accuracy limits the final validation of the results. Future research should prioritize reliable ground-truth data to conduct more comprehensive assessments of 3D segmentation accuracy and evaluate the practicality of the suggested methodologies. Finally, clinical validation is an important step in future development. While this study provides a solid technological foundation, its ultimate significance will be assessed by its effectiveness and reliability in real-world clinical situations. A thorough evaluation across a wide range of patient groups and wound types should be conducted in future works to verify that the suggested procedures are both accurate and practicable for use in clinical settings. This validation procedure will validate the system's effectiveness in supporting healthcare professionals and suggest possible areas for further improvement to optimize its application in wound evaluation and treatment. Clinical validation will be fundamental in establishing the system as a trustworthy tool for improving patient outcomes by bridging the research-practice divide.