esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Analysis of Finite Element Simulation Results
### Comparison of Energy Consumption Capacities of Joints  
The energy dissipation capacity of a joint plays a vital role in determining the overall capacity of the structure to withstand seismic loads. It refers to the ability of the joint to absorb and dissipate energy during the damage process, effectively safeguarding its structural integrity. The energy dissipation capacity of joints is commonly assessed using the energy dissipation coefficient \(E\). Equation (1) represents the formula for calculating the energy dissipation coefficient \(E\). Furthermore, Figure 6 provides a schematic representation of the calculation method for evaluating the energy dissipation coefficient.  
\[E=\frac{S_{(ABC+CDA)}}{S_{(OBE+ODF)}} \tag{1}\]  
In this paper, the hysteresis loop energy dissipation coefficients for the yield state, limit state, and damage state were calculated separately. The calculation results are presented in Table 3.  
According to the information provided in Table 3, it is evident that the structural energy dissipation coefficient of the cast-in-place joint in the yield state surpassed that of the prefabricated joint. This was mainly due to the fact that the members exhibited a better energy dissipation performance in the cast-in-place joint when in the elastic stress phase before the yield phase. In contrast, the prefabricated joint experienced a weaker energy dissipation during the yield phase because the presence of a profiled steel structure restricted the displacement of the assembled joints in the elastic stress phase. However, under extreme load conditions, the prefabricated joint showed significantly a higher ultimate bearing capacity compared with the cast-in-place joint. Consequently, the energy dissipation capacity of the prefabricated joint outperformed that of the cast-in-place joint. Furthermore, it is noted that the energy dissipation coefficients of the joints were generally similar when the concrete strength was equal. This similarity arose because the concrete  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Joint Form** & **Yield State** & **Limit State** & **Damage State** \\ \hline Cast-in-place joint & 0.37 & 0.58 & 1.53 \\ Prefabricated joint & 0.31 & 0.66 & 1.54 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Calculation results of joint energy dissipation coefficient.  
Figure 6: Schematic diagram of joint energy dissipation coefficient.  
was damaged during the nodal damage phase, resulting in a comparable energy dissipation performance in both types of joints.
## 3 Numerical Simulation and Results
### Finite Element Model  
#### 3.1.1 Geometry and Boundary Conditions  
Finite element models based entirely on real specimens usually require millions of nodes or elements, resulting in high computational and time costs for thermal-structural coupling analysis. As the core components of the cutting energy-absorbing structure, the interaction between the tool and the energy-absorbing tube undertakes nearly all amounts of energy absorption. Therefore, the tool and part of the energy-absorbing tube are selected as the research objects in this paper. A thermal-structural coupling finite element analysis model of the tool and tube is developed by using the commercial software LS-DYNA, as shown in Figure 4a.  
Figure 4b depicts the geometry and boundary conditions of the tool-tube model. The energy-absorbing tube is simplified to one-sixth by symmetry and given a length of 180 mm to ensure that the cutting temperature reaches a steady state. The geometrical parameters of the tool and the test are identical. The maximum rake angle of the curved tool rake face is 55\({}^{\circ}\). The cutting width and depth are each 27 and 2 mm. The initial speed is 5 m/s, the same as the speed of the test, along the positive direction of the x-axis. The tool's surface in contact with other parts is constrained with all degrees of freedom except for x-directional movement. The boundary of the tube is fully constrained.
# A Nested UNet Based on Multi-Scale Feature Extraction for Mixed Gaussian-Impulse Removal
## 4 MSNUNet
### Overall Pipeline  
Figure 2 shows the architecture of MSNUNet for denoising images corrupted by mixed noise. Given an image \(I\in\mathbb{R}^{H\times W\times 1}\), where \(H\times W\) represents the spatial dimensions of the image, MSUNNet first applies a \(3\times 3\) Conv to extract low-level image features \(F_{0}\in\mathbb{R}^{H\times W\times C}\), where C denotes the number of channels. These features are then processed by a nine-block symmetric encoder-decoder structure to obtain the depth features \(F_{d}\in\mathbb{R}^{H\times W\times C}\). The encoder blocks, including encoder1 and encoder2, and the decoder blocks, including decoder1 and decoder2, are filled with MSU-Subnet, a well-configured U-shaped subnetwork. By utilizing MSU-Subnet, MSUNNet effectively captures local and global features at multiple scales from high-resolution images. In the subsequent blocks with lower-resolution feature maps, downsampling further would result in the loss of valuable image information. To address this issue, we employ MSCAM, which combines local feature extraction with a CAB, allowing the network to extract local features while capturing channel correlations. This approach enables MSUNNet to efficiently extract multi-level features within blocks and aggregate multi-level features between blocks. Specifically, starting from the low-level feature \(F_{0}\), the encoder gradually reduces the spatial size of the feature map while increasing the channel capacity. The decoder takes the potential feature \(F_{1}\in\mathbb{R}^{\frac{H}{\hbar\times W}{16}\times 16C}\) as input and learns the noise distribution while gradually recovering the image resolution. Pixel-unshuffling and pixel-shuffling operations are applied during feature downsampling and upsampling, respectively [30]. The encoder is connected to the decoder through a skip connection to facilitate the image recovery process [24]. Before this connection, the output of the decoder is upsampled, and a summation operation is used to ensure a consistent number of channels. These design choices have resulted in improved quality, as described in the experimental section (Section 5). Finally, a \(3\times 3\) Conv is applied to the output features of the final decoder to generate the residual image \(R\in\mathbb{R}^{H\times W\times 1}\). This residual image is then added to the noisy image \(I\) to obtain the restored image, \(\hat{I}=I+R\). In the following section, we will describe the core components of MSNUNet, including MSCAM and MSU-Subnet, which are used to extract multi-scale features from the image.
# Towards a Provably Secure Authentication Protocol for Fog-Driven IoT-Based Systems
## 2 Complexity Assumptions
### User Revocation Phase  
Several circumstances in the fog computing system require the \(\mathcal{U}_{k}\) revocation mechanism to revoke a compromised \(\mathcal{U}_{k}\)'s account from the system. Assume that once a \(\mathcal{U}_{k}\)'s memory device is lost, \(\mathcal{U}_{k}\) revokes their account with \(\mathcal{CSP}\) using the same \(ID_{k}\). The mentioned points are critical to execute the user revocation phase:1. \(\mathcal{U}_{k}\) randomly chooses a new \(r_{k}^{new}\in Z_{p}^{*}\), identity \(ID_{k}\) and computes \(RID_{k}^{new}=h(ID_{k}\oplus r_{k}^{new})\). \(\mathcal{U}_{k}\) sends \(ID_{k}\), \(RID_{k}^{new}\) to \(\mathcal{CSP}\) using a private channel.
2. After getting \(\mathcal{U}_{k}^{\prime}\)s request, \(\mathcal{CSP}\) computes the following values \(X_{k}=h(ID_{k}\|x)\), \(Y_{k}^{new}=RID_{k}^{new}\oplus X_{k}\). Afterwords, \(\mathcal{CSP}\) selects \(TID_{k}^{new}\) for the respective \(\mathcal{U}_{k}\) and calculates \(Z_{k}^{new}=h(X_{k}\|ID_{k}\|TID_{k}^{new}\|RID_{k}^{new})\) and stores \(ID_{k}\) in its database corresponding to \(TID_{k}^{new}\) using \(x\). \(\mathcal{CSP}\) then transmits \(\{Y_{k}^{new},TID_{k}^{new},Z_{k}^{new}\}\) to \(\mathcal{U}_{k}\) via a private channel.
3. After getting the message from \(\mathcal{CSP}\), \(\mathcal{U}_{k}\) computes \(\overline{Y_{k}^{new}}=(Y_{k}^{new}\|r_{k})\oplus ID_{k}\) and stores \(\{\overline{Y_{k}^{new}},Z_{k}^{new},TID_{k}^{new}\}\) in its tamper-proof device.
# Transmit and Receive Diversity in MIMO Quantum Communication for High-Fidelity Video Transmission
## 3 Methodology
### Quantum MIMO Transmission and Quantum Channel
#### 3.4.3 Depolarizing Noise  
Depolarizing noise simulates a complete loss of quantum information by mixing the input state with the maximally mixed state. With depolarization probability \(p_{d}\), the qubit is affected by random Pauli errors (that is, X, Y, or Z) according to Equation (16).  
\[\mathcal{N}_{D}(\rho)=(1-p_{d})\rho+\frac{p_{d}}{3}\Big{(}X\rho X^{\dagger}+Y \rho Y^{\dagger}+Z\rho Z^{\dagger}\Big{)} \tag{16}\]  
This model uniformly distributes noise across all three Pauli operators, X, Y, and Z.
# Protecting Hybrid ITS Networks: A Comprehensive Security Approach
## 4 Experimental Evaluation and Results Analysis
### Network Latency
#### 4.2.2 Latency Measurements Analysis: G5 Network  
The latency measurements of G5 communications (RSU and OBU) are reported in Table 10. DLAPP increases the latency by 6% and the MFSPV by 10%. The impact of the protocols on the G5 network is not very noticeable.  
Figure 19: Box plot of latency measurements in M_RSU \(\rightarrow\) Smartphone X communication flow.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Communication Flow**} & **No Security [ms]** & **DLAPP [ms]** & **MFSPV [ms]** \\ \hline M\_RSU & \(\rightarrow\) & Smartphone X & 23.08 & 25.23 & 24.66 \\ Smartphone X & \(\rightarrow\) & M\_RSU & 29.74 & 31.90 & 32.32 \\ Smartphone X & \(\rightarrow\) & Smartphone Y & 31.78 & 33.22 & 33.97 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Communications latency measurements results involving the cellular networks using different security approaches.
# Vibration and Derailment Analyses of Trains Moving on Curved and Cant Rails
## 3 Simple Theoretical Method for Wheel Forces, Wheel Sliding, and Car Overturning  
### Illustration of the Simple Theoretical Equations  
In this section, a simplified theoretical solution is derived to compare with the results of the finite element analysis discussed in the next section. In this simplified method, since the railway radius is much larger than the wheel interval, so we assume that the problem is two-dimensional to calculate the car velocity for the wheel sliding and car overturning in this simplified method. As shown in Figure 3, a carriage is moving on a canted rail system with a rail gauge (\(G\)) of 1.435 m, where the carriage includes four axles, two bogies, and a car body with the total weight of \(w_{1}\), \(w_{2}\), and \(w_{3}\), and the mass centers of them are \(H_{1}\), \(H_{2}\), and \(H_{3}\). The railway is divided into five segments including the straight line, clothoid curve, circular curve, clothoid curve, and straight line, respectively, and the geometric calculation of these curves can be found in the thesis [22]. The clothoid curve connects the straight line and circular curve with a radius ranging from infinite to a constant. Assume the carriage moves to a location with a velocity \(V\), radius of \(R\), and the cant angle of \(\theta\). For a smooth rail system, the cant angle should be inversely propositional to the current radius, and the following equation was used:  
\[\theta=Ct/R \tag{21}\]  
where \(Ct\) is a constant set by the designer. The total forces, \(F1_{i}\) and \(F2_{i}\), where \(i=1\), \(2\), and \(3\), at each mass center in the location direction \(1\) and \(2\) are:  
\[F1_{i}=W_{i}sin\theta-\left(\frac{W_{i}}{g}\right)V^{2}cos\theta/R \tag{22}\]  
\[F2_{i}=-W_{i}cos\theta-\left(\frac{W_{i}}{g}\right)V^{2}sin\theta/R \tag{23}\]The sliding will occur under the following condition:  
\[-\sum_{i=1}^{3}F1_{i}\geq-\mu\sum_{i=1}^{3}F2_{i}, \tag{24}\]  
where \(\mu\) is the coefficient of friction. The contact normal force (\(F_{N}\)) at each wheel is:  
\[F_{N}=-\sum_{i=1}^{3}\frac{F2_{i}}{N}\pm\sum_{i=1}^{3}F1_{i}H_{i}/(NG) \tag{25}\]  
(+ for the inner wheel and \(-\) for the outer wheel), where a positive \(F_{N}\) means in the contact mode, and otherwise, the equation cannot be used due to the separation of the wheel and rail, and \(N\) is the number of wheels, which is eight in this paper. When one of the wheel normal contact forces is equal to zero, the separation of the wheel and rail occurs, causing the overturning of the carriage. This condition often comes from the inner wheel. The shear force (\(F_{S}\)) at each wheel is:  
\[F_{S}=-\sum_{i=1}^{3}F1_{i}/N. \tag{26}\]
## 4 Proposed Framework
### Architecture Overview
#### 4.1.2 Edge Server Layer  
The edge server layer consists of \(m\) edge servers, which act as local processing hubs for data aggregation, preprocessing, and model training.  
Composition  
Each edge server \(E_{j}\) handles a cluster of IoT nodes, denoted as follows:  
\[E_{j}=\{D_{1},D_{2},\ldots,D_{k}\},\quad j=1,2,\ldots,m. \tag{5}\]  
Resposibilities
* **Data Aggregation:** Each edge server aggregates data from its associated IoT devices as follows: \[A_{j}=\bigcup_{i=1}^{k}D_{i}.\] (6)  
* The raw aggregated data \(A_{j}\) is cleaned and standardized using the ontology \(O\), as follows:
* _Data cleaning:_ Removes missing values (\(x\
otin\mathbb{R}\)) and outliers.
* _Unit conversion:_ Converts raw data into consistent units, e.g., \[T^{\prime}=\frac{5}{9}(T-32),\quad\text{for temperature in Fahrenheit.}\] (7)
* _Ontology mapping:_ Maps raw data fields \(f_{i}\) to ontology attributes \(o_{i}\) as follows: \[M:f_{i}\to o_{i},\quad\forall f_{i}\in A_{j},\;o_{i}\in O.\] (8)
* **Local model training:** Trains the FL model locally on \(A^{\prime}_{j}\) by minimizing the local loss as follows: \[\min_{w_{j}}f_{j}(w_{j})=\frac{1}{|A^{\prime}_{j}|}\sum_{x\in A^{\prime}_{j}} \ell(h(x;w_{j}),y),\] (9) where \(h(x;w_{j})\) is the prediction, \(y\) is the true label, and \(\ell\) is the loss function (e.g., mean squared error).
* **Communication:** Edge servers transmit their model updates \(\Delta w_{j}\) to the central unit as follows: \[\Delta w_{j}=w_{j}-w_{0},\] (10)where \(w_{0}\) is the global model received from the central unit.
# A Novel Software Architecture Solution with a Focus on Long-Term IoT Device Security Support
## 4 Device Architecture
### Service Mobile Application  
A service mobile application was developed to support the service technician in the field. The application allows the technician to read raw values from devices that have not yet been processed by the cloud, which can help the service technician troubleshoot the smart device. For example, the liquid flow sensor is stuck. After the service technician drains a certain amount of fluid through the python tube, a fault can be easily detected. A service mobile application is shown in Figure 13.  
In order for the service mobile app to connect to the device, a cloud service technician account is required to retrieve the secret key for the device so that no one can access it without authorisation. An additional security feature is the mobile application connection window of only 5 min after the device was powered on. This creates a new application layer that is the only access point for changing device parameters and prevents unauthorised access to the device through two-tier security.  
Using the mobile application, it is possible to set all the internal parameters of the device as well as update the firmware running on the device. After launching the mobile application, it is necessary to read the public key of the smart device, which is located on the device in the form of a QR code, after which the application contacts the cloud to see if the service technician logged into the application has the authorisation to service  
Figure 12: Cloud application interface.  
the device. When the mobile application is connected and the smart device is in service mode, any action from the mobile device to the smart device must be signed with the public and private keys. Parameters that can be loaded and configured on the device are wireless connection settings, GSM connection settings, firmware update, manual device control, sensor constants, and cloud connection settings. Parameters that cannot be set, but only loaded, are real-time sensor data. All communication between the service mobile application and the cloud is done over an HTTPS connection via RESTful web services.  
The service mobile application is not only used for service purposes but also for the regular maintenance of the device. The regular maintenance of the device mainly includes the sanitation of the device and calibration of the flow sensor of the device. During the periodic maintenance, which is required by the authorities, the service technician enters the date of maintenance and the actions performed through the application, which are sent to the cloud. In this way, the condition of the device is monitored and the organisation of the regular maintenance of the device is simplified. The regular maintenance of the device must take into account the flow that has occurred since the last maintenance, in addition to the time interval.
## 6 Pitfall Trap Deployment
### Deployment Experience and Results  
Our deployment lasted three days, and the network was operational only during the daytime hours (7 a.m.-7 p.m.). We closed the traps during the night, since the biologists were mainly interested in species of animals that are active during the day. Since we had an LED flash equipped on each camera node, our system would have worked for a study at night also if that had been the purpose. We have collected 589 images, out of which 588 were complete; there was one incomplete image from a node that almost ran out of battery. Figure 6 plots the time when each has transferred an image on the last day of the experiment. Unfortunately, we did not capture any animal, probably due to the cold weather. However, there was one time period where image transfer due to object-detection was continuously triggered. This is when we caught a spider (Figure 7). Many of the transferred images were triggered as a result of changes in the intensity of sunlight (Figure 8), and some others were intentionally triggered by us to test the system. Image transfer due to sunlight/shade change is a false positive for our object detection algorithm that we had not anticipated in the lab environment. Furthermore, there were a couple nodes that were continuously transferring images during the night (Figure 9). It turned out that the IR flash was malfunctioning on those nodes, and complete darkness was also generating false positives in our object detection algorithm. For there reasons, we have modified our image compression strategy for our next bird nest monitoring deployment.  
The main lessons learned from our pitfall trap deployment are as follows.  
* It is feasible and practical to use an image-based wireless sensor network for effective pitfall trap monitoring. Our system is flexible, supports multihop and satisfies many application requirements described in Section.
* However, the background subtraction-based object detection algorithm may result in frequent false positives, either due to sunlight intensity changes during the day or complete darkness when not equipped with an IR flash.
* An IEEE 802.15.4-compliant 2.4-GHz radio does not propagate well in mountain environments with dense foliage, and lower frequency radio may be a better choice.  
Figure 6: Image-transfer _vs_. time plot: an image is generated every 30 min, in addition to whenever an object is detected.  
Figure 7: A spider triggered image transfers.
# Therapeutic Potential and Challenges of Pioglitazone in Cancer Treatment
## 3 Therapeutic Use of Pioglitazone
### Clinical Evidence  
To evaluate the antineoplastic potential of pioglitazone, it is important to review the latest clinical trials (Supplementary Table S1--from clinicaltrials.gov platform) [129; 130; 131; 132; 133; 134; 135]. [PERSON] et al. investigated the combined use of pioglitazone with chemotherapy for metastatic breast cancer treatment. Despite the study's statistical power being significantly restricted due to the small sample size of just 60 female participants, it was concluded that combining pioglitazone with chemotherapy was more effective than using taxol alone. This combination resulted in better radiologic responses, disease stability, and halted progression, ultimately providing a greater overall clinical benefit [156]. In contrast, the study by [PERSON] et al. showed no clear superiority of one combination over the other, when participants received carboplatin (AUC = 2) and paclitaxel (80 mg/m\({}^{2}\)) weekly in combination with either hydroxyurea (1000 mg and 1500 mg) or pioglitazone (30, 45 and 60 mg) daily. However, treatment with hydroxyurea demonstrated slightly more favorable indications based on the measurement of uncoupling protein-2 gene expression and rate of cells with double-strand breaks [157].  
Besides therapeutic use, pioglitazone can also play a role in the diagnosis of cancer. [PERSON] et al. have studied the effect of pioglitazone on 18F-2-fluoro-2-deoxy-D-glucose (18F-FDG) uptake by neoplastic lesions. In the study group of 43 patients (13F/30M, Median Age 64.0), the participants were scanned 1h after intravenous administration of 18F-FDG (5.5 MBq/kg). The same procedure was repeated the next day, 2 h after the oral administration of pioglitazone (15 or 30 mg). Results indicated a dose-independent betterment in all assessed aspects of positron emission tomography (PET) imaging in the group that responded to the administration of pioglitazone, with discrimination between the effect on malignant and inflammatory lesions. However, it must be highlighted that, like the rest of the trials discussed, this one also suffers from a small sample size, dampening the statistical significance of the results [158].  
Despite the substantial number of clinical trials, evidence regarding the use of pioglitazone for cancer prevention and treatment remains insufficient. This can be attributed to the low number of participants and lack of reports, which limit the statistical value of the reported results. Thus, a large number of clinical trials is required to properly assess the antineoplastic potential of pioglitazone. More specifically, there is a need to further investigate combination therapies and adverse events. For this purpose, multiple parameters should be taken into consideration, such as population, duration of treatment, and follow-up appointments. Regarding population, its size and diversity need to be increased, especially in phase II and III trials, for future studies to possess enough power to be considered for applications in standardized cancer care.