esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Design of the Unit Structure of the Main Reflector in the Deployable Reflector Antenna  
### Performance Evaluation According to Stacking Sequence of Facesheet  
The composite material is an orthotropic material which has different mechanical properties depending on the stacking angle. In this paper, YSH70A is applied to the main reflector. This material is s space-grade high-strength CFRP structure, and it is manufactured using a plain weaving process. The main reflector is a honeycomb sandwich composite structure, a structure in which the composite material of YSH70A surrounds the upper and lower surfaces of the honeycomb core. To evaluate the influence of the laminated pattern, a parameter study of modal analysis was performed on several types of the unit structure consisting of an Aramid core with the same physical/dimensional properties and YSH70A with different laminated patterns. As shown in Table 2, five cases were selected with the lamination angles of \(\left[0,30,45,90\right]_{\text{deg}}\). To compare the mechanical performance for each case, the effective properties of the laminated structure are possible to calculate according to Classical Laminate Theory (CLT) [17; 18].  
In order to numerically evaluate dynamic stiffness, the modal analysis of the unit structure with respect to the laminated pattern of the CFRP facesheet was performed using Optistrut Solver. The finite element model of the unit structure consists of a 2D composite shell element of the YSH70A facesheet and the Aramid core of a 3D anisotropic solid element. To compare the dynamic stiffness values with respect to the laminated pattern, modal analysis was performed under the free-free boundary condition. During  
Figure 1: Deployable reflector antenna under (**a**) stowed and (**b**) deployed conditions.  
the deployment process, the root part of the main reflector is connected to the hinged mechanism adjacent to the center of gravity. The dynamic stiffness of the fully deployed main reflector shown in Figure 1b depends on the root part of the main reflector connected to the hinge. Thus, the dynamic stiffness of the main reflector is evaluated as the first mode frequency.  
Table 3 shows the modal analysis results of each main reflector under the free boundary condition with respect to the stacking pattern of the CFRP facesheet. While the first and second mode frequencies for Case 1.2 show a high value, the third mode shows a relatively low value because this laminated pattern has relatively low shear modulus. The laminated pattern of [0/90] has the highest first mode frequency at 30.75 Hz, but the mode frequency for the twisting mode is shown to be significantly low. The second bending mode of Case 1.1 with the laminated pattern of [0/45] is shown the highest at 78.09 Hz, and the torsional mode of Case 1.5 with the laminated pattern of [\(-\)30/30] is shown the highest at 116.95 Hz. The laminated pattern with Case 1.1 of [0/45] is reasonable for the main reflector in the DR-A.
# Advancing Digital Twin-Based Collision Avoidance:
## 3 Evaluation of Results
### Experimental Methodology  
The designed experiment evaluates the necessity and effectiveness of a 5G-enabled collision warning system in ensuring safety. Initially, the focus is on analyzing the efficiency of various collision avoidance strategies, coupled with a theoretical assessment of 5G network capabilities in handling multiple trucks, based on the proposed communication flow models. Subsequently, a forklift equipped with a 5G-compatible RTLS module is tested within a local campus network. Latency is measured using Internet Control Message Protocol (ICMP) packets to ascertain the system's efficacy in improving safety in relevant environments.
# Generation of Monoclonal Antibodies for Sensitive Detection of Pro-Inflammatory Protein S100A9
## 2 Materials and Methods
### Confirmation of the Activity of Recombinant Human S100A9  
Indirect ELISA for confirming the activity of purified rhS100A9 was performed as follows: several concentrations of purified rhS100A9 or commercially available purified hS100A9 protein (Sino Biological, Beijing, China, 11145-H08B) in PBS were immobilized on a 96-well plate for 16 h at 4 \({}^{\circ}\)C, and the wells were filled with 200 \(\upmu\)L of PBST (PBS buffer with 0.05% Tween20) containing 2% BSA for 1 h at 37 \({}^{\circ}\)C and washed three times with 200 \(\upmu\)L of TBST. Subsequently, 150 ng of anti-hS100A9 antibody (Sino Biological, 11145-MM01) in TBST was added and incubated for 1 h at 25 \({}^{\circ}\)C. After washing three times with 200 \(\upmu\)L of TBST, the bound protein was probed with 5000-fold diluted HRP-conjugated goat anti-mouse antibody in TBST for 1 h at 25 \({}^{\circ}\)C. The wells were washed three times with 200 \(\upmu\)L of TBST and developed with 100 \(\upmu\)L of tetramethylbenzoicz (TMB) solution. After incubation for 15 min, the reaction was stopped with 25 \(\upmu\)L of 1 N sulphuric acid, and the absorbance was read at 450 nm using a microplate reader Model 680 (Bio-Rad). As a negative control, 200 ng BSA was used instead of antigen, and the titer was used as a baseline. A well coated with 200 ng BSA only was used as a negative control to use its titer as a baseline signal.
# Gait Recognition Algorithm of Coal Mine Personnel Based on LoRa
## 2 Materials and Methods
### Preliminary Experiment  
For gait recognition based on LoRa signals, the first challenge we faced was whether the target's walking would induce obvious variation patterns and whether different targets walking under the same experimental conditions would cause different signal variation patterns. To verify the feasibility of gait recognition based on LoRa signals, we designed a preliminary experiment.  
The experiment scenario was a laboratory environment covered with desks, chairs, and computers on both sides, where the multipath effect was significant. The transmitter and receiver were placed side by side. The target was located at a distance of 5 m from the transceiver and walked perpendicular to the line of sight (LOS) for a total of four round trips. The experiment results are shown in Figure 4. The top and bottom figures represent the normalized amplitudes of the received signals for users A and B, respectively. We observed that the four round trips of the user's walking corresponded to the four variation periods of the received signal, indicating that the LoRa-received signals contained the walking information of the target. In addition, the received signals for different targets show similar periodicity when they carried out the same walking process. However, the signal variation patterns in the same period were obviously different, and the corresponding period lengths were also different. A longer period represented a slower target walking speed, whereas a shorter period indicated a faster target walking speed.  
In the same place, the gait of the same target walking over time was reflected in the consistency of the received signal, which determined whether the same target could be recognized at different times in the same place. In the process of data collection, the target was required to repeat the walking process. It was observed that the received signal variations caused by the target walking remained basically consistent over an hour. The above preliminary experiment and analysis verified the difference between different target gaits and the consistency of the same target gaits over time, which provides an experiment basis for LoRa-based gait recognition research and proves the feasibility of gait recognition by processing LoRa received signals.  
Figure 4: Amplitude of received signal.
# Efficient SMC Protocol Based on Multi-Bit Fully Homomorphic Encryption
## 5 Secure Multi-Party Computation Protocol Based on NFHE Scheme
### Security
#### 5.3.2 In Semi-Malicious Model
The real decryption result of \(P_{h}\) is: if \(\mathbf{v}=\frac{1}{N}\sum\limits_{i\in[N]}v_{i}=\mathbf{\mu}\big{\lceil}\frac{q}{2} \big{\rceil}+\mathbf{e}^{\prime}\Rightarrow N\mathbf{e}^{\prime}=\sum\limits_{i\in[N] }v_{i}-N\mathbf{\mu}\big{\lceil}\frac{q}{2}\big{\rceil}\), then:  
\[\mathbf{\rho}_{h}=\mathbf{\eta}_{h}\mathbf{G}^{-1}\Big{(}\mathbf{w}^{T}\Big{)}+ \mathbf{\varepsilon}_{h}=\mathbf{v}_{h}+\mathbf{\varepsilon}_{h}\] \[\qquad\qquad=\sum\limits_{i\in[N]}\mathbf{v}_{i}-\sum\limits_{i\
eq h }\mathbf{v}_{i}+\mathbf{\varepsilon}_{h}\] \[\qquad\qquad=N\mathbf{e}^{\prime}+N\mathbf{\mu}\Big{\lceil}\frac{q}{2} \Big{\rceil}-\sum\limits_{i\
eq h}\mathbf{v}_{i}+\mathbf{\varepsilon}_{h}\]  
where \(\mathbf{\varepsilon}_{h}\leftarrow\chi\). It is easy to determine that \(\mathbf{\varepsilon}_{h}\) and \(\mathbf{e}_{h}^{\prime}\) are statistically indistinguishable, which proves that \(\mathbf{\rho}_{h}\) and \(\mathbf{\rho}_{h}^{\prime}\) cannot be distinguished, so the conclusion is proven. \(\square\)  
**Lemma 2**. : \(HYB_{\pi,A,Z}\overset{\text{comp}}{\approx}IDEAL_{F,S,Z}\)__  
Proof. : The ciphertext generated by \(P_{h}\) is the only difference between the two games. From the semantic security of the encryption method of the NFHE scheme, it can be seen that the ciphertext is computationally indistinguishable, so the two games are also computationally indistinguishable. From Lemma 1 and Lemma 2, \(IDEAL_{F,S,Z}\overset{\text{comp}}{\approx}REAL_{\pi,A,Z}\) can be obtained. End. \(\square\)
## 3 The Proposed Improved ACS
### Fitness Value for Evaluation for Optimization  
As mentioned above, the ants in this study will visit various nodes according to different situations during the entire movement process. In addition to the path distance, the node's selection will also refer to the satisfaction with attractions, restaurants, and hotels. The conditions included in attraction satisfaction (A.S.), restaurant satisfaction (R.S.), and hotel satisfaction (H.S.) are shown in Table 1.  
Attraction satisfaction, restaurant satisfaction, and hotel satisfaction can be expressed by the following formulas:  
\[A.S.=A.T.*u_{1}+A.P.*u_{2}-A.C.*u_{3} \tag{6}\]  
\[R.S.=R.T*u_{4}+R.P.*u_{5}-R.C.*u_{6}-W.S.*u_{7} \tag{7}\]  
\[H.S.=H.T.*u_{8}+H.P.*u_{9}-T.C.*u_{10} \tag{8}\]Among them, \(u_{1}\) represents the weight of interest; \(u_{2}\) represents the user's interest in popular attractions; \(u_{3}\) represents the user's degree of interest in spending on attractions; \(u_{4}\) and \(u_{8}\) represent the user's restaurant and hotel interest weights, respectively. \(u_{5}\) and \(u_{9}\) indicate whether users like the weight of popular restaurants or restaurants; \(u_{6}\) and \(u_{10}\) indicate whether they care about excessively expensive restaurant or restaurant weights; \(u_{7}\) represents how much users care about waiting time.  
After each ant completes its entire journey, it will have its own path length, denoted as P.L. In traditional TSP, this path length (or P.L.) is the ant's fitness value. However, in this study, the ant will deduct the calculated A.S., H.S., and R.S. from P.L., as shown in the following Formula (9). Even if the total journey distance is the same, itineraries with high-satisfaction restaurants and hotels will have a lower fitness value,  
\[\text{Fitness Value}= P.L.-(R.S.+H.S.+A.S.) \tag{9}\]  
Accordingly, we have the following optimization problem in the mathematical form included with constraints:  
\[\text{Minimize }P.L.-(R.S.+H.S.+A.S.)\]  
Subject to  
\[A.S.=A.T.*u_{1}+A.P.*u_{2}-A.C.*u_{3}\]  
\[R.S.=R.T*u_{4}+R.P.*u_{5}-R.C.*u_{6}-W.S.*u_{7}\]  
\[H.S.=H.T.*u_{8}+H.P.*u_{9}-T.C.*u_{10}\]  
In the end, the proposed ACS will select an ant with the best fitness and list the time recorded in the Watch of the ant and all the travel sequences and become the recommended itinerary for this system.
## 3 Results and Discussions
### Hop Cone Analysis
#### 3.9.3. HPLC-UV Analysis and Essential Oil  
In Table 7 the best performance in alpha and beta acid production (6.98% of alpha acids and 6.37% beta acids) is shown for Treat 2, the treatment with the highest amount of N supplied (Table 1); the lowest quantity of alpha acids was registered for Treat 4 and Untreat (5.08% and 4.32%) (Table 7), that are the treatments with the lowest amounts of N supplied. This data was not in accordance with the study of [PERSON] et al. [7], who observed a reduction of alpha acids with the increase of nitrogen. Their data can be explained by the fact that in their study [7] N was supplied directly in soil through irrigation four times during the growing season, thus giving an excess quantity of nitrogen that caused a reduction of alpha acid synthesis, probably in favor of the vegetative (and cone) growth as explained by [PERSON] [1]. In our study, in Treat 2 that reached the highest alpha acid production (6.98%), N was given only in the first part of the experiment, when plants were in the vegetative stage and not yet in the reproductive stage. The second step of fertilization (t2) of Treat 2,  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Treatment** & **Fresh Weight** \\  & **(Kg/Plant)** \\ \hline Treat 1 & 2.48 \(\pm\) 0.482 b \\ Treat 2 & 3.55 \(\pm\) 0.323 a \\ Treat 3 & 2.20 \(\pm\) 0.404 b \\ Treat 4 & 3.03 \(\pm\) 0.112 ab \\ Untreat & 2.42 \(\pm\) 0.275 b \\ \hline \hline \end{tabular}
\end{table}
Table 6: Fresh cone yield in the different fertilization treatments. Values followed by the same letter indicate no significant differences (\(p\) < 0.05).  
instead, contained the highest amount of Mn, Fe, Cu and B, which determined the highest productivity, as was shown in a study by [PERSON] and [PERSON] [52]. Untreat showed the lowest content of bitter acids, thus demonstrating the importance of fertilization for an optimal production.  
The essential oil yield was highest in Treat 2, with 1.78%, and the lowest in Untreat (1.07%) (Table 7). Treat 2 was the treatment in which microelements were added only in t2 (Table 1). This result showed that the introduction of micro-elements in the fertilization plan is important, especially in the second part of hop growth, since they are likely involved in the biosynthesis of the essential oils. In a study on the effect of foliar fertilization on thyme [53], it was observed a positive influence of P on essential oil yield; this result is not supported by our study, because P was given to plants only in t2, in Treat 1 and Treat 3; instead, Treat 2 was the only treatment that did not involve P in the fertilization plan. This means, that hop essential oil production is likely to be more influenced by several microelements.
# Microwave Photonic Devices Based on Liquid Crystal on Silicon Technology
## 2 LCOS Based Optical Processor Operating Principle and Functions
Four microwave signal generators are required for realizing four independent phase shifting operations, which is more expensive compared to a commercial 4-port LCOS based optical processor (WaveShaper 4000A, Finisar, Sunnyvale, CA, USA) that costs around $50K AUD. Reconfigurability is the most powerful feature of an LCOS based microwave photonic device as tuning the notch frequency of a microwave photonic filter or shifting an RF signal phase can be accomplished within a fraction of a second by uploading a definition table into an LCOS based optical processor. Furthermore, the compactness and off-the-shelf availability make an LCOS based optical processor stand out from other techniques for implementing microwave photonic devices.
# Radiomics in Gynaecological Imaging: A State-of-the-Art Review
## 5 Mesenchymal Tumours  
Uterine mesenchymal tumours arise from uterine smooth muscle, endometrial stroma, or a combination of both [132]. Benign leiomyomas (LM) are the most common mesenchymal uterine tumours, affecting up to 80% of women of reproductive age [133]. Conversely, uterine sarcomas (US) are a rare form of mesenchymal tumours, accounting for approximately 1% of gynaecological neoplasms and 3-7% of all uterine malignancies, and have a poor prognosis [134]. Currently, there are no reliable imaging criteria for distinguishing US, especially leiomyosarcomas, from LM with atypical features, including degeneration or unusual pattern of growth [135]. The final diagnosis is usually made only after the surgery, based on postoperative histopathological assessment.  
Considering the overlap imaging features in atypical LM and US, several authors investigated the possible role of MRI texture analysis in aiding radiologists in the differential diagnosis between these two entities [136]. However, the majority of these publications are retrospective and monocentric, beyond being limited by small sample cohorts of patients, particularly those with malignant lesions.  
[PERSON] et al. developed a radiomic model based on MR perfusion, which showed good diagnostic values (accuracy, sensitivity, and specificity of 91%, 100%, and 90%, respectively) [137]. The same authors aimed to develop a decision tree and a complex algorithm to differentiate US and LM, with accuracies of 96% and 100%, respectively. However, the algorithm was reported to be time consuming, with a special limit for everyday clinical practice [138].  
Finally, [PERSON] et al. reported that a radiomic model based on ADC map can predict pathological results of patients with sarcomas and atypical leiomyomas with an AUC of 0.83 [139].  
[PERSON] and colleagues compared the performance of ML using multiparametric MRI and positron-emission tomography (PET), concluding that the MRI-based model was superior to the PET one and comparable with that of experienced radiologists [140]. Another investigation compared the diagnostic performance of three different volumes of interests (VOIs)--lesion, lesion and surrounding tissue, and whole uterus--in ADC map-based radiomic analysis for distinguishing US and LM. The results showed that the model based on features extracted from VOIs covering the whole uterus had the best diagnostic performance (AUC: 0.876, sensitivity: 76.3%, and specificity: 84.5%) [141]. [PERSON] and [PERSON] focused their research on distinguishing low-grade US and LM, testing different ML models and various cutting-edge deep learning techniques. For the classic techniques considered, the highest classification accuracy was 0.85, while the most accurate learning model achieved an accuracy of approximately 0.87 [142].
# A Failure Risk-Aware Multi-Hop Routing Protocol in LPWANs Using Deep Q-Network
## 5 Detailed Description of the Proposed Protocol
### DQN-Based Routing Decision Mechanism
#### 5.2.2 DQN Architecture  
The DQN architecture implemented in FFDR is detailed as follows:  
1. Input layer: A fully connected (FC) layer is used as the input layer. The input feature dimension is set to \(3N+1\), corresponding to the pre-masked state \(\mathbf{s}_{t}\).
2. Hidden layer: The hidden layer comprises two FC layers with 64 and 32 neurons, respectively. For each FC layer, the Leaky Rectified Linear Unit (Leaky ReLU) activation function with a negative slope coefficient of 0.01 is employed. Moreover, the backpropagation gradients from non-candidate ENs are set to zero.
3. Output layer: We define an FC layer with \(N\) neurons as the output layer to generate raw Q-values \(Q_{true}\), where a linear activation function is utilized. Feature-based masking is then applied to compute final values by Equation (24). \[Q=\begin{cases}Q_{true},&EN_{i}\in\mathbb{N}_{t}^{cf}\\ -10^{5},&otherwise\end{cases}\] (24)
## 2 Literature Review
### Other Works That Employ STPA  
In [15], STPA-Sec [13] is extended by introducing a standardized set of safety and security terminologies and adding some security elements into its generic control loop diagram; such extended STPA-Sec is demonstrated via analysing a vehicular battery management system [15]; besides, limitations of the original STPA-Sec with respect to relevant automotive standards (e.g., ISO26262 and SAEJ3061) are discussed. In [38], the extended STPA-Sec proposed in [15] is adopted to identify the hazardous scenarios which are then inputted to a component-centric approach called FMVEA (Failure Mode, Vulnerabilities, and Effect Analysis) [39] to evaluate the likelihood and severity of the Safety and Security risks of a realistic train braking system. In [32], STPA and NIST SP 800-30 [40] are employed to identify the Safety constraints and Security constraints, respectively; the security team performing NIST SP 800-30 also works with the safety team in certain steps of STPA to identify the potential causal factors and scenarios for identifying the Safety constraints; then, Safety team and Security team jointly process the two individual sets of constraints to identify the potential conflicts or pairwise reinforcements between them, and subsequently define the appropriate security measures and safety recommendations; such methodology is illustrated via analysing a revolving door system. In [41], STPA is employed to identify how and when hazardous scenarios may emerge due to generic causes and intentional causes like jamming and tampering; the identified hazardous scenarios are then translated into critical requirements on a formal model processed via a specific tool to formally verify whether the constraints sufficiently mitigate the identified hazardous behaviours; such methodology is demonstrated via analysing a hydroponics facility. In [10], safety requirements and security requirements are derived from STPA and attack trees, respectively; the derived requirements along with other relevant information are then integrated into a Six-Step Model [42] for achieving integration and alignment safety and security of automated vehicle.