esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Design, Manufacturing and O&M Integration Mode of Complex Product Based on Digital Twin  
### Design, Manufacturing and O&M Integration Process of Complex Product  
In terms of the general traditional manufacturing mode of a complex product, the feasibility of the design scheme is verified by the design department after the designer completes the product design, then the relevant design information is conveyed to the manufacturing department. After that, the design stage of the product is finished. The manufacturing department will plan and dispatch the manufacturing resources per the data provided by the design department, then finish the processing and assembly of the product. After that, the manufacturing stage of the product is finished. After the finished products are delivered to users, users shall contact after-sales personnel for factory return and maintenance in case of fault repair in the usage process. After that, the O&M stage of the product is finished. Those three stages are independent of each other. Such a manufacturing mode forms a kind of information islanding, which causes the associated data at each stage to be omitted in the whole life cycle process of the product.  
To form the tight closed loop of a complex product in the design, manufacturing and O&M and to ensure the intellectualization of the whole manufacturing model, the design-manufacturing-O&M integration method of a complex product based on digital twin shall comply with the following methods, as shown in Figure 2. The demands of users are analyzed first and then conveyed to the design department for virtual prototype design, and the virtual design model and product's actual design scheme are optimized collaboratively. The design department conveys the information to the manufacturing department for production. In the meantime, the workshop manufacturing model of virtual space synchronizes with it, and feeds back the optimal production manufacturing plan tothe actual manufacturing workshop via constant iterative optimization. The manufacturing department feeds back the dimension deviation, assembly relation, actual manufacturing parameters, etc., in the manufacturing process to the design department. The product is delivered to users after manufacturing. During product operation, the system would carry out fault pre-warning and analysis as per data. If the product needs maintenance, the actual maintenance center of the product and the virtual maintenance center form the optimal maintenance scheme via constant iterative optimization. The data from the O&M center will also be fed back to the manufacturing and design departments. Finally, the design department will implement the product's optimization design as per the manufacturing and O&M feedback data. During the entire process, the data goes through the whole process.
# Numerical Analysis on the Performance of a Heat Pump Powered by Surface Water Freezing
## 3 Results and Analysis
### Influence of Water Temperature at Evaporator Inlet  
Figure 10a shows the curves of exhaust temperature and isentropic power changing with time under different inlet water temperatures of the solidification heat evaporator. It can be seen from the figure that, when other conditions are fixed, the higher the inlet water temperature of the evaporator, the higher the exhaust temperature, and it increases first and then remains unchanged with time. The reason for the above results is that the higher the inlet water temperature, the larger the evaporator heat transfer temperature difference, the more heat transfer, the higher the icing rate, the larger the required refrigerant flow rate, and the higher the exhaust temperature. The exhaust temperature changes with time following the same trend as in the previous analysis.  
It can also be seen from Figure 10a that, when other conditions are fixed, the higher the evaporator inlet water temperature, the higher the isentropic power, which is also related to the required mass flow rate. The analytical reason is the same as the previous one. After the system reaches stability, the isentropic power of condition 5 is 40.5 kW, the isentropic power of condition 6 is 24.3 kW, and the isentropic power of condition 7 is 13.7 kW.  
Figure 10b shows the curves of the system heating capacity and the heating performance coefficient changing with time under different evaporator inlet water temperatures.  
Figure 10: Effect of inlet water temperature with time (**a**) on \(T_{r}\) and \(w\), (**b**) on \(Q\) and \(COP\).  
It can be seen from the figure that, when other conditions are fixed, the higher the inlet water temperature is and the more the heating capacity is. The analytical reason is the same as that mentioned above. When the system is running stably, the heating capacity of condition 5 is the highest, which is 151 kW. The heating capacity of condition 6 is the second, which is 93 kW. The heating capacity of condition 7 is the lowest, which is 53 kW.  
It can also be seen from Figure 10b that, under the same conditions, the higher the inlet water temperature, the lower the system _COP_, and the system _COP_ decreases first and then remains unchanged with time. The reasons for the above results are the same as those analyzed above. The optimal deicing time of the system under three operating conditions is about 15 minutes after operation. The average _COP_ and _COP_ after stabilization are the largest in operating condition 7, which are 3.88 and 3.86, respectively. The second largest _COP_ values are in operating condition 6, which are 3.86 and 3.81, respectively. The third largest _COP_ values are in operating condition 5, which are 3.81 and 3.37, respectively.
## 2 Related Work
### The Effective Use of the Bat Algorithm in IoT Environments  
[PERSON] et al. [12] proposed a novel, self-adaptive, energy-efficient bat algorithm to fortify software-defined IoT networks (SDN) by bolstering the intrusion detection system. The method unfolds in two stages: initially, real-time network traffic packets from SDN undergo analysis, and feature selection is executed. Subsequently, packets are classified as normal or malicious, triggering appropriate preventive measures upon the detection of an attack. The evaluation results underscored the outperformance of the proposed approach over swarm-based bat algorithms in terms of accuracy, precision, and time overhead, indicating its superior efficacy for the specified problem.  
[PERSON] et al. [13] proposed a robust solution to safeguard commercial IoT devices against two types of botnet attacks. Their local-global best bat algorithm for neural networks (LGBA-NN) exhibited high efficiency in detecting and securing nine different IoT devices. An evaluation using N-BaloT showcased superior results compared to weight optimisation using particle swarm optimisation (PSO-NN).  
In the area of smart homes, [PERSON] et al. [14] tackled the challenge of balancing energy efficiency and smart home services. Their bat-algorithm-based solution aimed to optimise temperature, illumination, and indoor air quality settings to enhance home convenience while minimising energy costs. The experimental findings demonstrated the superiority of the proposed approach over alternative algorithms such as firefly, genetic, ant colony optimisation, and artificial bee colony algorithms.  
Addressing the challenges of energy efficiency and rerouting in mobile ad hoc Networks (MANET), [PERSON] et al. [15] devised an adaptive information weight bat algorithm (AIW-BA) model. MATLAB simulations revealed that AIW-BA surpassed existing routing protocols, including genetic algorithm ad hoc on-demand multipath distance vector (GA-AODDV), bat-optimised link state routing (BOLSR), ant colony harmony search algorithm (ACO-HSA), particle swarm optimisation routing protocol (PSO-RP), and enhanced ant colony ad hoc on-demand vector (EA-AODV) in terms of enhancing the network's lifetime and reducing link failures.  
In cloud computing, [PERSON] et al. [16] proposed a modified bat algorithm (MBA) to optimise task allocation to cloud resources, ensuring optimal load balancing. The evaluation results demonstrated the superior performance of MBA compared to the original bat algorithm, highlighting its efficacy in achieving efficient resource allocation and load balancing in cloud environments.
## 4 Proposed ms-SDRGN Solution For SAG-MCS
### Aggregate Adjacent Information with Graph Attention Mechanism  
For the purpose of multi-agents exchanging information through ad-hoc connections in SAG-MCS, we model the UAV swarm as a graph network, where each node is represented as a UAV, and the edges are the communication links of neighboring UAV pairs. For each node \(i\), we denote \(\mathbf{e}_{i}\) extracted from observation space as its node embedding. Let all UAVs networked with UAV node \(i\) as a set \(G_{i}\). This is implemented by an adjacency mask \(\mathbf{A}\), which is a \(U\times U\) symmetric matrix and satisfies \(\mathbf{A}(i,j)=1\) if UAV node \(i\) is interconnected with UAV node \(j\). For all UAV node \(j\in G_{i}\), we utilize GAT to determine the weight of UAV node \(i\) towards its different neighbors \(j\) as \(a_{ij}\). Building on the concept of self-attention [48], an attention coefficient between node \(i\) and its neighboring node \(j\) is defined as \(\mathbf{e}_{ij}=a(\mathbf{We}_{i},\mathbf{We}_{j})\), where \(a()\) is a shared attentional mechanism. Then, we calculate the attention weight \(\alpha_{ij}\) by normalizing \(\mathbf{e}_{ij}\) across all possible node \(j\) using softmax function:  
\[\alpha_{ij}=\text{softmax}_{j}\big{(}\mathbf{e}_{ij}\big{)}=\frac{\exp\Big{(} \big{(}W_{K}\mathbf{e}_{j}\big{)}^{T}\cdot W_{Q}\mathbf{e}_{i}\Big{)}}{\sum_{k \in G_{i}}\exp\Big{(}\big{(}W_{K}\mathbf{e}_{k}\big{)}^{T}\cdot W_{Q}\mathbf{e }_{i}\Big{)}}, \tag{10}\]  
Then GAT aggregates information from all adjacent nodes \(j\) by weighted summation, which is given by:  
\[\mathbf{g}_{i}=\sum_{j\in G_{i}}\alpha_{ij}^{k}\cdot W_{V}\mathbf{e}_{j}. \tag{11}\]  
where we denote \(\mathbf{g}_{i}\) as the aggregated output embedding of UAV \(j\) after one GAT layer. In addition, \(W_{Q},W_{K},W_{V}\in\mathbf{W}\) are learnable weight matrixes related with query, key, and value vector.  
As shown in Figure 3, we utilize two GAT layers to aggregate information from neighboring UAV agents within a two-hop communication range, which could further expand the perception range and enhance cooperation of the UAV swarm. For better convergence, we then use skip connections [49] by concatenating the input observation embedding \(\mathbf{e}_{i}\), the outputs of the first GAT layer \(\mathbf{g}_{i,1}\) and the second GAT layer \(\mathbf{g}_{i,2}\), as \(\mathbf{g}_{i}=\text{concatenate}(\mathbf{e}_{i}\mid\mathbf{g}_{i,1}\mid \mathbf{g}_{i,2})\).  
Additionally, to make full use of temporal information during the interaction with RL environments and improve long-term performance, we integrate a gated recurrent unit (GRU) to memorize temporal features as:  
\[h_{t}=\text{GRU}(\mathbf{g}_{i}\mid h_{t-1}). \tag{12}\]  
where we take \(\mathbf{g}_{i}\) as input and \(h_{t}\) is the hidden state of timestep \(t\) stored in the memory unit. After adjacent information aggregation and GRU, we apply an affine transformation layer to \(h_{t}\) for calculating Q-value \(Q(\mathcal{O}_{t},a_{t})\).
# Stable and Low-Spurs Optoelectronic Oscillators: A Review
## 4 Conclusions  
Low phase noise RF oscillators are required in multiple applications, ranging from radar and Global Position System (GPS) to high performance analog to digital converters to trans-oceanic data transmission. Since 1996, the OEO has been investigated for applying to many different fields requiring low phase noise RF signals. However, inferior frequency stability and rock-ribbed spurs, which result from the inherent structure, hinder the practicability of the OEO. In this article, we analyzed and analyzed the performance of the proposed OEO system in the context of the OEO system.  
Figure 16: Schematic diagram of the stabilized millimeter-wave OEO scheme in [53]. MZM: Mach-Zehnder modulator. DSF: dispersion-shifted fiber. PD: photodetector. EA: electronic amplifier. PC: power combiner. BPF: band-pass filter. FD: frequency divider. VCPS: voltage controlled phase shifter. PID: proportional-integral-derivative regulator module. Reprinted with permission from Ref. [53], OSA, 2018.  
Figure 17: (**a**) The overlapping Allan deviation of the OEO system. (**b**) Comparison of phase noise for the OEO system. Reprinted with permission from Ref. [53], OSA, 2018.  
Figure 15: Schematic diagram of the stabilized OEO scheme based on the FCP with equivalent wide frequency-compensation range [52]. MZM: Mach-Zehnder modulator. DSF: dispersion-shifted fiber. PD: photodetector. EA: electronic amplifier. PC: power combiner. BPF: band-pass filter. FD: frequency divider. VCPS: voltage controlled phase shifter. Att: attenuator. FM: frequency multiplier. PS: power splitter. Ref: microwave reference. FS: frequency synthesizer. PID: proportional-integral-derivative regulator module. Reprinted with permission from Ref. [52], IEEE, 2018.  
summarized the research on the improvement of frequency stability and spurious suppression of OEOs to contribute and provide new insights for future work. The wide-band frequency tunability and stability of optoelectronic oscillators are a critical issue that need to be solved urgently.  
Investigation and Writing, A.L.; Resources and Review, J.D.; Supervision and Project Administration, K.X.  
This research was partially funded by National Natural Science Foundation of China (NSFC) Program (61501051, 61625104 and 61821001); Fundamental Research Funds for the Central Universities (2018 RC22), Fund of State Key Laboratory of Information Photonics and Optical Communications (BUPT No. IPOC2017 ZT01); State Key Laboratory of Advanced Optical Communication Systems and Networks, China Postdoctoral Science Foundation (No. 2018T110074).  
The authors declare no conflict of interest.
# Emerging Trends and Management for Sjogren Syndrome-Related Dry Eye Corneal Alterations
## 3. **Glandular Ocular Involvement: Dry Eye Disease (DED) or Keratoconjunctivitis Sicca (KCS)**
### Histology, Biochemistry, Anatomy, and Physiology
#### 3.1.5. Cornea  
In vivo confocal scanning laser microscopy studies have found that, as compared with healthy or non-SS DED subjects, SS patients show many corneal alterations, including epithelial cells' decreased density and morphological abnormalities [32]; superficial epithelium squamous metaplasia or keratinization (rare) [32]; reduced density of the stromal keratocytes and stromal thinning [32]; upregulation of the stromal dendritic cells, whose density and size seem to be linked to systemic disease severity (assessed using salivary gland biopsy and serum autoantibodies) and to the ocular surface damage [33]; and decreased density and morphological abnormalities of the sub-basal nerve plexus [32; 34]. In particular, corneal nerve density and length have been demonstrated to be inversely related to systemic SS disease activity and the degree of eye dryness severity [34]. Moreover, studies investigating corneal biomechanics have demonstrated significantly reduced corneal stiffness in SS-related DED patients as compared with healthy subjects [35].
## 5 Improving Reactor Performance
### Thermophilic Regiment to Increase Reactor Treatment Capacity  
The increase in digestion temperature is also an evident way of increasing the degradation rate and therefore reactor productivity. Anaerobic digestion is strongly influenced by temperature [199]. Operating at thermophilic conditions and attaining stable performance may not be exempt from complications. Increasing the temperature of the digestion system from a mesophilic to thermophilic regimen allows for an existent installation to treat a significantly greater amount of organics. The biogas yield obtained under thermophilic conditions has been reported by some authors to be similar to that obtained under mesophilic conditions [200,201], but the main advantage resides in the lower time needed to complete the full degradation.  
Thermophilic regimen may also bring, as a consequence, a lower quality of digested material. Thermophilic reactor liquor has been reported to contain higher levels of VFA and ammonia [202, 203, 204], which adversely affects its organic quality. [PERSON] et al. [205] studied the organic characteristics of cattle manure digestates, indicating a better quality than that obtained under mesophilic conditions. On the contrary, [PERSON] et al. [206], studying the digestion of sewage sludge and municipal solid wastes, reported a better performance for the thermophilic system. The apparent inconsistency in results may be explained by the different characteristics of substrates and nitrogen content. The higher nitrogen content of manure leads to higher ammonia levels during digestion, having, therefore, a greater adverse effect on the process outcomes when the temperature is increased.  
[PERSON] and [PERSON] [207] performed a literature review regarding the effect of ammonia in mesophilic and thermophilic digestion. These authors explained the discrepancies found in numerous studies due to the different levels of free ammonia reached in the digestion assay, the values of which depend greatly on temperature, pH conditions and ammonium concentration. Thus, higher temperatures favor the degradation rate, reaching higher ammonium levels in a lower period, affecting the pH of the system and creating a toxic environment because of the high free ammonia content in the reactor. The increase in temperature may need specific adaptation protocols for the microbial biomass based on the intrinsic characteristics of the substrate and the OLR at which the reactor is expected to operate.  
[PERSON] and [PERSON] [208] studied the digestion of sewage sludge under thermophilic conditions in a high-solid configuration (9-10% total solids). These authors included in the digestion system an ammonia stripping stage to remove the excess ammonia produced. This way, the digestion could proceed at low levels of total ammonium nitrogen of 1720 mg N/L (below the value of 2500 mg N/L reported as inhibitory by the authors) proving that this strategy is an efficient way of attaining higher gas production rates at high loadings.
## 3 Comparison Traditional Access Control vs. Security Access Control Based on Three Layers
### Security Access Control Based on Three Layers (Ideal Security Policy)
#### 3.2.2 Tactical Layer  
The second layer, known as the tactical layer, encompasses a BYOD policy that focuses on authentication through access control, consisting of three key functions [1]. Firstly, the identification access control policy establishes access controls for BYOD devices to safeguard an organization's information services and resources from unauthorized access. Within BYOD ecosystems, activities such as illegal use or inappropriate communication in information-sensitive applications, including password authentication, authorization, and network segmentation, are strictly prohibited.  
The second function is the security communication policy, also known as data protection. Its objective is to ensure the protection of confidential data while enabling secure and protected access, sharing, and transfer between BYOD devices and the organizational infrastructure. Various security control mechanisms, such as encryption, virtual private networks (VPN), data wiping, and data backup, are employed to achieve data confidentiality.  
Thirdly, the application control policy is responsible for safeguarding organizations that adopt BYOD from malicious applications. The application management policy aims to prevent any confusion between personal and business data, allowing the exchange of  
Figure 2: Secure Three-Layer Architecture for BYOD Access Control Policy [1].  
data between personal and company applications on BYOD devices. Control mechanisms such as virtualization, licensing, application blocklisting, application whitelisting, and containerization support this layer.  
Overall, the tactical layer addresses multiple security requirements, including confidentiality, integrity, and prevention of unauthorized access.
## 4 Simulation Results
### Retrieval Using Gaussian Basis Functions
There are some horizontal gaps in the plot, indicating that discontinuity is present in the value range of the retrieved field (\(R_{i}^{\prime}\)). This is because the retrieved \(\{\psi_{n}\}_{n=1}^{N}\) after the first least-squares computing was reduced unevenly by the PL function. Panel (c) shows the scatter plot for the retrieved field in Figure 3e, and the PCC is 0.883. The scatter plot for the retrieved field with Gaussian basis functions (Figure 5a) is in panel (d), and the PCC is 0.892.
## 4 Methodology to Define the Haptic Scales
### Experiment 2: Defining the Limits of the Range of the Frequency Haptic Scale
#### 4.2.1 Experimental Design  
We have designed an adaptive method based on the responses of the users. Initially, the user feels a set of three haptic icons with a fixed amplitude and frequency. This set of three icons can include a single wave shape repeated thrice, two different wave shapes (one of them repeated twice) or three different wave shapes. The order in which the waves were presented was randomised. Users answer how many different wave shapes have they perceived in that set. Similarly to the previous experiment, while users answer correctly, the value of the frequency increases. Following the JND, the value of the frequency increases by 1/5 of the base frequency. Once the subject fails the first time, our system begins to evaluate the percentage of failures from that moment on. When the failure rate reaches 50% (we chose the 50%-discriminated disparity [46]), the experiment stops, and we take the maximum frequency value with which the subject was still able to discriminate the shapes.