esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Test Results and Analysis  
### Test Phenomena and Damage Morphology  
In the process of launching the test, all of the specimens emitted a slight ringing sound. The sound of the core concrete's friction on the steel tube wall was registered when specimen ZU\({}_{0.5}\)N\({}_{0.5}\)Y was loaded to 7.50 mm displacement and suddenly thumped loudly. The internal weld reinforcement's weld fracture sound was registered after the sudden drop in the load rose slowly. The damage process of the test specimen followed a similar law: at the beginning of loading, the steel tube and the core concrete worked together, and relative sliding had not yet occurred between them; with increasing load, the interface cracks developed slowly, and a small misalignment occurred between the steel tube and the core concrete at the loading end when the tension sensor at the measured end produced readings. It can be assumed that, at this time, the interfacial adhesive force failed and, thus, friction and mechanical occlusion played the main roles [24]. After the peak load, the relative sliding of the core concrete continued to occur under the action of the axial load. The test machine load exhibited a stable trend, accompanied by the sound of the concrete being crushed by extrusion, and the internal welded steel rebar ring weld was torn; at this time, friction, mechanical occlusion force, and ring rebar shear joints together transferred the load to the steel tube. The specimen damage morphology after the launch of the test is shown in Figure 3. After removing the steel mat, the loading end of the steel tube-concrete interface was ground, resulting in concrete debris; at the loading end of the inner wall of the steel tube, friction scratches were visible to the naked eye, and the measured end also contained concrete debris. The interface of the smooth unconstructed specimens was relatively flat, with only some slip marks, and the specimens constructed with internally welded reinforcement rings had mostly small concrete fragments. The damage pattern of the specimens depended on their construction type. No local buckling occurred in the tubes of the smooth unconstructed specimens (ZU\({}_{0}\)N\({}_{1}\)W, ZU\({}_{1}\)N\({}_{0}\)W, and JU\({}_{1}\)N\({}_{0}\)W); the specimens with the internally welded one-ring or two-ring reinforcing ring construction all bulged outward at the welded reinforcing rings of the tubes, due to the high thrust of the tubes transmitted from the internally welded reinforcing rings; when the pushout load continued to increase, the stress in the tubes continued to increase beyond the yield stress of the steel. The high thrust in the tubes caused the concrete to squeeze the walls, which explains the bulging deformation of the steel tube walls. From the above analyses, it can be concluded that the existence of internally welded reinforcement rings can largely prevent the rapid development of gaps at the steel tube-concrete interface, transfer axial loads to the steel tube efficiently, and change the damage mode of the interface.  
Figure 3: Specim damage morphology.
# FLGQM: Robust Federated Learning Based on Geometric and Qualitative Metrics
###### Abstract  
Federated learning is a distributed learning method that seeks to train a shared global model by aggregating contributions from multiple clients. This method ensures that each client's local data are not shared with others. However, research has revealed that federated learning is vulnerable to poisoning attacks launched by compromised or malicious clients. Many defense mechanisms have been proposed to mitigate the impact of poisoning attacks, but there are still some limitations and challenges. The defense methods are either performing malicious model removal from the geometric perspective to measure the geometric direction of the model or adding an additional dataset to the server for verifying local models. The former is prone to failure when facing advanced poisoning attacks, while the latter goes against the original intention of federated learning as it requires an independent dataset; thus, both of these defense methods have some limitations. To solve the above problems, we propose a robust federated learning method based on geometric and qualitative metrics (FLGQM). Specifically, FLGQM aims to metricize local models in both geometric and qualitative aspects for comprehensive defense. Firstly, FLGQM evaluates all local models from both direction and size aspects based on similarity calculated by cosine and the Euclidean distance, which we refer to as geometric metrics. Next, we introduce a union client set to assess the quality of all local models by utilizing the union client's local dataset, referred to as quality metrics. By combining the results of these two metrics, FLGQM is able to use information from multiple views for accurate poisoning attack identification. We conducted experimental evaluations of FLGQM using the MNIST and CIFAR-10 datasets. The experimental results demonstrate that, under different kinds of poisoning attacks, FLGQM can achieve similar performance to FedAvg in non-adversarial environments. Therefore, FLGQM has better robustness and poisoning attack defense performance.  
federated learning; poisoning attack; robust defense +
Footnote †: journal: Applied Sciences
# New Treatment Addressing the Pathogenesis of Psoriasis
## 3 Treatment
### Janus Kinase (JAK) Inhibitors  
Type 1 and 2 cytokine receptors strongly depend on the JAK and STAT pathways [84]. The JAK family is intracellular protein tyrosine kinase and includes JAK1,2,3, and TYK2, and the STAT family includes STAT1, 2, 3, 4, 5a, 5b, and 6 [84]. The functions of each JAK and STAT are different [84]. The IL23 receptor, which is important in psoriasis, is associated with JAK2, TYK2, and STAT3 [84]. JAK inhibitors are currently being tested in clinical trials for the treatment of psoriasis (Table 2) [85].  
Tofacitinib, which is a first-generation JAK inhibitor, primarily targets JAK3, JAK2, and JAK1 [85]. In the phase 3 studies, the PASI75 response to tofaitinib at weeks 16-24 was 39.5-54.3% (5 mg twice daily) and 59.2-81.1% (10 mg twice daily), as compared to 5.6-12.5% for the placebo [86, 87, 88, 89]. During the study, increased circulating total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, and creatinine phosphokinase levels and decreased blood hemoglobin and lymphocyte counts were observed [85]. Serious adverse events including angina pectoris, pyclonephritis, urosepsis, and atrial fibrillation were observed in three patients [90]. Nasopharyngitis, sinusitis, upper respiratory tract infection, back pain, and headache were observed as adverse events [90]. Now, new JAK inhibitors such as JAK1/JAK2 inhibitor baricitinib are under clinical trial and exhibited good results [91].  
The TYK2 inhibitor BMS-986165, which is a more selective JAK inhibitor than first-generation candidates, showed efficacies of 75% (12 mg daily), when compared to 7% in the placebo group in terms of PASI75 response at week 12 of treatment in a phase 2 clinical trial [85].  
The TYK2/JAK1 inhibitor PF-06700841 directly suppresses TYK2-dependent IL12 and IL23 signaling, and JAK1-dependent signaling in cells such as T cells and keratinocytes [92]. PF-06700841 improves clinical symptoms of chronic plaque psoriasis by inhibiting proinflammatory cytokines that require TYK2 and JAK1 for signal transduction [92]. Topical PF-06700841 is now under investigation in a phase 2 trial [93].
# Low-Computational Extended Orthogonal Matched Filter Structure for Multiuser Detection
## 4 Performance Evaluation and Discussion  
### Purpose of Computer Simulation  
Two kinds of computer simulations built in MATLAB were performed to evaluate the performance of the proposed system. The purpose of the first computer simulation, named Multiple Users Detection Test (MUDT), is to confirm whether the proposed structure can be detected by multiple users. MUDT is confirmed in the case of two user detection. That is, the respective interference cancellation performances are compared under substantially the same conditions as the situation in which desired users 1 and 2 exist. On the other hand, the purpose of the second computer simulation, named Communication Quality Evaluation Test (CQET), is to compare the interference cancellation performance of the proposed structure and the conventional EOMF. CQET compares the bit error ratio (BER) according to the DIR of each system. At this time, there are multiple desired users in the proposed system, and the performance of desired user 1 among them is compared with that of the conventional EOMF. Hence, the conditions for the desired user 1 of the proposed system and that for the desired user of the conventional EOMF are almost the same. DIR in MUDT and CQET is defined by Equation (32):  
\[\text{DIR}=10\log_{10}\frac{P_{m_{d}}}{\sum_{m=m_{d}}^{M}P_{m}}. \tag{32}\]  
Figure 3: Example of the computational complexity of the conventional EOMF and the proposed structure. \(M_{d}=2\) to 8; \(K=8\); \(N=31\). The red and blue bars represent the result of the conventional EOMF and the proposed structure, respectively.  
Here, \(P_{m}\) (\(m=1,\ 2,\ \dots,\ M\)) is the signal power of user \(m\), and \(M\) is the total number of users. The desired user number of the conventional EOMF is \(m_{d}=1\). Then, the computer simulation parameters are based on [18]. Moreover, no specific application is assumed since the performance of the system itself is confirmed in the computer simulation.
## 2 Methods
### Introduction of Network Architecture
#### 2.2.2 Encoder-Decoder Network Architecture
In this model, except for the last convolutional layer (Conv 7), a batch normalization module and an activation function are applied after every convolutional layer, dilated convolutional layer, and transposed convolutional layer, as shown in Figure 6(b). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number** & **Block Name** & **Input/C** & **Output/C** & **Output/Size** \\ \hline Conv3 & 64 & 64 & \(208\times 144\) \\ Conv4 & 3 & 32 & \(208\times 144\) \\ Conv5 & 128 & 128 & \(208\times 144\) \\ Conv6 & 128 & 128 & \(208\times 144\) \\ Conv7 & 128 & 1 & \(208\times 144\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._  
Figure 7: Residual structure, batch normalization and activation function position: (**a**) The network structure of the residual unit in ResNet; (**b**) Batch normalization module and activation function location. To evaluate the performance of the incorporated feature fusion module and refinement module, the CNN model was trained and tested on the RGB-D dataset alongside the original model. The testing results are illustrated in the accompanying figure. As depicted in Figure 8, as the number of iterations increases, the loss values of both the ResNet model and the improved model exhibit three distinct phases: a rapid decline, a gradual decline, and a stabilization phase. Notably, the evaluation metrics of the model enhanced with the feature fusion and refinement modules consistently outperform those of the original model across all iteration stages. Furthermore, the improved model demonstrates smaller curve fluctuations compared to the original model, indicating a more stable overall detection performance.
## 3 The Performance of the \(8\times 8\) PAA  
### The PAA Configuration  
An \(8\times 8\) planar PAA simulation model is established in CST2022 to verify the scanning performance of the proposed antenna. According to the PAA grating lobe suppression condition, G \(<\lambda/(1+\sin\theta_{\max})\), where G is the spacing between adjacent elements, \(\lambda\) is the wavelength, and \(\theta_{\max}\) is the maximum scanning angle without grating lobes [37]. To ensure that there are no grating lobes during the wide-angle scanning process across the entire operating bandwidth, the element spacing is set to 1.3 mm (which is 0.516\(\lambda_{0}\) at 119 GHz). Consequently, the dimensions of the \(8\times 8\) planar PAA are 10.4 \(\times\) 10.4 \(\times\) 0.75 mm\({}^{3}\). Shown in Figure 8 is the CST simulation model of the \(8\times 8\) planar PAA. We have commissioned Taizhou Wangling to conduct a cost estimation for the proposed \(8\times 8\) planar PAA. The quoted material cost for the dielectric substrate is RMB 10,000, and the processing fee is RMB 1700, making the total cost RMB 11,700.
## 1 Introduction
[14; 15] calculated the illumination angle of solar light at a specific time using astronomical knowledge and used the plane geometric relationship between the box girder structure and the solar irradiation light to determine the calculation formula for the shadow shielding height of the box girder web, but this method is difficult to use to determine the plane geometric relationship between the structure and the sunlight, and the calculation workload is large when it is used for complex structures such as bridge towers and trusses. Subsequent scholars used ray tracing in computer graphics theory to research the dynamic recognition algorithm of sunshine shadows. The core technology of the sunshine-shadow recognition algorithm is to determine the intersection between sunshine light and structural surface mesh. In the algorithm solution, it is often realized by a cross multiplication or triangle barycenter coordinate operation [16]. If the shadow is identified directly through a large number of cross-multiplication operations, the calculation process is accompanied by a large number of invalid operations, the calculation efficiency is low, and the computer's computing power has higher requirements. When the method of shadow recognition by triangular barycentric coordinate calculation is used for models with a large number of grids, the calculation time is long, which greatly wastes computing resources. How to improve and optimize the shadow recognition algorithm to improve the calculation speed is an urgent problem for the accurate calculation of the three-dimensional temperature field of complex bridge structures. [PERSON] and [PERSON] et al. [17; 18] effectively improved the computing efficiency of the sunshine shadow algorithm by introducing spatial subdivision technology and a grid acceleration structure, respectively. However, the number of meshes in the calculation model has also increased accordingly with the continuous increase in the span of modern bridges, further improving the efficiency of the shadow recognition algorithm, which is of great significance for the calculation of a three-dimensional sunshine temperature field, considering the influence of sunshine shadow. This paper used the Python programming language to write a script to extract the mesh information of the structural surface based on the previous research, which greatly reduced the workload of the computer in shadow recognition. At the same time, the traditional sunshine-shadow recognition algorithm was improved and optimized by combining the cross-product judgment method and the triangle barycentric coordinate method in ray tracing, and the efficiency of the algorithm for sunshine-shadow discrimination was further improved. Finally, a set of accurate and efficient three-dimensional temperature field simulation methods was developed. The distribution characteristics of the temperature field were analyzed based on the accurate simulation of the three-dimensional temperature field of the concrete bridge tower in this paper, which provided a reference for the design and construction of the concrete bridge tower.
## 3 Experimental Results
### Humid Air Exposure Condition  
The defective cable was placed in a sealed glass chamber, with a defect width of 1.2 mm. The sprayed mist had a liquid conductivity of 3 mS/cm, and the grounding resistance was 30 \(\Omega\). The initial relative humidity inside the chamber was 55%. During the continuous pressurization process, no discharge phenomenon was observed. As the mist spraying continued, gradually increasing the humidity inside the chamber, a small amount of liquid condensation was observed on the cable surface. When the relative humidity exceeded 80%, intermittent sub-cycle arcs, as shown in Figure 8, were observed. These arcs were accompanied by sharp sounds but exhibited no significant arc flashes. In this case, the defect area was noticeably moist, indicating that moisture accumulation contributed to arc generation. Compared to the sub-cycle arcs caused by direct liquid penetration into the defect, arcs extinguished rapidly after initiation, with a duration of approximately 0.5 ms. In terms of waveform characteristics, these arcs also occurred at the voltage peak, with the current displaying a narrow pulse shape. The amplitude of these sub-cycle arc currents was similar to that of sub-cycle arcs in the first and second stages in 3.1, ranging from approximately 6 to 8 A.  
Due to the slow accumulation of moisture at the site of the defect in humid air, the arc discharge rapidly evaporates the accumulated liquid. As a result, only intermittent arcs occur throughout the experiment, with the time interval between two arcs generally reaching several hours. Higher relative humidity facilitates liquid accumulation at the defect. When the relative humidity approaches saturation (>90%) and water mist continuously surrounds the cable defect, the interval between two arcs can be reduced to tens of minutes. Under these conditions, a cable sample generated six arcs during a five-hour experimental period. However, the change in the defect width was negligible, and the sample did not reach the stage where a continuous leakage current was generated. This indicates that the duration of each arc was very short, resulting in limited ablation of the insulation.
# Hydrogen Production from Offshore Wind Parks: Current Situation and Future Perspectives
## 3 System Configurations
### Onshore Electrolyzer Scenario  
This approach is also known as a hybrid system, where the energy produced is transmitted to shore as electricity in conventional cables; once onshore, the energy can be sold directly to the grid or used to produce hydrogen. The main advantage of this system is flexibility: when the market price for electricity is high, the investor can sell electricity directly to the grid; when the market price is low or grid level curtailment must occur, the energy can be redirected to an electrolyzer to produce hydrogen. Curtailment occurs when the production of electricity is greater than the consumption, which leads to a need to reduce the production. Figure 3 contains an overview of the onshore electrolyzer system.  
Since in this approach the electrolyzer will be onshore, it is possible to place the electrolyzer and all other sensitive equipment inside a building, where they can be sheltered from the elements; this also provides a better work environment for the personnel responsible for the operation and maintenance. Furthermore, since access to the elec  
Figure 3: Onshore electrolyzer system.  
trolyzer is much simpler than if it was offshore, the increased maintenance requirements and decreased power density of an AEL do not present as big an obstacle, so the reduced CAPEX of this technology means both AEL and PEMEL are viable when the electrolyzer is installed on land.  
HVDC is a more expensive technology that only becomes interesting when wind farms are located far from shore and/or have high nominal powers [26]. In the case of HVAC, longer lines imply more powerful line-reactive compensators to account for the capacitive losses, which in turn increases the cost. Since HVDC transmission does not show capacitive losses--only ohmic losses occur--the transmission losses and costs are significantly lower for HVDC. To summarize, transmission losses and costs are lower in the case of HVDC, and even though the initial investment for HVDC transmission (stations and equipment) is higher than HVAC, the difference in cost diminishes when the transmission distance increases. The break-even distance for which HVDC becomes preferable is around 50-100 km for underground and underwater cables [26].  
Regarding the source of water, the two possible options are connecting the electrolyzer to the freshwater grid, an option that might not be viable due to environmental concerns in areas with recurring droughts, like southern Europe, or installation of a desalination unit next to the electrolyzer. Even though the water produced by a desalination unit is clean, and the water in freshwater grids has been previously treated, further treatment such as deionizing the water is still required for both options before being used in electrolysis [27].
## 3 CENTERA: Our Centralized Trust-Based Efficient Routing Protocol with Authentication
### Nodes Analysis and Metrics Calculations Block
The maliciousness is calculated based on the ratio of the bad packets to the total packets received, as follows:  
\[maliciousness(N)=\frac{\sum\mathit{bad\ packets\ received\ from\ N}}{\sum\mathit{ packets\ received\ from\ N}} \tag{1}\]  
Using information from all the good packets it received, the sink BS calculates the competence and cooperation of all the WSN nodes. The competence shows the ability of a node to properly deliver a packet to the sink BS and is calculated as the ratio of the packets received by the BS to the packets sent by the node, as follows:  
\[\mathit{competence}(N)=\frac{\sum\mathit{packets\ received\ by\ BS\ from\ N}}{\sum \mathit{packets\ actually\ sent\ by\ N}} \tag{2}\]  
where the count of packets sent by a node is calculated using its p_counters, which can be checked using the p_counters of the downlink and uplink nodes. The cooperation shows the willingness of a node N to cooperate and forward packets sent by others towards the sink BS. Cooperation is calculated as the weighted ratio of the number of packets sent by the ULs of N through it over the total number of packets sent by those ULs, as follows:  
\[\mathit{cooperation\ (N)=\ \frac{\sum_{\mathit{ULs\ of\ N}}(a*pkts\ \mathit{rcvd\ by\ BS\ from\ UL/packets\ sent\ by\ UL})}{\mathit{count\ a}}} \tag{3}\]  
Note that (a) is the weight of each uplink node (inversely) related to its maliciousness, as follows:\[a_{i}=1-maliciousness(i) \tag{4}\]  
The sink BS then calculates two trust values for each node: a Data Trust value and a Forwarding Trust value. The Data Trust of a node n is an indication of the benign nature of the packets of n. It is calculated based on the maliciousness of the node while taking into account the cooperation value (in order to force nodes to cooperate to increase their Data Trust). Data Trust assumes values between 0 and 1 and is calculated as follows:  
\[Data\:Trust(N)=\frac{\left(1-maliciousness(N)\right)*\left(cooperation(N) \right)}{competence(N)} \tag{5}\]  
The Forwarding Trust of a node is an indication of the trust in a node's ability to forward a packet and being confident that the packet will be delivered successfully to the sink BS. The Forwarding Trust is calculated based on the approximated battery level and the competence values of a node, as follows:  
\[ForwardingTrust(N)=\left(Approximated\:battery\:level(N)\right)*\left(competence(N)\right) \tag{6}\]