esa-sceva/satcom-chunk-collection huggingface dataset:
# Seismic Design of Steel Moment-Resisting Frames with Damping Systems in Accordance with KBC 2016
## 3 Damping System Design Procedure
### Target Damping Ratio Calculation  
In order to estimate the target damping ratio, nonlinear response history analysis of the seismic-force-resisting system designed in Section 3.1 is performed repeatedly with a damping ratio raised at a constant increment. The peak story drift and peak base shear of the seismic-force-resisting systems are computed for each analysis until those peak responses reach their target values, respectively. Both peak story drift and peak base shear are determined as average responses obtained from nonlinear response history analysis using seven or more ground motion records. The target base shear \(V^{*}\) is determined as the minimum base shear corrected by the demand-capacity ratio (DCR), which is determined in the elastic design of the seismic-force-resisting system and represent redundancy in the design.  
\[V^{*}=\frac{V_{min}}{DCR} \tag{5}\]  
The damping ratio of the seismic-force-resisting system is composed of inherent damping and damping added by damping systems. In this study, the damping ratio of the structure is assumed to be 5%. The target damping ratio \(\beta_{t}\) is calculated by Equation (6).  
\[\beta_{t}=\beta_{IH}+\beta_{v} \tag{6}\]  
where, \(\beta_{IH}\) is the inherent damping ratio and \(\beta_{v}\) is the damping ratio added by damping system. In the nonlinear response history analysis, \(\beta_{v}\) is increased at a constant increment, and the increment need not be too small because the response can be interpolated numerically. Thus, three to five times of nonlinear response analysis for a given ground motion set is adequate.  
Response reduction ratio with respect to effective damping ratio can be plotted in order to determine the target damping ratio and design of damping devices. An illustrative example of such plots is represented in Figure 2, where response reduction ratios for both base shear and maximum story drift are plotted in broken lines.
# BiModalClust: Fused Data and Neighborhood Variation for Advanced K-Means Big Data Clustering
## 4 The Proposed Algorithm
### Analysis of the Algorithm  
The proposed algorithm, BiModalClust, iteratively constructs and optimizes partial solution landscapes for the MSSC problem, restricted to relatively small input data samples \(S\) that are randomly drawn from the original dataset \(X\). The K-means algorithm serves as the local search mechanism, identifying optimal solutions within cyclically expanding neighborhoods of the incumbent solution \(C\).  
BiModalClust also incorporates an explicit shaking procedure to perturb the incumbent solution. Following the local search phase, exactly \(p\) random centroids, including any degenerate ones, are reinitialized within the incumbent solution \(C\). As \(p\) increases, the intensity of the perturbation applied to \(C\) grows. In the extreme case where \(p_{\text{max}}=k\) and \(p\) reaches \(p_{\text{max}}\), the entire incumbent solution is reinitialized using the K-means++ seeding strategy. This results in a complete restart of the solution. However, it is generally more practical to set \(p_{\text{max}}\) to a value much smaller than \(k\). Limiting \(p_{\text{max}}\) ensures that the applied perturbations remain controlled, preventing a total or near-total restart.  
The shaking power \(p\) is incrementally increased with each iteration. This approach allows for a gradual yet controlled expansion of the search space for the local search procedure in each successive partial solution landscape. By leveraging the K-means++ seeding logic to sample new centroids during the shaking phase, BiModalClust ensures that the newly introduced centroids are well-distributed across the sample, promoting optimal coverage. This strategy minimizes the risk of the incumbent solution becoming trapped in local minima, such as scenarios where multiple cluster centers are positioned too closely together, erroneously splitting a single true cluster.
# Evolution of the Complex Supply Chain Network Based on Deviation from the Power-Law Distribution
## 2 Evolutionary Characteristics and Model Analysis of Complex SCNs
### SCN Analysis Based on Scale-Free Network Evolution
The connection probability expression is as follows: \[\prod\limits_{j\to i}=\frac{k_{i}}{\sum\limits_{j}k_{j}},\] (2)
* Dynamics of network evolution: Due to the competition and cooperation mechanism of the economic market, node companies need to constantly learn and update to maintain the policy operation of the entire SCN and not only will increase as the SCN evolves but also because they cannot adapt to the market. Elimination leads to the dynamic nature of the evolution of the network topology over time. The probability of selecting node \(i\) as the disconnected end is as follows: \[\prod\limits_{j\to i}r=\frac{1}{N(t)-1}(1-\frac{k_{i}}{\sum\limits_{j}k_{j}}),\] (3)  
In Formula (3), \(N(t)\) is the total number of network nodes when disconnecting the edges. * Emergence of network evolution: SCN node companies may be affiliated with multiple supply chains simultaneously in the network, and nodes have multimodal competitive relationships and nonlinear interactions, and there are intersections between multiple supply chains or multiple networks. The phenomenon of nesting promotes the evolution of the network. The SCN will emerge with new structures and functional characteristics under the joint action of internal factors and the external environment and other systems, such as the small-world nature of network functions, cascading failures and agglomeration, robustness, community structure, etc., on the network structure.
# Stripe-Assisted Global Transformer and Spatial-Temporal Enhancement for Vehicle Re-Identification
###### Abstract  
As a core technology in intelligent transportation systems, vehicle re-identification has attracted growing attention. Most existing methods use CNNs to extract global and local features from vehicle images and roughly integrate them for identifying vehicles, addressing intra-class similarity and inter-class difference. However, a significant challenge arises from redundant information between global and local features and possible misalignment among local features, resulting in suboptimal efficiency when combined. To further improve vehicle re-identification, we propose a stripe-assisted global transformer (SaGT) method, which leverages a dual-branch network based on transformers to learn a discriminative whole representation for each vehicle image. Specifically, one branch exploits a standard transformer layer to extract a global feature, while the other branch employs a stripe feature module (SFM) to construct stripe-based features. To further facilitate the effective incorporation of local information into the learning process of the global feature, we introduce a novel stripe-assisted global loss (SaGL), which combines ID losses to optimize the model. Considering redundancy, we only use the global feature for inference, as we enhance the whole representation with stripe-specific details. Finally, we introduce a spatial-temporal probability (STPro) to provide a complementary metric for robust vehicle re-identification. Extensive and comprehensive evaluations on two public datasets validate the effectiveness and superiority of our proposed method.  
computer vision; vehicle re-identification; discriminative feature; spatial-temporal probability; intelligent transportation systems +
Footnote †: journal: Applied sciences
## 3 Background
### Hierarchical Multi Blockchain  
A Hierarchical Multi-Blockchain was proposed in [9] as a solution for fine-grained access to data in medical environments, with multiple participants (e.g., hospitals, medical device manufacturers, insurance companies, etc.) and multiple types of users (e.g., doctors, technical staff, etc.). This architecture was intended to enable autonomous administration of trusted medical data and transactions between mutually untrustworthy stakeholders, while at the same time providing a built-in forensic mechanism optimized for granular auditing. End users from a variety of health care domains can access and securely exchange medical data, provided that a domain-specific access policy is adhered to.  
Figure 2 illustrates the architecture of a Hierarchical Multi-Blockchain. At the first level, a Proxy Blockchain is the _single access point_ for participating users. This provides interoperability between independently managed trust authorities and also acts as an immutable _single source of truth_ for all transactions. At the second level, one or more Domain Blockchains enable each domain (e.g., hospitals, device manufacturers, insurance providers) to enforce their policies and provide fine-grained access control via Attribute-Based Encryption (ABE). Databases are handled locally by each entity and data are encrypted with attribute keys based on the access policy of the corresponding domain. Services at all levels are implemented by smart contracts published on the relevant blockchain.  
Other studies have also adapted hierarchical multi-blockchain for secure data storage and sharing in various environments. For example, in [47], the authors propose such a model as a solution for massive data traffic in IoT environments, while in [48; 49] a similar multi-domain blockchain architecture is presented as an answer to scalability issues. In [50], the authors propose an architecture to minimize IoT information loss by using multiple blockchain groupings, linking them in hierarchical chains. A similar scheme is used in [51] for fine-grained audit capability using sensor values to strengthen confidence. The authors in [52] use sidechains to ensure hierarchical fine-grained data access, but the proposed architecture lacks domain isolation and cannot be used for flexible, manageable domain-wise and cross-domain access policy enforcement. Other applications of hierarchical multi-blockchains involve domain level supervision systems [53].  
Figure 1: MA-ABE attribute binding and key generation.  
Despite the growing evolution of Hierarchical Multi-Blockchain applications, numerous open challenges remain. These mainly concern the aspect of security (e.g., strong trust assumptions) and implementation (e.g., synchronization between multiple blockchains and inter-blockchain communication).
## 2 Methods
### SDVN Framework
#### 2.1.2 Route Prediction Phase  
In the P2P communication process, the source node firstly needs to send a route request (RREQ) packet to the nearest RSU. The RREQ message contains the source's ID and destination's ID. When receiving RREQ, the routing server will calculate a more reliable and optimized routing path from source to destination using global network state vector and our proposed routing algorithm. After that, this routing server will respectively respond a route reply (RREP) to all nodes on the routing  
Figure 1: The architecture of software-defined network (SDN)-based routing framework.  
path (including source, destination, and other intermediate vehicles) via local controllers. When these nodes receive the respective RREP message, they insert the routing path mark and next hop node ID to their own routing tables. Then the data flow will be sent out and propagated along the route to the destination. The route will stay alive for a certain length of time, after this time, it will be deleted so that the new routing path will be recomputed for the next cycle. This alive-timeout length is important to make our routing algorithm adaptive to the dynamic network topology. In addition, the routing path also will be limited by a certain maximum number of hops, so that the communication distance between source and destination will not be too long to reduce the efficiency of data packet transmission. The route discovery process in detail is described in Algorithm 1, and the whole session process of our protocol in detail is described in Algorithm 2.
## 2 Preliminaries
### Federated Learning  
The term federated learning (FL) was introduced in 2016 by [PERSON] et al. [3]. FL is a machine learning setting where many clients collaborate to train a centralized ML model. Each client's raw data are stored locally and not exchanged with other parties; only updates needed for immediate aggregation are shared with the central server.  
Two main settings are discussed in the literature [16]: the cross-device and the cross-silo. The difference between the two is simple, cross-device is associated with mobiles and IoT devices while cross-silo is associated with organizations like hospitals, banks, etc. In cross-silo, the number of clients is small and they have large computational ability. On the other hand, cross-device considers a huge number of clients with small computation power. Another difference between the two settings is reliability. In cross-silo, the organizations are always available to train, unlike user devices.  
FL can also be classified by data partition. We distinguish Horizontal FL, Vertical FL and Hybrid FL. In HFL, the datasets of the clients have the same features space. In VFL, the local datasets have the same individuals, but with different features. The hybrid setting is a combination between HFL and VFL.  
A typical federated training process is considered by the algorithm of FedAvg proposed by [PERSON] et al. [3]. It consists of five steps: The server selects a subset of clients according to some criteria. The selected client downloads the current model weights and a training program from the server. Each client locally computes an update to the model by executing the training program. The server then collects an aggregate of device updates and updates the central model.
# Electrically Conductive Nanocomposite Fibers for Flexible and Structural Electronics
## 3 Results and Discussion
### Fabrication of Continous Fiber  
Composite fiber extrusion attempts showed a significant effect of the amount and type of functional phase on the ability to produce a continuous fiber with a small diameter. The addition of a functional phase in the form of graphite powder (materials G1-G5), even in large amounts of up to 50 vol. %, did not significantly impair the rheological properties of the composite. Thus, it was possible to continuously extrude the fibers a with high graphite content with a constant diameter. To the best of our knowledge, continuous fibers with such a high graphite content and a diameter of less than 1 mm have not yet been reported in the literature. The polymer/graphite composites reported so far, contained a significantly smaller amount of the functional phase, typically below 35 vol. % [11; 12; 13; 14; 15] and/or a higher fiber diameter, especially 1.75 mm to be used in fused deposition modeling 3D printing [16].  
During the extrusion attempts of composites containing CNTs as the functional phase (materials C1-C3), significant difficulties were observed in obtaining a continuous fiber. Despite the possibility of extruding long fibers with CNT content up to 10 vol. %, microcracks were visible on the surface. The fibers were characterized by significant roughness. According to the literature, additives of carbon filler in the polymer matrix cause an increase in viscosity [17]. However, this phenomenon is significantly greater for the CNT filler than for the graphite filler. An increase in composite viscosity results in the reduced extrusion efficiency, reduced homogenization in the extruder barrel and a \"sharkskin effect\" appearing on the fiber surface [18; 19]. The microcracks visible on the fiber surface were caused by the presence of high friction at the extruder nozzle exit [20].  
Composites containing a mixture of CNT and graphite as a functional phase did not exhibit \"sharkskin effect\" after extrusion, in spite of the overall higher content of the carbon phase. This was due to the excellent lubricating properties of graphite [21; 22]. The addition of graphite to the composite allowed for a significant reduction in the coefficient of friction between the extruder nozzle exit and the extruded composite. It resulted in a reduction in local stresses and the deformation of the composite fiber surface (Figure 1).
# Three-Dimensional Broadband Electric Field Sensor Based on Integrated Lithium Niobate on Insulator
###### Abstract  
A three-dimensional (3D) electric field sensing scheme is proposed and experimentally demonstrated based on an integrated lithium niobate on insulator (LNOI) platform. The 3D measurement is realized by packing three LNOI-based sensor chips in a triangular-prism-type clamp. For each sensor chip, the optical waveguide has an asymmetrical Michelson interferometer architecture, and the tapered dipole antenna is inclined to the optical waveguide. By finely placing the three sensor chips in the clamp, the three pairs of inclined tapered dipole antennas are mutually orthogonal and can be applied to measure the electric field in three orthogonal polarization directions. The volume of the packaged 3D sensor is 9.5 cm\({}^{3}\). In the experiment, a flat response in the frequency range of 10 MHz to 3 GHz is demonstrated. In addition, a \(3\times 3\) response calibration matrix is obtained and utilized to reduce the measurement error. After calibration, the relative measurement error of the electric field amplitude is smaller than 5.1% for every polarization direction.  
electric field sensor; lithium niobate on insulator; Michelson interferometer +
Footnote †: journal: 8
## 4 Invariant Multi-Mode Vectors Pushover Analysis
### Improved Modal Pushover Analysis  
In contrast to the modal pushover analysis (MPA) procedure which utilizes invariant multi-mode lateral load pattern vectors for obtaining responses, the improved modal pushover analysis (IMPA) [57] incorporates the redistribution of inertia forces after the yielding of the structure. The key enhancement in IMPA is its use of post-yielding structural deflection shape as an invariant lateral load pattern. However, to circumvent extensive computation, it suggests a two-phase lateral load distribution for the first mode, while the force patterns for the higher modes follow the MPA approach. The steps encompassed in the IMPA procedure are as follows:  
1. Initiate with first mode MPA: Begin with the initial steps (1-3) of the MPA method for the primary'mode'. The initial load pattern is determined by the lateral force distribution, represented as \(S_{1}*=m\Phi_{1}\).
2. Determine displacement at yield: from the pushover analysis in the first step, ascertain the structure's displacement vector, \(\psi 1y\), at its yielding point.
3. Progress with an updated load pattern: From the point of structural yielding, extend the pushover analysis using a new load distribution \(S1y*=m\Phi 1y\). This is the second-phase lateral load pattern. Utilize this updated curve and the subsequent Steps 4-7 of the MPA method to assess the structure's behavior.
4. Compute overall response (IMPA): Merge the primary'mode' response from Step 3 with the responses of higher modes derived from the conventional MPA method. Combine using methods like the square root of the sum of squares (SRSS) or complete quadratic combination (CQC).
## 3 Data Collection and Preparation
### Parameter Calibration  
For the model calibration, the GEH (Geoffrey E. Havers) statistic was used following VISSIM protocol [25]. Model verification was also done as part of a process pertaining to the model calibration. Three parameters (CC0, CC1, and CC2) for car following model, nine parameters for lane change model, and lane change distance were calibrated until the traffic volumes from the simulation fell within an acceptable range of convergence (GEH \(<\) 3.0) with the observed volumes from the field (5417 veh/h).  
The calibration started after the first simulation ran with the default values, which were given in the VISSIM software, for car following model, lane change model, and lane change distance. The next step was to calibrate only the three parameters of car following model: CC0, CC1, and CC2 [25].  
The car following model, Wiedenmann 99, in VISSIM, has ten parameters from CC0 to CC9. However, the VISSIM protocol [25] recommends calibrating only three of them. They are standstill distance (CC0), headway time (CC1), and following variation (CC2). For the parameters from CC3 to CC9, the protocol recommends using the default values given in the VISSIM software. Moreover, the CC0, CC1, and CC2 cannot be calibrated separately. Since the following distance between two successive vehicles in VISSIM consists of desired safety distance and following variation (CC2), of which the desired safety distance is the summation of the standstill distance (CC0) and the headway distance (CC1 \(\times\) Speed), the combination of three parameters affect the resultant traffic flow in simulation.  
_Following Distance = Desired Safety Distance (CC0 + CC1 \(\times\) Speed) + Following Variation (CC2)_  
Therefore, the sensitivity analysis method based on the suggested range from VISSIM was applied to the calibration of the combination of the three parameters. From this calibration, the best combination of the three parameters was chosen which returned the highest traffic flow near to the real traffic flow from the field: 1.6 m of CC0, 0.9 s of CC1, 12 m of CC2, and resultant maximum traffic flow of 4567 veh/h.  
After the calibration of three parameters for the car following model, similar sensitivity analyses were conducted for the calibration of the nine parameters of lane change model one by one, as well as for the lane change distance, starting at a given default of 200 m and in 100 m increments tested up to 700 m. The compulsory lane-changing behavior of VISSIM was specifically considered, focusing on the maximum and accepted deceleration rates for the merging and trailing vehicles. The best-calibrated values for the lane change model were chosen, shown in Table 2. Through these second and third calibration processes, the VISSIM returned the maximum traffic flow of 5447 veh/h, which would fall within an acceptable range of convergence (GEH \(<\) 3.0).