esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 IoT Systems in the Pre-Disaster Stage
### Communication Technologies and Protocols  
People usually transmit the data from sensors to servers through Bluetooth [38], Ethernet [38; 42; 43], Wi-Fi [38; 43; 55; 56; 59; 68; 72], and cellular communication technology [42; 57; 59; 71; 80], Radio Frequency [42], and radio [44]. Cellular communication technologies include GSM [57], GPRS [57; 71], and 3G [42]. The communication protocols include Choco protocol [39], UDP [41], IPv6 with LoWPAN [41], IEEE 802.15.4 [16; 41; 52], Message Queuing Telemetry Transport (MQTT) [42; 43; 47; 59; 67; 81], concurrent multipath transfer protocol [49; 82], LoRaWAN [50; 62; 80], TCP/IP Internet protocol [50; 83], Hyper Text Transfer Protocol (HTTP) [61], Zigbee [16; 45; 71], LRWiFi [59], Cat-M1 [59], CoAP [59], XBee [70]. The data is usually transmitted in JSON format [42; 50; 61]. Regarding data storage, people may use local data storage (e.g., SD memory cards [55]) and cloud storage (e.g., MongoDB [48], Dynamo [43]). [PERSON] and [PERSON] [58] used the SQL Server 2008 database software. [PERSON] et al. [44] store the data in Oracle's MySQL and host the database on an Ubuntu Server. Drones can relay data from sensors to base stations, thereby effectively achieving large-scale data transmission [18]. Drones can cooperate with drifters to collect river velocity data. In this combination, drones aim to locate the drifters and transmit the data from the drifters to a server [42].  
The connection solutions for earthquakes include Bluetooth [38], Ethernet [38], Wi-Fi [38], Choco protocol [39], IEEE 802.15.4 [16; 52], MQTT [81], and Zigbee [16]. Regarding the connectivity solutions for landslides, we can use Wi-Fi [55; 56], cellular communication technology [57; 59], MQTT [59], LRWiFi [59], Cat-M1 [59], and CoAP [59]. The connection solutions for floods include UDP [41], IPv6 with LoWPAN [41], IEEE 802.15.4 [41], MQTT [42; 43], Ethernet [42; 43], cellular communication technology [42; 71; 80], Wi-Fi [43; 68; 72], concurrent multi-path transfer protocol [49], LoRaWAN [50; 62; 80], TCP/IP [50; 83], radio [44], HTTP [61], ZigBee [45; 71], 6 LoWPAN [74], and Xbee [42; 70]. Concurrent transfer can achieve higher throughput [49; 82], accelerate transmission [49; 82], reduce packet loss [49; 82], and save energy [39]. Choco protocol [39] and 6 LoWPAN [74] can save energy. In addition, [PERSON] et al. [84] proposed the \"MWAC model\" for sensor networks to save power and transmit information over long distances (p. 49).  
Usually, we convert information between different protocols using different systems and intermediate devices. For instance, [PERSON] et al. [50] and [PERSON] et al. [83] both transmitted the data for sensors to the server via TCP/IP while transmitting the data from the server to user applications through HTTP. However, [PERSON] et al. [61] built the servers to connect sensors and human clients based on the HTTP protocol without the use of other protocols.
## 4 Results and Discussion
### Dataset Description  
In order to evaluate and substantiate the effectiveness of the presented AMC approach, two datasets generated with GNU radio [7] are used to evaluate the modulation classification task. The datasets are designated as RML2016.10A and RML2016.04C, respectively. Their parameter list is shown in Table 1. Additionally, the characteristics of pulse shape, modulation, and carried data are made similar. The data are created randomly to match the data from the actual system. There are two data sources that comprise the RML2016a dataset. The publicly accessible Serial Episode No. 1 is utilized as an input for the analog modulations, along with other continuous sources such as voice. The full Gutenberg edition of [PERSON]'s plays is used for digital communication. The block normalizer is used for data whitening in order to make the digitally modulated data equiprobable. Then, to obtain the effect of unknown scaling and translation, the synthetic signals are routed across a number of channels. The final dataset is produced by the GNU radio channel model  
Figure 5: t-SNE of the input space vs. the latent feature space.  
unit, which divides the time series signals into the datasets for training and testing using a rectangular window of 128 samples. Each sample in these data collection possesses two data channels (I/Q) of raw data with a size of \(2\times 128\), consisting of real and imaginary values. This dataset includes 11 digitally modulated signals: 8 digital and 3 analog modulation, which are all frequently employed in wireless communication networks. These include BPSK, 8 PSK, CPFSK,BFSK, 16 QAM, 64 QAM, and PAM4 for digital modulations and AM-DSB, WB-FM, and AM-SSB for analog modulations. The signal-to-noise ratio (SNR) varies from \(-18\) dB to \(20\) dB, with an overlap of 2 dB. All the SNRs in this range are contained in the train-and-test label.
## 2 Related Works  
Several mechanisms for SD and RD have been proposed in the literature, from both academic and industrial worlds. The Service Location Protocol (SLP) [8; 9] and the Universal Plug and Play (UPnP) [10] protocol refer to discovery and auto-configuration of existing services in Local Area Networks (LANs). Regardless of their features, neither are suitable to constrained IoT devices, due to high computation requirements, energy consumption, and reduced node lifetime.  
In the area of SD and RD mechanisms for IoT systems, a few approaches have been proposed. In [11], [PERSON] et al. propose a protocol, denoted as NanoSD, suitable for mobile, highly dynamic and large-scale Wireless Sensor Networks (WSNs). It is based on multicast and broadcast messaging schemes to keep the list of resources and services available at neighboring nodes updated. In [12], an adaptive and context-aware RD/SD protocol, denoted as TRENDY and based on CoAP RESTful Web Services, is proposed. TRENDY splits a network into groups based on location tags, hierarchically organizing them, and assigning each node a specific role inside its own group. Although TRENDY leads to a scalable architecture, in which discovery is performed with a registration on a specific register entity, it suffers from a single point of failure, because of the central role of this register entity. In [13], a hybrid RD/SD mechanism for local and large-scale IoT scenarios is proposed, adopting a Peer-to-Peer (P2P)-based architecture for large-scale and geo-distributed scenarios, and the ZeroConf protocol [14] for local 6 LoWPAN scenarios. The latter protocol reuses the Domain Name System (DNS) message-based semantics, denoted as DNS-SD [15; 16; 17], over IP multicast, to provide name resolution and service discovery/advertisement. In this way, while in single-hop LLNs broadcasting can be used, in multi-hop networks multicast can be implemented using the Multicast Protocol for LLNs (MPL) [18]. It is also possible to use RPL-based techniques, such as Stateless Multicast RPL Forwarding (SMRF) [19] or Lightweight Multicast Forwarding Protocol (LMFP) [20].  
Owing to the characteristics of CoAP, other SD and RD mechanisms have been proposed. An example is represented by IoTivity [21], an open-source interoperability framework running over various types of radio connectivity (e.g., IEEE 802.3, Bluetooth, BLE, ZigBee, IEEE 802.15.4, Z-Wave, NFC, etc.) and using the IP/UDP/CoAP stack. Issuing a multicast CoAP request for a specific resource type, a CoAP client will receive a response only from the nodes that host these resources.
## 3 Results and Discussion
### Performance Decay Prediction of the DEA Mode
#### 3.1.1 Comparison of the ANN and M-ANN
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Mean/Maximum Relative Error, \%**} \\ \cline{2-4}  & **Train** & **Validation** & **Test** \\ \hline one-hidden-layer ANN (ReLU) & 0.405/2.037 & 0.625/1.122 & 0.725/1.563 \\ one-hidden-layer M-ANN (ReLU) & 0.119/1.632 & 0.180/0.759 & 0.198/0.803 \\ two-hidden-layer ANN (ReLU) & 0.186/1.381 & 0.291/0.665 & 0.341/0.932 \\ two-hidden-layer M-ANN (ReLU) & 0.094/1.010 & 0.146/0.484 & 0.158/0.534 \\ MPR-MPR & 0.228/2.210 & 0.264/1.263 & 0.348/1.510 \\ two-hidden-layer M-ANN (sigmoid) & 0.074/0.822 & 0.122/0.444 & 0.157/0.617 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Prediction performance (mean and maximum relative error) of different modeling methods in the train, validation and test set for the DEA mode. Figure 5: Two-hidden-layer M-ANN with ReLU activation function. Comparison of predictive values and true values of current density change evolution for the PEM fuel cell with the DEA mode. (**a–l**) represent the 12 transient processes. (**a–h**), (**i,j**) and (**k,l**) are the train set, validation set, and test set, respectively. Therefore, when the amount of data is small, it is a wise choice to use the MPR method to predict results. 2. The ANN solves the problem of predicting current density change. After eliminating the influence of the initial point, the current density changes under various operating conditions are similar. However, this change is relatively complex, not only rise or fall, but also the magnitude and time of rise and fall are uncertain. Therefore, with a large number of samples, a relatively complex ANN is more effective in learning the current density change. Figure 6 shows the result of MPR method predicting current density change. MPR-MPR means to use MPR to predict the initial current density and then MPR to predict the current density change. Compared with Figures 5 and 6, ANN is better at predicting the current density change than MPR. Overall, this study decomposes the predictive degradation process into two different types of problems, and then gives the two problems to two algorithms to solve, which achieve the complementary advantages between algorithms, resulting in better performance than a single algorithm to solve a single problem.
## 3 Methodology
### ML Models  
In order to refine the performance of the user authentication technique, three different ML algorithms--Gradient Boosting Machines (GBM), Random Forest (RF), and K-Nearest Neighbors (KNN)--have been employed. Each on these three models have been trained with and without incorporating the TOPSIS methodology.  
1. Gradient Boosting Machines (GBM): GBM iteratively builds decision trees to minimize prediction errors, aiming to improve the model's predictive accuracy. The prediction at each iteration is represented in Equation (8): \[F_{m}(x)=F_{m-1}(x)+\eta\cdot h_{m}(x)\] (8) where \(F_{m}(x)\) is the updated model, \(F_{m-1}(x)\) is the previous model, \(\eta\) is the learning rate, and \(h_{m}(x)\) is the weak learner [48].
2. Random Forest (RF): RF constructs multiple decision trees during training and combines their outputs--through majority voting for classification or averaging for regression--with the objective of enhancing predictive performance. The prediction function is given in Equation (9) below: \[f(x)=\frac{1}{N}\sum_{i=1}^{N}T_{i}(x)\] (9) where \(N\) is the number of trees, and \(T_{i}(x)\) represents individual tree predictions [49].
3. K-Nearest Neighbors (KNN): KNN aims to classify data points by determining the majority label of their \(k\)-nearest neighbors, using a distance metric such as the Euclidean distance [50], as shown in Equation (10): \[d(x,y)=\sqrt{\sum_{i=1}^{n}(x_{i}-y_{i})^{2}}\] (10)
## 1 Introduction
Our research showed that irradiance and spectral non-uniformity on the focal plane lead to the loss of spectral mismatch and reduce the photoelectric conversion efficiency of multijunction cells [24]. Moreover, due to the coupling between electricity and heat, the decrease in photoelectric conversion efficiency causes the temperature of the cell to increase, which further reduces the output performance. The electrical-thermal coupling effect is particularly prominent in non-uniform illumination. In the outdoor operating environment, the output performance of solar cells is different from those under IEC standard due to variable irradiance, temperature and other factors [27]. Especially, in the field of high-concentration photovoltaics, non-uniformity of irradiation, spectral mismatch and increase in temperature are hot issues and a lot of research already exists about their effects on cell efficiency alone as mentioned above. The novelty of this paper is to combine these factors and consider their coupled effect on solar cells. Optimizing the placement of the solar cell is proposed to improve the output performance according to the results of coupling effect. The reasons for the novelty of this paper are in real power generation, non-uniform concentration and the change in temperature often occur at the same time and affect each other, which causes further change in the output of solar cells. When the cell is placed at the design focal plane, the efficiency of the cell will reduce due to non-uniform concentration. The lost electrical energy converts into internal energy to raise the cell temperature, which further reduces the output performance. The performance of the cell is affected by the electrothermal coupling produced by the non-uniform concentration and is not equal to the value in the standard testing condition. In order to study the influence of optical-electrical-thermal coupling in the actual operation of the HCPV cells, we established an optical-electrical-thermal coupling model. The non-uniform irradiance characteristics under the concentration of the Fresnel lens are simulated and spectral distribution characteristics along the optical axis position will be calculated to explore their influence on the mismatch among sub cells. Based on ANSYS, this paper depicts the cell temperature variation along the optical axis and analyzes the coupling effect on the cell output performance. The outdoor measurement was carried out to verify the coupling effect.
# Two-Dimensional Topology Optimization of Headtube in Electric Scooter Considering Multiple Loads
###### Abstract  
The safety and structural integrity of electric scooters have gained considerable attention owing to their increasing use in personal mobility and the associated accident rates. This study applied topology optimization to the headtube of an electric scooter, which is a critical component susceptible to structural failure under diverse load conditions. A finite element model was developed to simulate the behavior of the headtube under 16 single-load conditions, followed by optimization for multiple loads. The optimized design demonstrated an enhanced material distribution, balancing the lightweight construction and structural robustness. Collision performance analysis revealed an improvement of at least 51% in the strain energy distribution compared with that of existing commercial models. These results underscore the potential efficacy of topology optimization in enhancing the safety and reliability of personal mobility devices. Moreover, topology optimization can provide a systematic framework for designing critical structural components.  
Keywords:electric scooter; finite element analysis; multiple loads; optimality criteria method; topology optimization +
Footnote †: journal: Applied sciences
## 2 Overview of the SOTM System
### Directionality of the Array Antenna Beam  
The array antenna contains multiple elements, which are generally uniformly arranged. Figure 3 shows a schematic diagram of an N-element linear array.  
In Figure 3, multiple linear array elements are uniformly arranged along the \(y\)-axis. Assuming the elements' quantity is \(N\) and the distance between two elements is \(d\), as the array factor is rotationally symmetric with respect to the \(y\)-axis, we only need to research the array factor in any plane passing through the \(y\)-axis. For example, by studying the plane with \(\varphi=90^{\circ}\), which is referred to as the \(\Gamma\) plane, we can obtain the array factor of the \(\Gamma\) plane.  
\[S=\sum_{n=1}^{N}I_{n}e^{j(nkd\sin\theta-\varphi_{n})}, \tag{1}\]  
where \(I_{n}e^{-j\varphi_{n}}\) is the excitation current of each element.  
Figure 3: Multi-linear array elements.  
Figure 2: The servo control system of the SOTM.
# Traffic-Predictive Routing Strategy for Satellite Networks
## 5 Route Maintenance
### Route Repair Method  
When a link breaks, routing needs to be repaired to ensure continued smooth communication. After the link is broken, the broken node obtains \(hopS\) and \(hopD\) from the routing table. \(hopS\) is the current hop count value, that is, the number of hops that the current data packet has experienced from the source node to the break node. \(hopD\) is the number of hops required from the broken node to the destination node. By comparing \(hopS\) and \(hopD\), select the corresponding route repair method. Route repair methods are divided into source node repair and local repair.  
(1) Source node repair  
As shown in Figure 2, when \(hopS<hopD\), the source node repair is used for route repair at the fracture point. At this point, the fracture location is relatively close to the source node, and using source node repair can quickly establish the optimal path. This repair method can reduce the increased latency caused by complex local repair judgments and reduce routing overhead.  
(2) Local repair  
As shown in Figure 3, when \(hopS\geq hopD\), local repair is used to repair the route at the break. At this time, the fracture location is far away from the source node, so local repair can be used to establish the optimal path faster.
## 2 Understanding WebAssembly
### The Security Model of WebAssembly
* **Control-Flow Integrity** Control-flow integrity is crucial for measuring security effectiveness. Wasm provides implicit guarantees for direct function calls through function section indexes and for returns through the protected call stack. Indirect function calls undergo runtime type checks to ensure coarse-grained control-flow integrity. Fine-grained control-flow integrity can be achieved through the use of the Clang/low level virtual machine (LLVM) compiler infrastructure, which offers built-in support for Wasm. * **Clang/LLVM CFI** Enabling fine-grained control-flow integrity (CFI) using the Clang/LLVM compiler has several advantages. It provides enhanced defense against code reuse attacks involving indirect function calls, and performs function signature checks at the C/C++ type level. Although enabling this feature incurs a slight performance cost for each indirect call, future updates will optimize it by leveraging built-in support for multiple indirect tables. * **Code Validation and Verification** Wasm implements a two-step validation process to ensure the safety of its modules. The first step involves structural validation to check whether the module follows the binary format specification. The second step includes control flow and type checking to ensure that the code is well-structured and does not perform illegal operations. * **Safe Interoperability with JavaScript** Considering that Wasm is designed to work alongside JavaScript in the same web environment, it is crucial to ensure safe interoperability between the two. Wasm modules can interact with JavaScript through a set of explicit exports and imports, allowing for controlled interaction between the two. By strictly defining these interfaces, Wasm ensures that the security of the web platform is preserved.
## 5 Simulation Results and Discussion  
### Experimental Setup  
The FL-based ResVGG-SwinNet framework was tested in a decentralized IoT context with nine clients and one central server for federated learning. For privacy, each client received a unique subset of data from CIC-DDoS2019, UNSW-NB15, and IoT23 datasets, preventing raw data interchange with the central server. The FedAvg technique aggregated local model changes on the central server across 30 communication cycles. Local model training for five epochs every round was followed by server-aggregated and distributed updates. The central server had an Intel Core i9-12900K CPU, NVIDIA RTX 3090 GPU, and 64 GB RAM for processing large datasets and global model aggregation. Simulations of clients with different resource setups reflected network heterogeneity. For example, some clients had 1.0 GHz single-core processors and 512 MB of RAM, as low-resource nodes, while others had 3.2 GHz quad-core processors and 8 GB of RAM, as high-performancenodes. To simulate IoT connectivity, clients have different bandwidth, latency, and packet loss. Transmission speeds ranged from 512 Kbps to 20 Mbps, latency from 20 to 120 ms, and packet loss from 0.5% to 3.0%.  
Divided into five balanced divisions, the datasets enabled simultaneous local training for many clients. Stable convergence was achieved during training using a learning rate of 0.001, dropout rate of 0.3, and batch size of 64. The use of adaptive preprocessing methods (AFN, CTA, and MTB) improved data quality and class balance. This experimental setting created a realistic resource and network heterogeneity environment to test the framework's scalability and performance. Table 3 lists the hardware and network specifications of simulated clients. This experimental setup established a realistic environment with both resource and network heterogeneity, providing a comprehensive basis for evaluating the scalability and performance of the proposed framework [40].  
This setup allowed realistic testing of the framework's speed and scalability. Simulation findings and performance analysis are discussed subsequently.