esa-sceva/satcom-chunk-collection huggingface dataset:
# An Automatic Geometric Registration Method for Multi Temporal 3D Models
## 3 Methodology
### Data Processing  
After acquiring multi-temporal UAV images and position and orientation system (POS) data, the software of \"Context Capture V10.16.0.75\" was used for 3D reconstruction to obtain 3D models. Then the feature points of the 3D model were extracted and their corresponding spatial positions were calculated based on the method in Section 3.1. Thirdly, the spatial-feature points were initially matched to obtain the initial correspondence based on the method in Section 3.2. Fourthly, the model transformation parameters from the registration model to the base 3D model were estimated based on the initial correspondence, and all vertices of the registered 3D model were updated based on the method in Section 3.3. The workflow of the data processing is shown in Figure 6.
## 3 Correlation Analysis Between Macroscopic Mechanics and Microscopic Pore Characteristics
### Porosity Analysis of Three-Dimensional Images Based on SEM  
This study utilizes the three-dimensional visualization methods and analysis modules of ArcGIS to process and analyze the SEM microstructural images of soil samples (\(RD=0.29\)), as shown in Figure 2. The reconstructed three-dimensional visualization images of the microstructure are displayed in Figure 3.  
Figure 1: Stress–strain relationship curves under different disturbance degrees. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Vibration Time/min**} & \multicolumn{3}{c}{**Vibration Frequency/Hz**} \\ \cline{2-4}  & **20** & **35** & **50** \\ \hline
30 & 0.22 & 0.31 & 0.42 \\
60 & 0.28 & 0.39 & 0.52 \\
90 & 0.28 & 0.43 & 0.58 \\
120 & 0.29 & 0.47 & 0.61 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Disturbance degree (\(RD\)) [27]. Owing to the overlapping and adhesive characteristics of soil particles, the maximum set height for these particles was capped at 255. A height threshold range of 0-255 was established, where any value above 1 indicates the height of soil particles. In SEM images, particles closer to the light source exhibit higher pixel grayscale values, appearing brighter, whereas those further away appear darker. In three-dimensional space, it is possible to conceptualize a rectangular prism derived from a two-dimensional image by using the maximum grayscale value to define its height. Within this prism, each pixel is represented as a distinct volume, with its area forming the base and its grayscale value determining the height. The aggregate of these individual pixel volumes constitutes the total volume of the particles, while the unoccupied volume within the prism represents the total pore volume [31; 32]. Utilizing this methodology, the volumes of both particles and pores are determined. The porosity results derived from the three-dimensional model are presented in Table 4.  
Figure 3: Three-dimensional porosity calculation model of structural clay (\(RD=0.29\)). Figure 2: SEM image of structural clay (\(RD=0.29\)). Table 4 illustrates that disturbances lead to an increase in the number of pores and porosity in the sample. Specifically, at a relative disturbance degree (\(RD\)) value of 0.22, porosity escalates by 0.78% to 66.94%. At an \(RD\) value of 0.61, the increase is more pronounced, with porosity rising by 13.35% to 79.51%. The data show a consistent trend of increasing porosity with elevated disturbance degree. Furthermore, based on Table 4, a linear regression analysis of \(RD\) as a function of porosity (\(m\)) reveals a strong correlation (\(R^{2}=0.922\)), as shown in Figure 4. The derived linear relationship is as follows:  
\[m=0.662+0.289\times RD. \tag{2}\]  
Figure 4 clearly shows that the porosity in the disturbed samples increases linearly as the disturbance degree intensifies. This trend can be attributed to the flocculated structure of the clay, characterized by larger pores that are highly sensitive to disturbances.
## 3 Results
### One-Way (Monologic) Tools  
The results regarding the interactivity exhibited by different social organizations on their websites, considering monological resources and thus unidirectional communication, are quite revealing (see Figure 1). In this regard, it can be observed that most social interest groups have very high interactivity, specifically, 83.33% (\(n=25\)), falling within the range of 2.1 to 3, which represents the maximum level established on the scale. On the other hand, 16.67% (\(n=5\)) are within the range of 1.1 to 2 in terms of interactivity, indicating a medium level of interactivity corresponding to organizations such as ActionAid International, Wikimedia Foundation, the Acumen Fund, Plan International, Ashoka, and Anti-Slavery International. It is noteworthy that none of the NGOs have low interactivity, with all of them positioned at 1.1 or higher on the established interactivity scale regarding one-way tool.  
Another aspect sought to be understood was which tools for unidirectional interaction with website visitors are most used by non-governmental organizations on their websites. Upon observing Figure 2, one can see that there are essential tools on the websites of these organizations concerning information dissemination: informational brochures or presentations about the NGO, published news, photographs and images, and participatory resources such as the social media follow buttons seen earlier. All the studied NGOs use these tools on their websites.  
Figure 1: Levels of interactivity for one-way communication tools on NGO websites. Source: own elaboration.  
Ninety percent (\(n=27\)) also publish studies, reports, articles, or their own magazines on topics related to the issues they address as social organizations, while 86.77% (\(n=26\)) use multimedia resources through audio and/or videos to create richer content. Interactive resources like graphics or infographics are also widely used by NGOs (80%, \(n=24\)), in addition to the presentation of annual reports (83.33%, \(n=25\)) to promote transparency and make annual performance publicly accountable.  
To a lesser extent, the NGOs integrate links on their websites, connecting them to other institutions in the same field or other websites (60%, \(n=18\)). Finally, not many social organizations have press rooms where they include press releases and statements for the media and interested audiences, with only 40% (\(n=12\)) having this feature. Likewise, the publication of an events agenda or the creation of a blog without user response capability are tools used less frequently by the analyzed NGOs, with corresponding values of 26.67% (\(n=8\)) and 30% (\(n=9\)), respectively.
# Characteristic Analysis and Experiment of Adaptive Fiber Optic Current Sensor Technology
## 4 Effect of Magnetic Field Changes on a Straight-Through Magneto-Optical Glass Current Sensor  
A straight-through magneto-optical glass current sensor can solve the temperature drift problem due to the use of diamagnetic material. If a diamagnetic material is selected, it is no longer necessary to consider the influence of temperature on the Verdet constant, resulting in stable temperature performance. This kind of current sensor has a small volume and a short optical path, and it is difficult to satisfy the closed loop integral in [PERSON]'s circuital law. Therefore, a sudden non-uniform magnetic field will create interference in a magneto-optical glass current sensor.  
[PERSON]'s effect and the Biot-Savart law can be used to derive the rotation angle through magneto-optic glass:  
\[\theta_{2}=V_{G}\int_{0}^{L}\frac{i}{2\pi r}\cdot rd\varphi=\frac{V_{G}i}{\pi} \mathrm{arctan}\bigg{(}\frac{L}{2h}\bigg{)} \tag{13}\]  
where \(V_{G}\) is the Verdet constant of the magneto-optical glass, \(L\) is the length of magneto-optical glass, and \(h\) is the distance between the current wire and magneto-optical glass.  
Magneto-optical glass is in an open optical circuit, in contrast with the closed circle structure of the sensing fiber. The magneto-optical signal is affected by internal magnetic field changes caused by current changes and external magnetic field interference. When the internal or external magnetic field changes, the rotation angle will change to:  
\[\theta_{2}^{\prime}=\theta_{2}+VL\Delta H=\theta_{2}+\Delta\theta \tag{14}\]  
where \(\Delta H\) is the magnetic field changes.  
Figure 4: Effects of the temperature fluctuations on the relationships between \(\theta\) and \(L\) in the optical fiber.
# Research on Temperature Compensation Method in Crankshaft Online Measurement System
## 3 Compensation Method for LVDT Based on IDA-BP Neural Network  
### BP Neural Network  
BP neural network has the characteristics of strong nonlinear mapping ability, self-adaptation and self-learning ability, strong fault tolerance, etc. It is one of the most widely used in temperature compensation.  
The basic structure of the BP neural network is the input layer, hidden layer, and output layer. Input layer accepts the training data set; hidden layer maps the input data to the output layer through connection weights and activation functions; output layer linear-combine the outputs of hidden layer to computes the predicted result. Figure 5 is a representation diagram of BP neural network structure. In Figure 5, \(I_{1}\), \(I_{2}\),..., \(I_{n}\) are the input values of the BP neural network, \(w_{\text{pk}}\) and \(w_{\text{kq}}\) are the weight relationships of the BP neural network, and \(O_{1}\), \(O_{2}\),..., \(O_{m}\) are the expected output values of the BP neural network.  
Figure 5: Structure of BP neural network.  
The BP neural network designed in the article has an input layer with three nodes, a hidden layer with 7 nodes, and an output layer with one node. The input neurons of the input layer are displacement measured by the displacement sensor, the temperature measured by the temperature sensor and the output voltage of the temperature sensor. The output voltage of the displacement sensor is used as the output neuron.
# Adaptive Handover Decision Using Fuzzy Logic for 5G Ultra-Dense Networks
## 4 Performance Evaluation
### Results and Discussions  
The performance of FLDHDT is compared with Event A3 and FLDH [14], and Table 6 summarizes the differences among the three schemes. All results are averages obtained over 10 simulation runs. The performance measures are the number of handovers, overall system throughput, and ping-pong ratio.  
Figure 11 shows a plot of the number of handovers versus the number of gNBs with the Event A3, FLDH, and FLDHDT schemes. Among the three schemes, Event A3 had the largest number of handovers because, in making the handover decision, the Event A3 scheme considers only the signal strength of the UE. Furthermore, FLDH can dynamically adjust the HOM such that a UE with a higher signal strength has a higher HOM to avoid unnecessary handovers. As for FLDHDT, it had the smallest number of handovers because both HOM and TTT are dynamically adjusted based on the signal strength and horizontal speed of the UE. Moreover, it is apparent that the number of handovers for any of the three schemes increases with the number of gNBs due to the increase in small BS density.  
A ping-pong handover is defined as occurring when a device changes connection from BS A to BS B and switches back to BS A within 1 s. The ping-pong ratio, denoted \(R_{pp}\), is the number of ping-pong handovers divided by the total number of handovers, as expressed in Equation (4). According to Figure 12, the ping-pong ratio invariably increases as the density of small BSs increases, but FLDHDT improves \(Rpp\) compared to the two other schemes, especially in dense UDNs. However, dynamic HOM causes a more significant improvement than dynamic TTT, and the difference between the ping-pong ratios of FLDHDT and FLDH  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Scheme** & **Event A3** & **FLDH** & **FLDHDT** \\ \hline Using fuzzy logic & No & Yes & Yes \\ HOM & fixed & dynamic & dynamic \\ TTT & fixed & fixed & dynamic \\ \hline \hline \end{tabular}
\end{table}
Table 6: Differences among event A3, FLDH, and FLDHDT.  
Figure 11: Number of handovers versus number of gNBs.  
for 15 gNBs is marginally smaller than for 10 gNBs. This is because HOM is essentially the most important parameter when making handover decision.  
\[R_{pp}=\frac{\text{no. of ping}-\text{pong handovers}}{\text{total no. of handovers}}\times 100\% \tag{4}\]  
As depicted in Figure 13, the overall system throughput is improved by FLDH or FLDHDT for dense UDNs because these schemes reduce the number of handovers by using dynamic HOM or TTT. Notably, shortening the TTT can reduce the interruption gap due to hard handover and alleviate packet loss to improve the throughput of each UE. Therefore, the higher the density of small BSs is, the more substantial is the improvement due to FLDHDT compared to Event A3 and FLDH.  
Figure 12: Ping-pong ratio versus number of gNBs.  
Figure 13: System throughput versus number of gNBs.
# Resource Management in Converged Optical and Millimeter Wave Radio Networks: A Review
## 5 Delay Minimization and Resource Allocation Algorithms
### Selected Papers
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref.** & **Optimization Algorithm** & **Advantages** & **Limitations** & **Performance Metrics** \\ \hline \multirow{4}{*}{[67]} & a distributed iterative & improves average & \multirow{2}{*}{fairness is not considered for power consumption} & delay, energy \\  & algorithm and the & & respecting the delay & & for power consumption \\  & Lagrange multipliers & thresholds & & & \\ \hline \multirow{4}{*}{[68]} & a priority based resource & \multirow{2}{*}{reduces the delay of the high QoS class traffic} & the algorithm is not & \multirow{2}{*}{delay, throughput} \\  & & & & \\  & created & & & \\ \hline \multirow{4}{*}{[69]} & two-phase fronthaul and access delay minimization & \multirow{2}{*}{fronthaul link and access transmission delays} & reference simulation & \multirow{2}{*}{transmit} \\  & with two separate iterative & & & \\  & algorithms & & & \\  & & & & \\ \hline \multirow{4}{*}{[70]} & algorithm based on & \multirow{2}{*}{minimizes the maximum system delay of a multi-user mmWave MEC system} & complexity increases & \multirow{2}{*}{maximum delay} \\  & & & & \\  & decomposition framework & & & \\  & & & & \\ \hline \multirow{4}{*}{[71]} & user-driven offloading & \multirow{2}{*}{provides infrastructure sharing while reducing latency and increasing energy efficiency} & test-bed results may differ & \multirow{2}{*}{response time, energy consumption between users and APs} \\  & & & & \\  & & & & \\ \hline \multirow{4}{*}{[72]} & users and MEC servers & \multirow{2}{*}{improves average response time when compared to MEC- and cloud-only solutions} & offloading probability of & \multirow{2}{*}{average response time} \\  & & & & \\  & & & & \\ \hline \multirow{4}{*}{[72]} & ANN for decentralized & \multirow{2}{*}{around lens end-to-end delay can be achieved for Tactile Internet} & \multirow{2}{*}{networks with arbitrary delays are not considered} & \multirow{2}{*}{end-to-end delay} \\  & bandwidth allocation & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Analysis of resource allocation papers with delay minimization objective. between the system components. Another work that investigates the relation between delay and the use of beamforming is [69], in which a two-phase fronthaul and access transmission delay minimization method is proposed.
# Initial Positioning Assessment of BDS New Satellites and New Signals
## Abbreviations  
\begin{tabular}{l l}
3D & Three dimensions \\ ADOP & Ambiguity dilution of precision \\ APC & Antenna phase center \\ AR & Ambiguity resolution \\ BDS & BeiDou navigation satellite system \\ CAS & Chinese Academy of Science \\ CoM & Center of mass \\ DCB & Differential code bias \\ DOP & Dilution of precision \\ DOY & Day of year \\ GEO & Geosynchronous Earth Orbit \\ IGMAS & International GNSS Monitoring and Assessment System \\ IGS & International GNSS service \\ IGSO & Inclined Geosynchronous Orbit \\ ISL & Inter-satellite link \\ MEO & Medium Earth Orbit \\ OMC & Observation minus corrections \\ PCO & Phase center offset \\ PDOP & Position dilution of precision \\ POD & Precise orbit determination \\ SISRE & Signal in space range error \\ SPP & Single point positioning \\ PHM & Passive hydrogen maser \\ PNT & Positioning, navigation and timing \\ PPP & Precise point positioning \\ RTK & Real-time kinematic \\ TARC-CSNO & Test and Assessment Research Center of China Satellite Navigation Office \\ TGD & Timing group delay \\ UERE & User equivalent range error \\ \end{tabular}
# An Improved Federated Learning-Assisted Data Aggregation Scheme for Smart Grids
## 3 System Model and Design Goals
### Design Goals  
* Privacy: This involves protecting the privacy and security of data transmitted from smart devices to edge nodes, ensuring that the data remain local throughout the entire learning process. Only encrypted model parameters are directly transmitted between the \(u_{i}\) and \(CS\). The data are protected by secure aggregation algorithms during transmission, making them resistant to third-party attacks, including interception or the inference of results. Each edge node is only aware of its own transmitted data, and during interactions with other nodes and the cloud server, only obtains fragmentary values processed by secret sharing algorithms. The cloud server serves the purpose of aggregating data and training the global model, but it cannot obtain the original parameters and training data of the nodes through the acquired parameters, and it cannot infer the data of other nodes through collusion with any single node.
* Identity authentication: Ensures that attackers cannot launch attacks on the system by forging identities. During the learning process, if attackers successfully launch attacks by using forged identities or data, it may result in incorrect aggregation results or even the leakage of original data. The utilization of zero-knowledge proof in this framework enables the cloud server to authenticate the identities of communication nodes. This feature allows for the detection of attackers who attempt to forge identities and it safeguards the data from tampering, thereby enhancing the security of the learning process.
* Fault tolerance: Ensures that the failure of any edge node during the FL does not affect the correctness of the aggregation results.
* Verifiability: Once the aggregation results are calculated, it is necessary for the \(CS\) to transmit the results back to the edge nodes that participated. Throughout this process, the edge nodes have the ability to verify the accuracy of the returned results and reject any results that cannot be verified.
## 6 Simulation Study  
In this section, we evaluate the performance of proposed algorithms for offloading tasks through simulations. First, we evaluate the utilization rate of physical machine and delay for 200 offloaded tasks by using three different methods, TLRL algorithm, DRL algorithm and random algorithm. Moreover, we compare our proposed TLRL algorithm with DRL algorithm and \(Q\)-learning for offloading tasks, observe convergence times and discounted cumulative reward. Finally, we show the validity of trading off between utilization rate of physical machine and delay for task offloading by using TLRL algorithm and adjust the weight factor \(\beta\).  
In this simulation, we set the number of PMs in the remote cloud is 100. According to Section 5, we know that the dimension of state in this simulation is 200 that is a high dimensional state space problem for RL. The bandwidths between these PMs and mobile devices are generated randomly among the interval (500 kbps, 10 Mbps). We suppose all PMs have almost the same hardware configuration. Each PMs could run three VMs at most for executing offloaded tasks. We consider multiple devices in our paper where different devices may contain the same tasks. Therefore, our proposed algorithm makes a decision according to the difference of tasks. In our experiments, we take 6 different tasks and the corresponding parameters are displayed in Table 1. We set \(\alpha=0.3\), \(\varepsilon\)-greedy strategy \(\varepsilon=0.6\) and reward discount \(\gamma=0.8\) for our proposed algorithms.  
In order to evaluate the TLRL algorithm, we divide the 100 PMs into 20 clusters using k-NN algorithm where the k is equal to 20. Therefore, the dimension of state belonging to RL of TLRL is decreased to 20 \(\times\) 2 and the number of corresponding actions is 20 according to Section 5. Comparing with DRL algorithm, both the dimension of state and the number of actions have a significant reduction.  
Before these experiments, we construct two full-connected CNNs with an input layer, an output layer, and two hidden layers. The details of the two full-connected CNNs are displayed in the Table 2.  
According to the Table 2, we can see that our proposed TLRL contains less number of input and output compared with DRL by classifying the PMs into 20 clusters via k-NN algorithm. Correspondingly, the hidden layers also contain less number of neurons than DRL.
## 2 Methods
### Integrated Task and Path Planning  
Figure 2 shows the overall workflow of the proposed approach. The tasks are first organized into a queue by layer according to their precedence constraints, as shown in Figure 1. Then, tasks are selected from the built task queue in increasing order of precedence layers, i.e., tasks from the base layer are extracted first. For each selected task \(i\), process planning is first performed by traversing all available machining centers. For each possible machining center \(k\), its position is set as the goal position of task \(i\). The energy consumed by the machining center \(k\) and the machining time can be obtained from the initial settings. Then, CA* is used to generate a reasonable path for robot \(j\) ensuring that no collision occurs with other planned paths.  
The time consumption and energy consumption for task processing and transportation are taken as the total consumption \(TC\) and stored in the Assignment Heap \(H\) in increasing order (i.e., the assignment with least total consumption is at the top of the heap). The Assignment Heap \(H\) contains all potential assignments of task \(i\) to each available robot and machining center when \(type_{i}=0\). The greedy algorithm is then used to select the optimal task assignment from \(H\) and the loop cycle continues until all tasks are successfully assigned. To reduce unnecessary waiting time for machining centers in the task assignment process, we propose the Looking-backward Search Strategy (LSS) and Regret-based Search Strategy (RSS), which are explained in the following subsections.