esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Climbing Scaffold Status Monitoring Method
### GNSS/BeiDou Positioning Solution Model with Height Constraint for Climbing Scaffolds
#### 2.1.2 Filtering Algorithm
\(\mathbf{P}_{k+1|k}\) is the predicted state covariance matrix at time \(k+1\), \(\mathbf{P}_{k|k}\) is the state covariance matrix at time \(k\), and \(\mathbf{Q}_{d}\) is the process noise covariance matrix. Update: In the update step of the Kalman filter, the predicted state is corrected using new measurements to obtain a more accurate state estimate. The process involves the following steps:  
\[\mathbf{K}_{k+1}=\mathbf{P}_{k+1|k}\mathbf{H}^{\intercal}\big{[}\mathbf{H}\mathbf{P}_{k+1|k}\mathbf{H} ^{\intercal}+\mathbf{R}_{d}\big{]}^{-1}, \tag{15}\]  
\[\hat{\mathbf{x}}_{k+1|k+1}=\hat{\mathbf{x}}_{k+1|k}+\mathbf{K}_{k+1}[z_{k+1}-\mathbf{H}\hat{ \mathbf{x}}_{k+1|k}], \tag{16}\]  
\[\mathbf{P}_{k+1|k+1}=[\mathbf{I}-\mathbf{K}_{k+1}\mathbf{H}]\mathbf{P}_{k+1|k}, \tag{17}\]  
where \(\mathbf{K}_{k+1}\) is the Kalman gain, \(\mathbf{H}\) is the measurement matrix, \(\mathbf{R}_{d}\) is the measurement noise covariance matrix. \(\hat{\mathbf{x}}_{k+1|k+1}\) is the updated state estimate, \(z_{k+1}\) is the actual measurement, \(\mathbf{H}\hat{\mathbf{x}}_{k+1|k}\) is the predicted measurement, and \(\mathbf{I}\) is the identity matrix. (3) Equivalent Weight Function  
In robust Kalman filtering, the key aspect of data processing lies in the selection of an appropriate equivalent weight function. Different equivalent weight functions result in different equivalent observation covariance matrices, ultimately leading to varying filtering outcomes. Commonly used equivalent weight functions in practical applications include the Huber, IGG1, and IGG3 functions. In this study, the IGG3 equivalent weight function is adopted for robust estimation. The IGG3 weight function is an improved approach based on the bounded nature of measurement errors. It extends the two-segment division of IGG1 by introducing an additional segment, classifying the data into three distinct regions: the normal region, the suspicious region, and the elimination region. In the normal region, observations are retained with their original weights, without any modification. In the suspicious region, the weights of the observations are reduced to mitigate the impact of potential gross errors. In the elimination region, observations with excessively large residuals are completely discarded, with their weights set to zero. This weighting strategy effectively suppresses the influence of gross errors, making it particularly effective in scenarios where a significant number of gross errors exist in the observation data. It is expressed as follows:  
\[\overline{P}_{i}=\begin{cases}P_{i}&|v|\leqslant k_{0}\\ P_{i}\frac{k_{0}}{|v|}(\frac{k_{
# Weighted Matrix Factorization Recommendation Model Incorporating Social Trust
## 5 Experimental Evaluation
### Evaluation Metrics  
In the experiments, two classic evaluation metrics were used: Mean Absolute Error (MAE) and Root-Mean-Square Error (RMSE). These metrics measure the accuracy of the recommendation results by calculating the error between the user's actual ratings and the predicted ratings. The smaller their values, the more precise the recommendation results are.  
\[MAE=\frac{\sum_{(u,i)\in R}\left|r_{ui}-\hat{r}_{ui}\right|}{\left|R\right|} \tag{21}\]  
\[RMSE=\sqrt{\frac{\sum_{(u,i)\in R}\left|r_{ui}-\hat{r}_{ui}\right|^{2}}{\left| R\right|}} \tag{22}\]  
where \(R\) represents the set of rating data in the test set, \(r_{ui}\) is the actual rating given by user \(u\) to item \(i\), and \(r_{ui}\) is the predicted rating for user \(u\) on item \(i\).  
Additionally, in the evaluation experiments of this model, the \(R^{2}\) metric was also used to measure the degree of fit between the model's predicted values and the actual observed values, providing more detailed insights into the performance of the model.  
\[R^{2}=1-\frac{\sum_{(u,i)\in R}(r_{ui}-\hat{r}_{ui})}{\sum_{(u,i)\in R}(r_{ui }-\overline{r})} \tag{23}\]  
where \(\overline{r}\) represents the average value of user ratings.
## 3 Results
### Bending-Performance Analysis  
The bending results of the three profiles are all compared using an equivalent 2 mm bending angle, which is calculated as follows.  
\[\alpha_{2}=\alpha_{1}\cdot\left(\sqrt{\frac{d_{1}}{2}}\right)\]  
Here, \(\alpha_{2}\) is the equivalent 2 mm thick bending angle, \(\alpha_{1}\) is the measured bending angle, and \(d_{1}\) is the material thickness.  
Figure 10 shows the bending macro-morphology of the three Al-Mg-Si alloy thin-walled profiles at different aging states. The bending effect of the 6 xxx-2(1.33 Mg/Si) profile in the near-peak-aged state is the worst, where cracks are visible to the naked eye and have penetrated through the surface of the profile. The three profiles have small cracks in the under-aged state, but the cracks are not connected. The surfaces of profiles in the over-aged state have some deformation texture, but macroscopic cracks cannot be observed with the naked eye.  
Figure 10: Bending macro-morphology of the three Al-Mg-Si alloy thin-walled profiles in different aging states: (**a**–**c**) 175 \({}^{\circ}\)C/10 h, 175 \({}^{\circ}\)C/16 h, 205 \({}^{\circ}\)C/6 h, 6 xxx-1(1.6 Mg/Si) profile; (**d**–**f**) 175 \({}^{\circ}\)C/10 h, 175 \({}^{\circ}\)C/16 h, 205 \({}^{\circ}\)C/6 h, 6 xxx-3(1.14 Mg/Si) profile.  
Table 6 shows the bending properties of the three Al-Mg-Si alloy thin-walled profiles in different aging states, and the equivalent bending angle of the three profiles in the over-aged state is the largest, indicating that profiles in the over-aged state have better toughness. Compared to profiles in the near-peak-aged state, the equivalent bending angles of profile 6 xxx-1(1.6 Mg/Si) in the under-aged and over-aged states are higher by 5.1% and 32.4%, respectively; the equivalent bending angles of profile 6 xxx-2(1.33 Mg/Si) in the under-aged and over-aged states are higher by 31.1% and 53.3%, respectively; and the equivalent bending angles of profile 6 xxx-3(1.14 Mg/Si) in the under-aged and over-aged states are higher by 5.5% and 11.1%, respectively.
# Dynamic Routing in Flying Ad-Hoc Networks Using Topology-Based Routing Protocols
## 2 Dynamic Routing Protocols in FANETs
### Proactive Routing Protocols
#### 2.1.2 Optimized Link State Routing (OLSR)  
Optimized link state routing is well known, and one of the most frequently proposed routing protocols for the FANET system, where routing paths are constantly stored and updated in the routing table [23]. Hence, whenever a path is required for data transmission, the protocol immediately determines the path to all the possible destination UAVs without a long wait time [24]. OLSR uses a unique packet, which comprises more than one message to establish a communication process between the UAVs in a network. This unique packet can carry three different type of messages, each one for a specific task: (i) a HELLO message, which is used to transmit periodically to find the neighboring UAVs; (ii) a topology control (TC) message, which is advertised to maintain topological information; or a (iii) multiple interface declaration (MID) message, which accomplishes the multiple interface declaration on a UAV [25]. Hence, the periodic flooding behavior capability of this specific protocol results in a large control overhead.  
Compared to the other routing protocols (e.g., flooding routing), OSLR uses the multipoint relay (MPR) mechanism to reduce routing overheads and improve the latency of the network. Therefore, effectively selecting the MPR-UAV becomes one of the most important factors that affects the OLSR's performance, because during the flooding process, only the MPR-UAV can forward the data packets. The sender UAV selects a set of MPR-UAVs so that it can span two hop neighbors. A source UAV, which selects another UAV as a member MPR-UAV, is called the MPR selector. Figure 2a shows MPR selection by the source UAV. However, one of the most substantial design parameters that affects delays drastically is the number of MPRs. If the number of MPRs shrink, the signaling overhead will reduce consequently. In this context, a new forwarding mechanism is suggested to decrease the number of member MPRs, as shown in Figure 2b. For each MPR sending data packets, the source UAV first tests the distance to the receiver UAV [26]. If the distance is greater than (Dmax/2), which is the maximum distance that can be attained by utilizing a directional antenna, ODLSR is chosen for data transmission. This also occurs if the destination is beyond the omni-directional approach of. Otherwise, OLSR will usually be used.  
Figure 2: Multipoint relay (MPR) mechanism in OLSR (**a**) MPR UAVs selection; (**b**) ODLSR block diagram.
## 2 Materials and Methods
### Wavelength Calibration  
The wavelength calibration of the EEMS sensor system needs no special adjustments by the user, as both spectrometers used are calibrated by the manufacturers and therefore are supplied with a set of calibration coefficients. For verification we performed a laboratory check with a reference standard for molecular fluorescence spectrophotometry (Compound 610; excitation: 440 nm; emission: 480 nm; nominal concentration of 1 \(\times\) 10\({}^{-6}\) M, Starma Scientific Ltd., Ilford, UK), data not shown. Such a measurement allows the evaluation of the EEMS optical setup over time and corresponding correction methods for the emission wavelengths.
## 3 System Model and Problem Formulation
### Problem Description  
In this section, to achieve the minimum system cost for all the M-IoT devices while satisfying the maximum available energy and tolerable delay constraints for the M-IoT devices, a related association control \(\mathbf{a}=\left\{a_{ij}\right\}\), task allocation \(\mathbf{D}=\left\{D_{i}^{l},D_{i}^{u},D_{i}^{s}\right\}\) transmission power control \(\mathbf{P}^{u}=\left\{P_{ij}^{u}\right\}\), and UAV computational resource allocation \(\mathbf{f}^{u}=\left\{f_{ij}^{u}\right\}\) optimization strategy for computational offloading is formulated.  
\[\begin{array}{ll}\min_{\mathbf{a},\mathbf{D},\mathbf{P}^{u},\mathbf{f}^{u} }&\sum\limits_{l=1}^{T}\sum\limits_{n=1}^{N}\text{Cost}_{i}(t)\\ \text{s.t.}&\text{C1}:T_{i}^{t}\leqslant T_{i},\forall i\in\mathcal{K}\\ &\text{C2}:E_{i}\leqslant E_{i}^{\text{max}},\forall i\in\mathcal{K}\\ &\text{C3}:\sum\limits_{j}a_{ij}f_{ij}^{u}\leqslant F_{j}^{u},\forall j\in \mathcal{N}\\ &\text{C4}:D_{i}^{l}+D_{i}^{u}+D_{i}^{s}=D_{i},\forall i\in\mathcal{K}\\ &\text{C5}:\sum\limits_{j=1}^{N}a_{ij}\leqslant 1,\forall i\in\mathcal{K}\\ &\text{C6}:a_{ij}\in\{0,1\},\forall i\in\mathcal{K},j\in\mathcal{N}\\ &\text{C7}:\sum\limits_{j=1}^{N}a_{ij}P_{ij}^{u}\leqslant P_{i}^{\text{max}}, \forall i\in\mathcal{K}\\ \end{array} \tag{17}\]  
Here, \(E_{i}^{\text{max}}\) is the maximum available energy for the \(i\)-th M-IoT device. The constraints in Equation (17) can be explained as follows: C1 indicates that each computational task should be completed before the tolerable delay; C2 ensures that the total energy consumption of each M-IoT device does not exceed its available energy; C3 limits the maximum CPU clock frequency for each M-IoT device at the UAV; C4 represents the constraint that each M-IoT device can only connect to one UAV; C5 is the association indicator variable for each M-IoT device; and C6 indicates the maximum transmission power for each M-IoT device.  
This optimization problem is a non-convex mixed-integer optimization problem that cannot be solved by traditional methods, as it involves both continuous and discrete variables. Furthermore, the optimal offloading strategy depends not only on each device's own strategy but also on the strategies of the other devices. The action space of the joint strategy increases exponentially with the number of devices and UAVs. Therefore, this section reformulates the problem as a POMDP and then designs a task offloading strategy based on the MADDPG to solve this optimization problem. Compared with DRL-based work, the MADDPG framework enables centralized training with decentralized execution, allowing agents to coordinate strategies despite the partial observational ability of the dynamic maritime environment. It can approach an optimized delay-energy trade-off [33].
## 4 Investigation Results
### Sensitivity Measurements  
The minimum theoretical sensitivity of the WuRx defines the minimum detectable signal. The amplification performance of each architecture is evaluated with signal generator and an input signal with various sensitivity level. Figure 18 shows the voltage after the amplification stage of BJT and TIA board.  
A poor detection mechanism will lead to packet errors, therefore the number of received WuPts versus transmitted packets is examined to study the noise immunity of the WuRx. Furthermore, the RSSI measured by the AS3933 is another value for evaluating and comparing the performance of the two architectures. The amount of transmitted packets \(N\) is calculated using the probability of arrival and the confidence interval and is seen in Equation (15) where \(z\) is the standard score, and \(p\) represents the probability of \(e\) the confidence level.  
\[N=\frac{z^{2}\cdot p(1-p)}{e^{2}}. \tag{15}\]  
At a confidence level of 99%, the standard deviation corresponds to a value of 2.58. The probability was determined by a sample measurement of 1000 packets, in which all packets arrived with a probability of 90%. This leads to a sample size of 6000 packets.  
The relationship between PER and RSSI was investigated with a signal generator. Starting with an input power of \(-35\) dBm, this is reduced in 1 dBm steps to \(-65\) dBm. In each stage, 6000 packets are transmitted. For each successfully decoded WuPt, the AS3933 issues an interrupt to the MSP430. \(N_{\text{WuPt},\text{Rx}}\) indicates the total number of interrupts. Then, the  
Figure 17: Current curve of WuPt. (**a**) Transmitter node, (**b**) receiver node with BJT, and (**c**) receiver node with TIA amplifiers.  
interrupts are logged and compared with the total number of transmitted WuPts \(N_{\text{WuPt\_Tx}}\). To obtain a practical indication of the sensitivity of the WuRx, the PER is measured at each iteration. The PER can be calculated as presented in Equation (16).  
\[\text{PER}=1-\frac{N_{\text{WuPt\_Rx}}}{N_{\text{WuPt\_Tx}}} \tag{16}\]  
The relationships of received WuPts against input power, as well as the RSSI value, are shown in Figure 19. With a probability of 96%, the TIA and BJT platines detect the packets at an input signal down to \(-48\,\text{dBm}\). Both architectures receive reliably up to a sensitivity of almost \(-50\,\text{dBm}\). With a packet receiving rate of around 1%, the lowest sensitivity of the BJT is \(-62\,\text{dBm}\) and RSSI drops to 1, whereas the TIA can only detect packets up to an input power of \(-54\,\text{dBm}\), due to the noisy LDO of the AS3933. This shows that it is not only the sensitivity of the WuRx that matters, but the reliability of the reception rate must also be taken into account.
# Seismic Design Procedure for Low-Rise Cold-Formed Steel-Special Bolded Moment Frames
###### Abstract  
In 2007, the American Iron and Steel Institute (AISI) established a standard for cold-formed steel-special bolted moment frames (CFS-SBMFs). This structural system is designed to resist seismic forces. The CFS-SBMF system employs double-channel beams and square hollow structural section (HSS) columns that are bolted together to create a study and robust structural frame. However, the CFS-SBMF system is only suitable for constructing one-storey buildings, and ASCE 7 prohibitis its use in buildings with a height of over one storey. This study was conducted to expand the use of CFS-SBMFs to the construction of multi-storey low-rise buildings. Firstly, a new moment connection detail is proposed, and a design procedure is proposed to ensure that bolted connections, instead of beams or columns, have the ductility to withstand seismic forces. Secondly, the proposed design procedure for bolted connections was verified through full-scale cyclic testing. Finally, a comprehensive evaluation was undertaken to evaluate the proposed structural system's performance under seismic excitation. The evaluation included nonlinear dynamic analysis and incremental dynamic analysis (IDA) according to FEMA P695, which provided a detailed understanding of the seismic design factors (SDFs) in multi-storey low-rise CFS-SBMF buildings.  
steel frames; cold-formed steel; bolted moment connection; slippage; bearing; seismic design factors; response modification coefficient; system overstrength factor; deflection amplification factor +
Footnote †: journal: Applied sciences
## 2 The Methodology of the Research
### Results Evaluation
#### 2.2.2 L2 Metric for Curve Difference Evaluation  
In this research, we need to compare machine learning performance metrics; these metrics will be represented in the form of curves, with the horizontal axis representing different time windows. The L2 norm is widely used to evaluate the differences between curves as it provides a mathematically robust measure that captures the overall discrepancy across the entire domain. By integrating the squared differences in a point-wise manner, it highlights significant variations while smoothing out smaller ones. This approach aligns with the principles of functional analysis, where the L2 norm is often employed to quantify the energy-like differences between functions [43].  
To evaluate the difference between two curves using the L2 norm, we replaced the pointwise differences with their squared counterparts and integrated over the domain of the curves:  
\[A_{L2}=\sqrt{\int_{x_{\min}}^{x_{\max}}(y_{1}(x)-y_{2}(x))^{2}dx} \tag{3}\]  
In the experiments, we do not obtain the curve itself--we obtained a discrete set of points instead. These points were visually presented as piecewise linear graphs, and the integral was approximated numerically using the trapezoidal method. The trapezoidal method approximates the area under the curve by dividing it into trapezoids, summing their areas and thus providing an approximation of the integral. Applying this method to (3), we obtained the following:  
\[A_{L2}\approx\sqrt{\sum_{i=1}^{n-1}\frac{\Delta x_{i}}{2}\bigg{[}(y_{1i}-y_{2} )^{2}+\left(y_{1(i+1)}-y_{2(i+1)}\right)^{2}\bigg{]}} \tag{4}\]  
where \(\Delta x_{i}=x_{i+1}-x_{i}\) represents the interval width.
###### Abstract  
A \((k,n)\) visual cryptography (VC) scheme encodes a secret image into \(n\) shadows that are printed on transparencies distributed among a group of \(n\) participants secretly, and reveal the secret image by stacking no less than \(k\) of them. Its decryption requires no computation and attracts much attention in image security applications. The pixel expansion and contrast are two important characteristics to evaluate the visual quality of the revealed secret image for a \((k,n)\)-VC scheme. The \((k,n)\) XOR-based VC (XVC) schemes can greatly improve the visual quality including both pixel expansion and contrast. Previous methods require complex computation and result in high pixel expansion when they are used to construct such schemes. In this paper, we propose a pixel expansion adjustable \((k,n)\)-XVC scheme, which allows pixel expansion to be changed among \(2^{k-1}-1\) different values. It can ensure each pixel being exactly recovered with the same average contrast no matter it takes any pixel expansion value. The least pixel expansion is much smaller than previous schemes. Our scheme can be easily implemented based on any conventional OR-based \((k,n)\)-VC (OVC) scheme.  
Keywords:visual cryptography; XOR; pixel expansion; contrast +
Footnote †: journal:
# FFG-YOLO: Improved YOLOv8 for Target Detection of Lightweight Unmanned Aerial Vehicles
## 4 Results  
### Dataset and Experiment  
We employed the VisDrone2019 dataset for model training and validation, co-constructed by Tianjin University and Shangtang Technology [28]. The dataset was obtained from \"[[https://github.com/VisDrone/VisDrone-Dataset](https://github.com/VisDrone/VisDrone-Dataset)]([https://github.com/VisDrone/VisDrone-Dataset](https://github.com/VisDrone/VisDrone-Dataset)) (accessed on 1 July 2025)\". This dataset includes UAV-captured aerial images with diverse scenarios (urban roads, villages, etc.) and corresponding annotations in the format of bounding boxes (\(x1\), \(y1\), \(x2\), and \(y2\)), target categories, and confidence scores. The output data of FFG-YOLO consists of the model's detection results, including predicted bounding box coordinates (\(x1\), \(y1\), \(x2\), and \(y2\)) for each target, classified categories (e.g., pedestrian, car), and confidence scores indicating the model's certainty in each prediction.  
This dataset comprises data collected from diverse UAVs across varied scenarios (urban roads, villages, campuses, parks), lighting conditions (sunny, cloudy, rainy), and viewpoints (swooping, side-flying). It features complex occlusions (e.g., vegetation, dense traffic) and multi-scale targets (10 \(\times\) 10-500 \(\times\) 500 pixels). The dataset includes over 100,000 images from 1020 video sequences, with the target detection subset consisting of 6471 labeled images (4766 for training, 548 for validation, 1157 for testing) across 10 categories (e.g., pedestrians, vehicles). Notably, small targets measuring 32 \(\times\) 32 pixels account for 40% of the over 280,000 identified objects. This pronounced scale diversity, complex background interference, multi-angle target morphology, and fine-grained intra-class variations make the dataset highly suitable for evaluating model feature extraction, contextual reasoning, and anti-occlusion capabilities. For the experiment, an RTX 4090 graphics card (24 GB memory), Ubuntu 22.04 operating system, Python 3.8.20, Pytorch 2.4.1, and CUDA 12.4 were used. Model training parameters included the following: An SGD optimizer, a momentum of 0.937, an initial learning rate of 0.01, a batch size of 4, 300 training epochs, an image size of 640 \(\times\) 384, and a weight decay of 0.0005.
###### Abstract  
Ecosystem water use efficiency (WUE describes carbon-water flux coupling in terrestrial ecosystems. Understanding response and resilience of WUE to drought are essential for sustainable water resource and ecosystem management under increasing drought risks over China due to climate warming. Here we analyzed the response of ecosystem WUE to drought (spatiotemporal variability and resilience) over China during 1982-2015 based on an evapotranspiration (ET) dataset based on the model tree ensemble (MTE) algorithm using flux-tower ET measurements and satellite-retrieved GPP data. The results showed that the multiyear average WUE was 1.55 g C kg\({}^{-1}\) H\({}_{2}\)O over China. WUE increased in 77.1% of Chinese territory during the past 34 years. During drought periods, the ecosystem WUE increased mainly in the northeast of Inner Mongolia, Northeast China and some regions in southern China with abundant forests but decreased in northwestern and central China. An apparent lagging effect of drought on ecosystem WUE was observed in the east of Inner Mongolia and Northeast China, the west and east regions of North China and the central part of Tibetan Plateau. Some ecosystems (e.g., deciduous needle-leaf forests, deciduous broadleaf forests, evergreen broadleaf forests and evergreen needle-leaf forests) in Central China, Northeast and Southwest China exhibited relatively greater resilience to drought than others by improving their WUE. Our findings would provide useful information for Chinese government to adopt a reasonable approach for maintaining the structure and functions of ecosystems under drought disturbance in future.  
water use efficiency; drought; ecosystem resilience; spatiotemporal variability; vegetation types +
Footnote †: journal: Accepted: 17 July 2019; Published: 18 July 2019