esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Verification  
### Introduction of the Test Model  
The test model is not according to a specific bridge but mainly references to a concrete-filled steel tubular arch bridge with a span of 90 m, and the size of the model was designed and determined according to the structural layout of the general through-arch bridge and laboratory conditions. The model bridge is illustrated in Figure 8. The total length of the model bridge is 3.6 m, the width is 0.6 m, the height is 0.8 m, and the linear shape is a quadratic parabola. The arch axis equation is: \(y=(4\times 0.8)/3.6^{2}\times(3.6-x)\times x\). The  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Damage Type** & **Damage Case No.** & **Damage Hanger No.** & **Damage Degree** \\ \hline
**Single hanger** & DC 19 & N4 & 20\% \\
**failure** & DC 20 & N5 & 10\% \\
**Double hanger** & DC 21 & N2 \& N4 & 10\% \\
**failure** & DC 22 & N3 \& N7 & 30\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Four damage conditions that considering the creep and stiffness degradation.  
Figure 7: Identification results for considering creep and stiffness degradation. (**a**) results for DC8&DC19; (**b**) results for DC10&DC20; (**c**) results for DC13&DC21; (**d**) results for DC18&DC22.  
cross-section of the arch rib is 60 mm \(\times\) 5 mm circular steel pipe. To study the influence of different rib-beam stiffness ratios, a set of arch ribs with a small cross-section was also prepared. The thickness of the bridge deck is 4 mm. The cross-section of the crossbeam and the tie-beam is L-shaped. Three wind braces are arranged in the whole bridge, and its cross-section is a circular steel pipe with a cross-section of 50 mm \(\times\) 5 mm.  
The test model is intended to research the damage in its early stages and therefore falls into the linear damage category in which the structure is assumed linear in the pre- and post-damaged states. Because its primary purpose is to identify the danger damage according to the deflection change of the tie-beam, it is not entirely scaled according to the raw materials. Steel was used instead of concrete-filled steel tubes. To accurately control the preset degree of damage, the hanger was specially designed in this model. The hanger is mainly composed of four parts in series including, a wire rope segment with a diameter of 3 mm, load cell for cable force, spring segment (consisting of 8 springs with the same stiffness in parallel), and small flanges for adjusting cable force (see Figure 8).  
The test bridge was instrumented with a dense array of sensors, including eighteen displacement sensors with an accuracy of 0.01 mm, and eighteen load cell sensors for cable force. The diagram of the sensors is shown in Figures 9 and 10, illustrating the locations of the deflection sensors. The measurement point of the south side is S1-S9, and the north side is N1-N9.  
Figure 8: Test model diagram of the through-arch bridge.  
Figure 9: Digital display laser displacement sensor and load cell sensor of cable force.
## 2 Methods
### Data Labeling  
The data-labeling process standardized labels across different databases (see Figure 3). In the Apnea-ECG database, labels were based on whether apnea occurred at the start of the minute (Figure 3a) with \"A\" for apnea and \"N\" otherwise. In the MIT-BIH database, segments were re-labeled by inheriting the original label, which corresponded to the first episode in the segment (Figure 3b). If the episode was associated with apnea, the label was converted to \"A\"; otherwise, it was assigned \"N\". For the UCD-DB database, the cohesion with the other two was ensured by synchronizing each ECG and its corresponding annotation file to the same time frame (Figure 3c). The recordings were then divided into one-minute segments, which was similar to the other databases. After segmentation, the ECG excerpts were labeled as \"A\" if an apnea-related episode occurred right at the start of the segment and \"N\" otherwise. This systematic approach allowed for uniformity in the labeling process across all databases, ensuring a consistent framework for training models.
# Development of a Wireless System to Control a Trombe Wall for Poultry Brooding
## 1 Introduction
In order to achieve this objective, air vents are controlled with servo motors, based on the analyzed temperature values at an instant moment. These actions are made by a microcontroller along with a server in the Internet, so that the decisions of closing and opening the air vents are not made locally, avoiding user's intervention. There are some wireless systems that are normally used in building automation. WiFi, for example, is a consistent system that allows high data rates, but on the other hand, it consumes plenty of power when compared to others and has a short range. However, its suitability is verified when installed in homes, given that, nowadays, a WiFi router is commonly used. ZigBee is also considered a good wireless communication system because it is low powered and characterized by low implementation costs. In fact, it presents a similar range as WiFi but smaller data rates. As ZigBee, there is a similar system called LoRa, which is also a low-powered and low-data-rate wireless technology. However, it can reach further distances [9, 10], which is an advantage in the case of its implementation over long distances. Given that the locations of poultry brooding houses are normally in large fields, WiFi and ZigBee might not be able to reach them; therefore, LoRa is considered the most suitable technology for these cases where temperature values registered by sensors are sent over a network. Furthermore,relatively small data packets are used; therefore, high data rates are not needed. The microcontroller used in this system is an end-node of a star topology LoRaWAN network, in order to be connected to a server called The Things Network (TTN) and to guarantee its ThingSpeak integration on the application layer. Data registered by the temperature sensors travels from the microcontroller to the application where it is plotted. A decision is made based on data analysis and sent as feedback to the microcontroller. Thatdecision contains information that tells the microcontroller to operate the actuators, which in this case are the servos controlling the air vents. The main goal of this research work is to make a decision in the Internet, using a server, given that a remote interaction with the system can be achieved [11].
## 2 Background
### Additive Slack Based Model  
Suppose DMU\({}_{i}\) j = 1,..., n by consuming m inputs x\({}_{1j},\ldots,x_{mi}\), j = 1,..., n can produce s outputs y\({}_{1j},\ldots,y_{gi}\); j = 1,..., n. The background of ASBM can be related to the additive model in [PERSON] et al. (1985) [4] and [PERSON] et al. (1997) [6]. Therefore, efficiency for output r of DMU\({}_{0}\) is defined as \(\frac{Y_{\rm{zo}}}{Y_{\rm{zo}}+\rm{t_{r}}}\leq 1\) and non-efficiency for output r of DMU\({}_{o}\) is defined as \(1-\frac{Y_{\rm{zo}}}{Y_{\rm{zo}}+\rm{t_{r}}}=\frac{\rm{t_{r}}}{Y_{\rm{zo}}+\rm{t _{r}}}\leq 1\). Furthermore, efficiency for input i of DMU\({}_{o}\) is defined as \(\frac{x_{\rm{zo}}-\rm{s_{i}}}{x_{\rm{zo}}}\leq 1\) and non-efficiency for output r of DMU\({}_{o}\) is defined as \(1-\frac{x_{\rm{zo}}-\rm{s_{i}}}{x_{\rm{zo}}}=\frac{\rm{s_{i}}}{x_{\rm{zo}}}\leq 1\). Therefore, by suggesting model (1), the relationship between non-efficiency calculated by [PERSON] et al. (1997) [6] and ASBM based on efficiency and non-efficiency definitions have presented [11].  
\[\begin{array}{c}\rm{Min}\ \frac{1}{s+m}\left(\sum_{r=1}^{s}\frac{Y_{\rm{zo} }}{Y_{\rm{zo}}+\rm{t_{r}}}+\sum_{i=1}^{m}\frac{x_{\rm{zo}}-\rm{s_{i}}}{x_{\rm {zo}}}\right),\\ \rm{s.t}\\ \sum_{j=1}^{n}\mu_{j}x_{ij}+s_{i}=x_{i\rm{o}},\ i=1\ldots m,\\ \sum_{j=1}^{n}\mu_{j}y_{ij}-\rm{t_{r}}=y_{r\rm{o}},\ r=1\ldots s,\\ \sum_{j=1}^{n}\mu_{j}=1,\\ \mu_{j},\ s_{i},\rm{t_{r}}\geq 0,\ i=1\ldots m,\ r=1\ldots s.\end{array}\]  
Model (1) is a nonlinear mathematical programming model containing linear constraints and linear fractional objective function. In this regard, model (1) by [PERSON] and [PERSON] (2020) [11] is equivalent to RGM developed by [PERSON] et al. (1985). Moreover, finding DMU targets using slack variables are important because focus is given to the summation of slack variables. In this regard, the projection of DMU with respect to Model (1) is calculated by \(\sum_{j=1}^{n}\mu_{j}^{*}x_{ij}=x_{i\rm{o}}-\rm{s_{i}^{*}}\), \(i=1\ldots m\), and \(\sum_{j=1}^{n}\mu_{j}^{*}Y_{\rm{rj}}=y_{r\rm{o}}+\rm{t_{r}^{*}},r=1\ldots s\), which \(\mu_{j}^{*},\ s_{i}^{*}\), and \(\rm{t_{r}^{*}}\) are optimal solutions of Model (1).
## 3 Results
### Postural Control (K-TIS and PASS)  
Postural control was assessed using the K-TIS and PASS (Table 3) (Figure 2). Postural control significantly improved throughout the intervention period in both groups. Furthermore, a significant time \(\times\) group interaction was identified for both the K-TIS and PASS, suggesting that the experimental group exhibited greater improvements than the control group.  
For the K-TIS score, the experimental group improved from 12.67 \(\pm\) 3.57 at baseline to 19.27 \(\pm\) 2.31 post-intervention, while the control group increased from 14.87 \(\pm\) 3.87 to 18.73 \(\pm\) 2.78. The mean change was significantly greater in the experimental group (6.60 \(\pm\) 3.56) than in the control group (3.86 \(\pm\) 2.47), as indicated by a significant interaction effect (F = 5.958, \(p\) = 0.021).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Variable**} & **Experimental Group** & **Control Group** & \\  & **(_n_ = 15_)** & **(_n_ = 15_)** & \\ \hline Age (years) & 62.73 \(\pm\) 10.83 & 65.67 \(\pm\) 13.39 & \(-\)0.659 (0.515) \\ Height (cm) & 164.07 \(\pm\) 7.90 & 163.80 \(\pm\) 8.33 & 0.090 (0.929) \\ Weight (kg) & 65.93 \(\pm\) 9.93 & 62.73 \(\pm\) 10.49 & 0.857 (0.399) \\ MMSE-K score & 24.33 \(\pm\) 3.22 & 25.60 \(\pm\) 3.48 & 1.034 (0.310) \\ Onset (months) & 2.73 \(\pm\) 1.22 & 2.47 \(\pm\) 1.30 & 0.578 (0.568) \\ \hline \hline \end{tabular}  
* MMSE-K = mini-mental state examination—Korean version.  
\end{table}
Table 1: Baseline characteristics of participants (\(N\) = 30).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Variable**} & **Experimental Group** & **Control Group** & \\  & **(_n_ = 15_)** & **(_n_ = 15_)** & **t (_p_)** \\ \hline K-TIS (score) & 12.67 \(\pm\) 3.57 & 14.87 \(\pm\) 3.87 & \(-\)1.616 (0.117) \\ PASS (score) & 25.00 \(\pm\) 4.69 & 27.00 \(\pm\) 4.29 & \(-\)1.218 (0.233) \\ BBS (score) & 23.07 \(\pm\) 13.06 & 30.93 \(\pm\) 13.03 & \(-\)1.651 (0.110) \\ LOS (mm\({}^{2}\)) & 6673.71 \(\pm\) 4430.90 & 6749.42 \(\pm\) 4393.63 & \(-\)0.047 (0.963) \\ \hline \hline \end{tabular}  
* K-TIS = Korean version of the Trunk Impairment Scale; PASS = Postural Assessment Scale for Stroke; BBS = Berg Balance Scale; LOS = limit of stability.  
\end{table}
Table 2: Baseline scores of outcome measures between groups (\(N\) = 30).  
In terms of the PASS, the experimental group's score increased from 25.00 \(\pm\) 4.69 at baseline to 32.60 \(\pm\) 2.92 post-intervention, while the control group improved from 27.00 \(\pm\) 4.29 to 30.93 \(\pm\) 2.93. The magnitude of improvement was significantly greater in the experimental group (7.60 \(\pm\) 3.92) than in the control group (3.93 \(\pm\) 2.63), as indicated by a significant interaction effect (F = 9.034, \(p\) = 0.006).  
These results not only demonstrate statistically significant improvements but also suggest clinically meaningful gains in trunk function and sitting balance, which are essential for safe transfers and early functional independence in stroke rehabilitation.
## 4 Experimental Investigation
### Experimental Setup
The magnetostrictive patch was re-magnetized at the beginning of every experimental case to keep the uniformity of the bias magnetic field and multiple signals were collected in each case to reduce the effect of random noises.
# New Model for Defining and Implementing Performance Tests
## 4 Experiment and Discussion  
This section presents the results of applying the above test scheme to some of the most common types of load tests carried out for two real systems (web servers)--the first, without most additional services such as DNS, running under laboratory conditions, and the second running under production conditions.  
All tests were performed using the IXIA Novus One traffic generator and the dedicated IxLoad software. The application operates on layers 4-7 of the ISO/OSI model, which makes it possible to test the efficiency of support for specific state protocols and to examine the server's response in fixed bandwidth traffic. The parameters of the generator used are shown below:  
General specifications: OS: Ixia FlixOS Version 202.2.82.5; RAM: 8 GB; Hard disk: 800 GB; 4 \(\times\) 10 GBase-T-RJ-45/SFP+; maximum bandwidth: 10 Gbit/s; complies with standards: IEEE 802.1x, IEEE 802.3 ah, IEEE 802.1 as, IEEE 802.1 Qbb, IEEE 1588v2, IEEE 802.1 Qat.  
Protocols: data link: Fast Ethernet, Gigabit Ethernet, 10 Gigabit Ethernet; network/ transport: TCP/IP, UDP/IP, LZTP, ICMP/IP, IPSec, iSCSI, ARP, SMTP, FTP, DNS, POP3, IMAP, DHCPv6, NFS, RTSP, DHCPv4, IPv4, IPv6, SMB v2, SMB v3; Routing: RIP, BGP-4, EIGRP, IGMP, OSPFv2, PIM-SM, OSPFv3, PIM-SSM, RIPng, MLD, IS-ISv6, BGP-4+, MPLS, IS-ISv4; VoIP: SIP, RTP.
# Use of Real-Time GNSS-RF Data to Characterize the Swing Movements of Forestry Equipment
###### Abstract  
The western United States faces significant forest management challenges after severe bark beetle infestations have led to substantial mortality. Minimizing costs is vital for increasing the feasibility of management operations in affected forests. Multi-transmitter Global Navigation Satellite System (GNSS)-radio frequencies (RF) technology has applications in the quantification and analysis of harvest system production efficiency and provision of real-time operational machine position, navigation, and timing. The aim of this study was to determine the accuracy with which multi-transmitter GNSS-RF captures the swinging and forwarding motions of ground based harvesting machines at varying transmission intervals. Assessing the accuracy of GNSS in capturing intricate machine movements is a first step toward development of a real-time production model to assist timber harvesting of beetle-killed lodgepole pine stands. In a complete randomized block experiment with four replicates, a log loader rotated to 18 predetermined angles with GNSS-RF transponders collecting and sending data at two points along the machine boom (grapple and heel rack) and at three transmission intervals (2.5, 5.0 and 10.0 s). The 2.5 and 5.0 s intervals correctly identified 94% and 92% of cycles at the grapple and 92% and 89% of cycles at the heel, respectively. The 2.5 s interval successfully classified over 90% of individual cycle elements, while the 5.0 s interval returned statistically similar results. Predicted swing angles obtained the highest level of similarity to observed angles at the 2.5 s interval. Our results show that GNSS-RF is useful for real-time, model-based analysis of forest operations, including woody biomass production logistics.  
forest operations; logistics; multi-transmitter; accuracy; precision forestry 2017/8/44; doi:10.3390/f8020044
## 3 Results
### Testing Results
The EfficientNetB2_16 model had the highest recall rate of 99.463% for the interlayer water seepage distress category, while the MobileNetV3_64 model had the lowest recall rate of 97.711%. For interlayer loosening distress, the InceptionV3_32 model performed best, with a recall rate of 98.105%, while the worst model was ResNet34_16 (96.211%). Regarding structural loosening distress, ResNet50_16 was the best model, with a recall rate of 98.578%. In contrast, VGG11_16 had its lowest recall, with 93.365%. For the f1-score, the overall best model is SqueezeNet1\(1\)64 (98.120%), while the worst model overall was MobileNetV3_16 (97.519%). For the interlayer debonding category of distress, the best model was AlexNet_64 (99.416%), while the worst model was EfficientNetB2_64 (99.068%). For the interlayer water seepage category of distress, the best model was DenseNet121_64 (98.725%), while the worst model was EfficientNetB2_64 (98.200%). For the interlayer loosening category of distress, the best model was AlexNet_64 (97.966%), while the worst model was DenseNet169_16 (97.275%). For the structural loosening category of distress, the best models were VGG13_16 and VGG13_64 (both 97.115%), while the worst model was MobileNetV3_16 (94.713%). Combining the performance of each model during the training period and the prediction results on the test set enables a more comprehensive performance evaluation of the 18 network models. To provide a more intuitive analysis of some of the better-performing models and the models of interest, 16 visualized confusion matrices covering eight network architectures are shown in Figure 8, including SqueezeNet1\(1\)64, VGG13_16, VGG13_32, VGG13_64, and VGG16_64, which performed better in general; AlexNet_64, which performed better in interlayer debonding, interlayer water seepage, and interlayer loosening; InceptionV3_16 and DenseNet121_64, which performed better in interlayer water seepage and interlayer loosening; ResNet18_32, which performed better in residual networks; ResNet18_64, which performed better in structural loosening and interlayer debonding; ResNet50_16, which performed better in interlayer loosening and structural loosening; ResNet50_32, which performed better in interlayer water seepage and interlayer debonding;
# Long-Term Structural State Trend Forecasting Based on an FFT-Informer Model
###### Abstract  
Machine learning has been widely applied in structural health monitoring. While most existing methods, which are limited to forecasting structural state evolution of large infrastructures. forecast the structural state in a step-by-step manner, extracting feature of structural state trends and the negative effects of data collection under abnormal conditions are big challenges. To address these issues, a long-term structural state trend forecasting method based on long sequence time-series forecasting (LSTF) with an improved Informer model integrated with Fast Fourier transform (FFT) is proposed, named the FFT-Informer model. In this method, by using FFT, structural state trend features are represented by extracting amplitude and phase of a certain period of data sequence. Structural state trend, a long sequence, can be forecasted in a one-forward operation by the Informer model that can achieve high inference speed and accuracy of prediction based on the Transformer model. Furthermore, a Hampel filter that filters the abnormal deviation of the data sequence is integrated into the Multi-head ProbSparse self-attention in the Informer model to improve forecasting accuracy by reducing the effect of abnormal data points. Experimental results on two classical data sets show that the FFT-Informer model achieves high and stable accuracy and outperforms the comparative models in forecasting accuracy. It indicates that this model can effectively forecast the long-term state trend change of a structure and is proposed to be applied to structural state trend forecasting and early damage warning.  
structural health monitoring; time series forecasting; FFT; Informer model; FFT-Informer model; 2023
## 4 Data and Experimental Setting
### Dataset Description  
The SST time series used here is delivered by the UK Met Office [5] from January 2008 to December 2015. The spatial resolution of our SST field is 0.05\({}^{\circ}\) and the temporal resolution \(h=1\) day. The data from 2008 to 2014 were used as a training set. The 215 data were used as ground truth to provide a quantitative analysis, observations used in the assimilation experiments were simulated from this  
Figure 1: Proposed neural-network-based representation of a spatio-temporal dynamical system. The input \(X_{t}\) is first decomposed into \(P\times P\) patches, each patch is then propagated using its associate local stochastic dynamical models \((\mathcal{F}^{P_{t}},\mathcal{F}^{P_{t}}_{\Sigma})\). The mean component of the output \(X_{t+1}\) is reconstructed by injecting the forecasted patches into the reconstruction model \(\mathcal{F}_{r}\). The block diagonal covariance matrix is formed by the collection of the patch-level covariances.  
ground truth based on realistic SST clouds patterns provided by the METOP-AVHRR mask. This sensor is highly sensitive to the cloud cover and results in very high missing data rates.As case-study area, we select an area off South Africa (from 2.5\({}^{\circ}\) E, 38.75\({}^{\circ}\) S to 32.5\({}^{\circ}\) E, 58.75\({}^{\circ}\) S). This region involves is particularly relevant for the considered complex fine-scale SST dynamics (e.g., fronts, filaments). It makes it relevant for the considered quantitative and qualitative evaluation.
# Multibeam Wideband Transmit Beamforming Using 2D Sparse FIR Trapezoidal Filters
### Wideband vs. Narrowband Systems in Modern Applications
#### 1.2.2 RF-Analog Phasing vs. Fully Digital Schemes  
There are several techniques for achieving multiple steerable and possibly independent RF transmit beams from an aperture containing \(N\) elements. Analog-RF, digital, and RF-analog--digital hybrid approaches [24; 25] can be utilized for achieving wideband multibeam directed energy beamforming. In an analog-RF approach, as shown in Figure 1, signals corresponding to each independent plane-wave are applied to a digital-to-analog converter (DAC) and transmit chain, followed by a phase shifting and feed network before being applied to a PA that drives each antenna [6; 8]. Note that each DAC output is bandpass filtered (BPF) and optionally upconverted to the carrier frequency, after which each channel is amplified and split into \(K\) outputs via an active or passive microwave splitter. The outputs of the power splitter are individually routed to \(N\) analog RF dot-product units, each having \(K\) phase-shifters and a \(K\):\(1\) passive microwave combiner whose output is applied to the PAof each of the antennas in the array. Furthermore, the \(N\) number of \(K\)-point dot-products is equivalent to a single RF-analog matrix-vector multiplication, where the input is a \(K\)-element vector, and the matrix is \(K\times N\). Here, beamforming is achieved at RF using tunable delay lines. Wideband delays, however, are difficult to realize on integrated circuits making low size and weight integrated systems difficult to realize. Nevertheless, for narrowband systems, it is possible to realize tunable phase offsets for each PA input using programmable phasing circuits on chip. But the issue is that we need a dedicated set of phase-shifters for each independent beam, together with low-distortion microwave combiners and other associated electronics for signal combination prior to driving PA inputs.