esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Remote Sensing Indices and LULC Classes  
Vegetation indices such as normalized difference vegetation index (NDVI) or soil adjusted vegetation index (SAVI) can be obtained from remotely sensed data. Vegetation indices are simple to generate from multispectral satellite imagery and effective algorithms for evaluating vegetation cover quantitatively and qualitatively. Similarly, an urban index, such as a normalized built-up index (NDBI), can be used to identify urban features on satellite images. In the hands of trained geospatial analysts, remote sensing indices can highlight different types of land cover and can be particularly useful for training classifiers used in LULC maps.  
In this study, the difference vegetation index (DVI) and NDVI were used to identify vegetation cover [7]. Since agriculture and forest have similar values in both indices, the barren lands were identified using the normalized bare land index (NBII) index to overcome this issue (Figure 2A1-A5,B1-B5). The NBII index is effective in highlighting soil composition and is helpful to differentiate agriculture from forested areas Figure 2A5,B5 [11]. Urban features were identified with the NDBI and built-up index (UI). These indices were used because they distinguish barren land from urban features [11]. Results of the NDBI index presented in Figure 2A3,B3 showed that some pixels representing urban areas on the Landsat-8 and Sentinel images were mixed with bare land features. This issue was resolved by using the UI index, since urban features can be identified with more precision in Figure 2A4,B4. The minimum and maximum values of vegetation indices between 0 and 1 are shown in the legend in Figure 2. To construct the training samples for LULC mapping, maximum value pixels were used in each index.  
Remote sensing indices presented in Table 2 were used to delineate the LULC classes. Four LULC classes, namely agriculture, urban, barren land, and forest, were identified in the study area (Table 3). These indices were used for extracting the training samples for LULC classification in ArcGIS Pro. A total of 2000 training samples, 500 samples for each class, were created to train the classifier. The sample size was determined to be large enough since it adequately covers the entire study area without exhausting the classifier computing power.  
Figure 2: Sentinel-2A and Landsat-8 indices are used to classify the agriculture, urban, forest, and barren land cover. The right portion (**A1–A5**) and (**B1–B5**) of each figure shows the details of the selected portion. The minimum and maximum values of vegetation indices vary from 0 to 1.
###### Abstract  
Manufacturing quality prediction can be used to design better parameters at an earlier production stage. However, in complex manufacturing processes, prediction performance is affected by multi-parameter inputs. To address this issue, a deep regression framework based on manifold learning (MDRN) is proposed in this paper. The multi-parameter inputs (i.e., high-dimensional information) were firstly analyzed using manifold learning (ML), which is an effective nonlinear technique for low-dimensional feature extraction that can enhance the representation of multi-parameter inputs and reduce calculation burdens. The features obtained through the ML were then learned by a deep learning architecture (DL). It can learn sufficient features of the pattern between manufacturing quality and the low-dimensional information in an unsupervised framework, which has been proven to be effective in many fields. Finally, the learned features were inputted into the regression network, and manufacturing quality predictions were made. One type (two cases) of machinery parts manufacturing system was investigated in order to estimate the performance of the proposed MDRN with three comparisons. The experiments showed that the MDRN overwhelmed all the peer methods in terms of mean absolute percentage error, root-mean-square error, and threshold statistics. Based on these results, we conclude that integrating the ML technique for dimension reduction and the DL technique for feature extraction can improve multi-parameter manufacturing quality predictions.  
deep regression network; multi-parameter; low-dimensional feature; manufacturing quality; prediction +
Footnote †: journal: Applied Sciences
## 4 Design of Adaptive PID Controller Based on ISSA
### Improved Adaptive PID Controller  
The performance of a PID controller is highly dependent on its three parameters. Determining these parameters typically involves adjustment based on empirical knowledge or experimentation, a method that often consumes significant time and effort and may result in unstable performance or suboptimal control effects. In the anti-swing control system of a bridge crane, which represents a single-input multiple-output system, the outputs include the position of the trolley and the swinging angle of the lifting load. Consequently, the system designed in this study adopts a dual-loop feedback control model. Within this model, the optimization objective is to find a set of PID values that minimize the error by optimizing the three parameters. PID control is chosen for trolley position control due to its requirement for high precision and elimination of steady-state errors, while PD control is selected for payload swing angle control because it necessitates quick response and oscillation suppression. The schematic diagram of the PID control system based on the ISSA algorithm is depicted in Figure 5.  
Figure 4: Schematic diagram of the PID control system.  
Figure 5: Improved bridge crane PID control system with ISSA algorithm.
## 10 Results and Discussion
### Energy Consumption  
Energy consumption refers to the amount of energy used or consumed by a device over a specific time. Figure 8 shows the energy consumption of the GBG-RPL mechanism and the BCPS-RPL mechanism over time.  
Figure 7: Packet loss ratio.  
Figure 8 illustrates a trend for both the GBG-RPL and BCPS-RPL mechanisms to consume less energy with time. The GBG-RPL displays less energy consumption over the simulation time, which results in enhanced network life. This means more availability of the CPS network. In both techniques, initially, the nodes consume more energy as the system is initialized and trust in the system is established. However, in GBG-RPL, the nodes are only observing and forwarding the trust parameters to the root, whereas, in BCPS-RPL, the nodes are observing the neighbor nodes for their behavior and also calculating the trust score, which increases energy consumption. This trend of high energy consumption in BCPS-RPL and low energy consumption in GBG-RPL is evident throughout the simulation time. Furthermore, once the attack is injected into the CPS network, the GBG-RPL takes less time to identify the attacker as compared to the BCPS-RPL, which further contributes to the energy savings of the sensor nodes.
# How Does the Built Environment Affect Mechanical Parking Space Planning: A Case Study in Xi'an City
## 5 Results  
### The Result of the Prediction Model  
A total of 2506 buffer areas (\(\pi\times 500\times 500\)) are used to construct models for predicting the mechanical parking spaces ratio. Python is used to conduct the prediction using the GBDT model. First, all samples are divided into 80% training and 20% test groups. Then, the GBDT model is fitted and the parameters are selected using the grid search method. The model parameters were determined using the five-fold cross-validation method. The data set was randomly divided into five subsets of equal sample size. Four of them were selected as the training set and the remaining one as the test set. The depth of the tree is 3 and the number of trees is 150. The prediction R\({}^{2}\) of the GBDT and OLS models are 37% and 11%. The GBDT model achieves a higher prediction accuracy.
## 2 Background and Related Work
### IoT for Emergency Management  
The growing interest in leveraging IoT technologies for emergency management has gained significant momentum in recent years, as organizations and institutions recognize the potential of connected devices to enhance safety and response capabilities. The ability of IoT systems to provide real-time data, automate alerts, and facilitate communication during crises has positioned them as a crucial component in modern emergency management strategies. This burgeoning interest is also demonstrated by the First International Workshop on Internet of Things for Emergency Management (2020), whose results have been presented in [26]. The discussion session among workshop participants and paper presenters highlighted key challenges, including (i) addressing issues related to the installation and coverage of IoT resources; (ii) understanding the impact of environmental context on detection; and (iii) meeting the real-time requirements. [PERSON] and [PERSON] directed their attention to urban emergencies, specifically emphasizing traffic emergency responses in their work [27]. They presented a system designed to bolster urban emergency management by gathering data from various sources to facilitate emergency respondents' coordination. [PERSON] et al. introduced a cloud-based system that integrates wireless sensor networks (WSNs) with 3D virtual environments for natural disaster management [28]. The system collects real-time data from WSNs to create a realistic 3D environment, facilitating the training of rescue teams in various scenarios. It employs an efficient cloud architecture, combining 3D data streaming and sensor data collection. Notably, the system utilizes an enhanced Routing Protocol for Low-Power and Lossy Networks (RLP) for data transfer and a dynamic game engine for near-real-time 3D rendering. An Extensible Markup Language (XML) atomic action concept allows continuous scene modifications without interrupting the engine. Additionally, the paper proposes a multi-objective multiple travelling salesman problem (AHP-MTSP) algorithm for generating efficient rescue plans. Results show that immediate feedback from the 3D environment aids in preparing effective rescue plans with optimal resource allocation. [PERSON] and [PERSON] [29] implemented a neural-network-based system for emergency detection with the aim of minimizing human-induced inefficiencies in emergency systems. An example of this smart government system has been tested in real settings to prove its effectiveness.  
Contrastingly, [PERSON] et al. [30] tackled emergency management from the opposite side, developing a user-centered smart emergency response. Such a system continuously monitors the health status of users and triggers warnings when emergencies are detected.
## 3 Materials and Methods
### Indoor Radon Monitoring  
Radon levels were measured in 100 residences in six of the nine municipalities (Figure 2). A questionnaire was issued to residents to obtain information regarding the building's features. Radon concentration measurements were mainly taken in the bedroom, where most people spend at least 30% of their time, using solid state nuclear detectors (CR39). The measurements began in September-November 2019 and completed in September-November 2020. To determine the average annual radon concentration, the CR39 detectors were exposed for two consecutive semesters. The measurements were carried out by the Radioactivity Laboratory (lab.RAD, University of Naples Federico II, Naples, Italy), which is certified to the ISO 9001:2015 standard and accredited to the European Standard EN ISO/IEC 17025 for the \"integrated measurement method for determining average activity concentration of radon 222 in the environment air using passive sampling and delayed analysis\" (UNI ISO 11665:2020, part 4). After the exposure, the CR-39 detectors were chemically etched, using a solution of 6.25 M NaOH at 98 \({}^{\circ}\)C for 60 min. The track density was determined by acquiring the image of the detector using a Politrack (Mi.am) automatic counter of track density. After reading the tracks, the concentration was calculated using the relationship:  
\[C_{Rn}=\frac{N}{E\times T} \tag{1}\]  
where \(N\) (tracks/cm\({}^{2}\)) is the track density corrected by background track density, \(E\) (tracks cm\({}^{-2}\)/Bq m\({}^{-3}\) h) is the calibration factor, and \(T\) (h) is the exposure time.  
Figure 2: Indoor radon measurement points on the Sorrentina peninsula.  
Once the exposure times \(\Delta t_{1}\) and \(\Delta t_{2}\) were known, the average annual concentration was determined as follows:  
\[C_{Rn}^{T}=\frac{\left(\Delta t_{1}\times C_{Rn}^{1}\right)+\left(\Delta t_{2} \times C_{Rn}^{2}\right)}{\left(\Delta t_{1}+\Delta t_{2}\right)} \tag{2}\]  
where \(C_{Rn}^{1}\) and \(C_{Rn}^{2}\) are the integrated measured radon concentrations determined in the first and second semester, respectively.  
A comparison exercise coordinated by the German Federal Office for Radiation Protection (BfS) was used to ensure the overall quality of the procedure.  
Figure 2 shows the distribution of the indoor radon measurement points on the Sorrentina Peninsula, and, in particular, in the area that concerns the six municipalities (Massa Lubrense, Meta, Sant'Agnello, Sorrento, Piano di Sorrento, and Vico Equense).
## 4 Parameter Discussion
### Effect of Vehicle Speed  
This section examines the performance of the proposed bridge damage identification method in Cases 4-6 under vehicle speeds of 2 m/s, 5 m/s, and 8 m/s. As shown in Figure 7, the process can effectively identify the damage location at all three vehicle speeds. As the vehicle speed increases, the peak at the damage location also gradually increases. This is because increased vehicle speed enhances the vehicle-bridge vibration response, making the damage more apparent. However, when the speed of the vehicle is too fast, on the one hand, the effective data collected is reduced, which is detrimental to damage recognition. On the other hand, the interference from the pavement roughness is also exacerbated. Therefore, the vehicle speed should be maintained within a reasonable range to optimize the damage identification effect.
# A Novel Heterogeneous Parallel Convolution Bi-LSTM for Speech Emotion Recognition
## 4 Experimental Evaluations
### Databases  
To query the effectiveness of HPCB in SER, it was tested on three benchmark databases EMO-DB [24], CASIA [25], and SAVEE [26]. EMO-DB is a German corpus and it contains 535 emotional sentences in total. It contains 10 speakers and 7 emotions, namely, boredom (B), anger (A), fear (F), sadness (S), disgust (D), happiness (H), and neutral (N).  
CASIA is a Chinese corpus, and it is constructed by the Institute of Automation, Chinese Academy of Sciences. The public CASIA corpus contains 1200 utterances and the average length of each audio is about 1.9 s. There are 4 speakers and each speaker records 300 words in the same text. There are 6 emotions, namely, anger (A), fear (F), happy (H), neutral (N), sad (Sa), surprise (Su).  
SAVEE is an English corpus, and it contains 4 speakers and 7 different emotions, namely, anger (A), disgust (D), fear (F), happiness (H), sadness (Sa), surprise (Su), and neutral (N). The number of samples of neutral is 120 while that of each remainder class is 60. Totally, there are 480 utterances.  
Figure 2 shows t-SNE visualizations of the databases EMODB, CASIA, and SAVEE. It can be seen from Figure 2a that even if the samples of database EMODB are projected into the two-dimensional space spanned by t-SNE bases, the degree of confusion can be still large. The t-SNE visualization implies that the dataset shows high nonlinearity and the corresponding SER classification seems to be a nonlinear inseparable problem. Figure 2b,c shows the similar situations for the samples of databases CASIA and SAVEE. They suggest that they both are nonlinear inseparable problems because there are no clear boundaries for each class and different types of samples are even wired together sometimes.  
Figure 2: The t-SNE visualizations of the benchmark databases: EMODB, CASIA, and SAVEE. **(a)** The t-SNE visualization of corpus EMODB. **(b)** The t-SNE visualization of corpus CASIA. **(c)** The t-SNE visualization of corpus SAVEE.
# Analysis and Experimental Test of Electrical Characteristics on Bonding Wire
## 4 Bonding Wire Design and Measurement Experiment
### S Parameter Measurement And Error Analysis  
The measurement band is determined from 0.3 MHz to 10 GHz using VNA. The test board was connected to the instrument through SMA. Also, the S parameters of the device were measured after calibration. The S11, \(\lx@sectionsign\)11, S21 and \(\lx@sectionsign\)21 measured graphs of bonding wire are shown from Figure 21a-d. The measured data were compared with the simulation data (as shown in Figure 22).  
Figure 21: (**a**) Measured S11 of circuit-under-test; (**b**) Measured \(\lx@sectionsign\)11 of circuit-under-test; (**c**) Measured S21 of circuit-under-test; (**d**) Measured \(\lx@sectionsign\)21 of circuit-under-test.  
Figure 20: (**a**) Bonding wire under VMS; (**b**) First bonding point under 10 times microscope; (**c**) First bonding point under 40 times microscope; (**d**) Second bonding point under 20 times microscope.  
It can be seen from Figure 22 that the trend of simulation curve and measured curve are consistent, that is, the S11 increases and S21 decreases with the rise of the frequency. This means that the signal reflection increases and the passing signal drops. The peak value of the test curve may appear because of the resonance problem of the whole test system. However, there is a certain gap between the measured value and the simulation value. The maximum error was about \(-20\) dB in S11 curve, while \(-7\) dB in S21 curve. The error comes mainly from the following three aspects:  
1. Systematic error of measurement experiment  
Due to the use of the SMA connector, the error of the measurement system is introduced to complete the measurement such as SMA conversion error, reflection/transmission measurement circuit error and fixture embedded error, as well as the random error in measurement.  
2. Model error  
For example, the simulation model is not precise enough resulting in less accurate modeling of the first and second bonding point; unable to establish completely accurate bonding wire model of actual experiment wire; the excitation port of stimulation is inconsistent with the actual measurement port.  
3. Manufacturing error  
The dielectric constant of the test plate is not constant resulting in dielectric loss; the actual impedance is inconsistent with calculation causing impedance mismatch and reflection loss problem.
## 2 Materials and Methods
### Algorithm for Determining the Correction of Track Axis Coordinates
#### 2.4.1 Definition of Quality Conditions  
In this method, the distance vectors between GNSS antennas--referred to as control distances--are central. Each antenna pair maintains a predefined reference distance. At each epoch, the absolute difference between the measured and reference distance for a given pair is compared against a quality threshold, \(\Delta L_{\text{max}}\). If the difference does not exceed \(\Delta L_{\text{max}}\), the pair is deemed to be within tolerance.  
The fundamental quality check for a GNSS receiver's measurement is satisfied if at least one of the following conditions holds:  
* At least two control distances between the considered receiver and the others must fall within the assumed tolerance, including
* At least one \"short\" distance.
* At least one \"long\" distance.
* All \"long\" control distances from the considered receiver must be within the accepted tolerance. For example, for the AC receiver, the measurement result (horizontal coordinates \(X\), \(\gamma\)) meets the quality requirements only when the following logical condition is evaluated as TRUE: \[W_{\text{AC}}:\begin{bmatrix}[(|L_{2}-L_{\text{w}}|\leq\Delta L_{\text{max}}) \wedge(|L_{6}-L_{\text{w}}|\leq\Delta L_{\text{max}})]\vee[(|L_{2}-L_{\text{ w}}|\leq\Delta L_{\text{max}})\wedge(|L_{7}-L_{\text{w}}|\leq\Delta L_{\text{ max}})]\vee\\ \vee[(|L_{12}-L_{\text{w}}|\leq\Delta L_{\text{max}})\wedge(|L_{6}-L_{\text{ w}}|\leq\Delta L_{\text{max}})]\vee[(|L_{12}-L_{\text{w}}|\leq\Delta L_{\text{max}}) \wedge(|L_{7}-L_{\text{w}}|\leq\Delta L_{\text{max}})]\vee\\ \vee[(|L_{14}-L_{\text{w}}|\leq\Delta L_{\text{max}})\wedge(|L_{6}-L_{\text{ w}}|\leq\Delta L_{\text{max}})]\vee[(|L_{14}-L_{\text{w}}|\leq\Delta L_{\text{max}}) \wedge(|L_{7}-L_{\text{w}}|\leq\Delta L_{\text{max}})]\vee\\ \vee[(|L_{12}-L_{\text{w}}|\leq\Delta L_{\text{max}})\wedge(|L_{2}-L_{\text{ w}}|\leq\Delta L_{\text{max}})\wedge(|L_{14}-L_{\text{w}}|\leq\Delta L_{\text{max}})] \end{bmatrix}\] (10)  
Logical conditions were similarly established for the other receivers. Consequently, by identifying which GNSS receivers yield measurements that meet the quality criteria at each epoch--and by applying the corresponding relationships--it is possible to compute the track axis coordinates even when some receivers do not provide sufficiently accurate data at that time.
# Zero-Shot Detection of Visual Food Safety Hazards via Knowledge-Enhanced Feature Synthesis
## 3 Methodology
### Knowledge-Enhanced Feature Synthesizer  
Building upon the framework introduced by [PERSON] et al. [12], we adapt the Knowledge-Enhanced Feature Synthesizer (KEFS) to the food safety domain. Our KEFS consists of two primary components: the Multi-Source Graph Fusion (MSGF) module, which integrates knowledge from multiple sources, and the Region Feature Diffusion Model (RFDM), which generates discriminative features for unseen hazard classes.
# Grapevine Branch Recognition and Pruning Point Localization Technology Based on Image Processing
## 2 Materials and Methods
### Thinning Algorithm  
The Zhang-Suen thinning algorithm refines an image by iteratively deleting pixel points in the image. The pixel point is judged to be removed or not based on the pixel value of its neighborhood. The eight neighbors of a pixel point (x,y) are shown in Figure 8.  
Figure 8: Names of the eight neighbors around the pixel point.  
Figure 7: The network structure of YOLOv5.  
The algorithm is divided into two main steps: for the foreground pixel (x,y). (a) if the four Equations (1)-(4) are satisfied, its pixel value is set to 0 and marked for deletion.  
\[2\leqslant B(P_{1})\leqslant 6 \tag{1}\]  
\[A(P_{1})=1 \tag{2}\]  
\[P_{2}\times P_{4}\times P_{6}=0 \tag{3}\]  
\[P_{4}\times P_{6}\times P_{8}=0 \tag{4}\]  
where \(B(P_{1})\) indicates the number of foreground pixels in the eight pixels adjacent to P1. \(A(P_{1})\) denotes the cumulative number of occurrences of 0 to 1 in pixels from P2 to P9 to P2, where 0 denotes the background and 1 denotes the foreground.  
(b) If the four Equations (1), (2), (5), (6) are satisfied, the pixel value is set to 0 and marked for deletion.  
\[P_{2}\times P_{4}\times P_{8}=0 \tag{5}\]  
\[P_{2}\times P_{6}\times P_{8}=0 \tag{6}\]  
Repeat steps (a) and (b) until the image no longer changes.  
In order to meet the experimental requirements of this paper, the Zhang-Suen refinement algorithm is modified by changing Equations (1) to (7) and it can be seen in RESULT3.1 that the modified Zhang-Suen refinement algorithm has fewer branches and is more suitable for determining the location of pruning points.  
\[1\leqslant B(P_{1})\leqslant 6 \tag{7}\]
## Materials and Methods
### Wavelength Selection Method
#### 2.5.4 iPLS  
The iPLS method is a wavelength interval selection method. The method functions by dividing the whole spectrum into several intervals, and then expanding or decreasing the wavelength variables by the center of the interval [18]. The modeling setting of the iPLS method was as follows: the number of intervals was set to 20 in MATLAB R2016a (MathWorks, Natick, MA, USA).