esa-sceva/satcom-chunk-collection huggingface dataset:
# Detection of Defective Features in Cerasus Humilis Fruit Based on Hyperspectral Imaging Technology
###### Abstract  
Detection of skin defects in _Census humilis_ fruit is a critical process to guarantee its quality and price. This study presents a valid method for the detection of defective features in _Census humilis_ fruits based on hyperspectral imaging. A total of 420 sample images were acquired that included three types of natural defects and undamaged samples. After acquiring hyperspectral images of _Census humilis_ fruits, the spectral data were extracted from the region of interest (ROI). Five spectral preprocessing methods were used to preprocess the original spectral data, including Savitsky-Golay (S-G), standard normal variate (SNV), multiplicative scatter correction (MSC), baseline correction (BC), and de-trending (De-T). Regression coefficient (RC), successive projections algorithm (SPA), and competitive adaptive reweighed sampling (CARS) were conducted to select optimal sensitive wavelengths (SWs); as a result, 11 SWs, 17 SWs, and 13 SWs were selected, respectively. Then, the least squares-support vector machine (LS-SVM) discrimination model was established using the selected SWs. The results showed that the discriminate accuracy of the CARS-LS-SVM method was 91.43%. Based on the characteristics of image information, images corresponding to eight sensitive wavebands (950, 994, 1071, 1263, 1336, 1457, 1542, and 1628 nm) selected by CARS were subjected to principal component analysis (PCA). Then, an effective approach for detecting the defective features was exploited based on the infill function, canny operator, region growing algorithm, bwareapen function, and the images of PCA. The location and area of defect feature of 105 _Census humilis_ fruits could be recognized; the detect precision was 88.57%. This investigation demonstrated that hyperspectral imaging combined with an image processing technique could achieve the rapid identification of undamaged samples and natural defects in _Census humilis_ fruit. This provides a theoretical basis for the development of _Census humilis_ fruit grading and sorting equipment.  
hyperspectral imaging: Cerasus humilis fruit; natural defects; principal component analysis; image processing
## 4 Classification of Routing Protocols in Wireless Sensor Networks
### Data Delivery Model  
Depending on the application, data gathering and interaction in wireless sensor networks could be accomplished on several ways. The data delivery model indicates the flow of information between the sensor nodes and the sink [7]. The data delivery models are divided into the following classes: continuous, event-driven, query-driven or hybrid. In the continuous model, the nodes periodically transmit the information that their sensors are detecting at a pre-specified rate. In contrast, the query-driven approaches force nodes to wait to be demanded in order to inform about their sensed data. In the event-driven model, sensors emit their collected data when an event of interests occurs. Finally, the hybrid schemes combine the previous strategies so sensors periodically inform about the collected data but also response to queries. Additionally, they are also programmed to inform about events of interest.
# LaRaWAN Metering Infrastructure Planning in Smart Cities
## 2 Related Works  
There are a small number of items in the literature on LoRaWAN simulation that take into account accurate radio propagation models and how to deploy gateways efficiently. Upon reviewing the literature, it is evident that the majority of articles conducting simulations of LoRa networks primarily focus on analyzing access to the common channel solely for the traffic generated by the end nodes. As if by design, multi-access in a radio link is modeled. The Pure Aloha mechanism is used in this context. The authors of [23] included the assumptions of the simulation model for the MAC sublayer of the LoRa network in an application called LoRaSim written using the SimPy framework in Python. The assumptions made by the simulator tend to overlook the impact of imperfect orthogonality between messages generated on the same channel but with different spreading factors (SFs). In simulations, the primary metric used to evaluate the system's performance is the Data Extraction Rate (DER). DER is defined as the ratio of received messages to sent messages within a specified time frame, providing an assessment of the system's message retrieval efficiency.  
The LoRaWANSim [24] project extends the LoRaSim simulator to support the MAC layer mechanisms of the LoRaWAN protocol and introduces bidirectional communication. The downlink transmission to the end node is applicable for IoT applications with automation of the joining process and generates additional traffic for handshakes, acknowledgment traffic (e.g., \(ACK\) messages), and key exchange in cryptographic algorithms. The LoRaWANSim incorporates a duty cycle of 1% for the majority of European subbands. Furthermore, it incorporates a realistic collision model that eliminates collisions for traffic in both directions, even if transmissions occur simultaneously on the same channel and with the same spreading factor. The simulator also considers the retransmission strategy, whereby packets that are not acknowledged due to collisions or duty cycle constraints are retransmitted.  
It is worth mentioning that some models make the fairly realistic assumption that LoRaWAN technology is used in applications that exhibit communication asymmetry, meaning that the amount of data in the uplink is greater than in the downlink. In [25], measurements in an urban environment showed that, with a distance of up to 2 km between the measurement node and the gateway, the reliability measured by the _packet reception ratio_ (PRR) was 95.5%.  
The problem of how to optimally select the number of gateways and how to optimally deploy them to achieve maximum radio coverage was not considered in the context of the above simulators. This problem is often generalized in the topic of wireless sensor networks (WSNs) as optimal sink node placement. The literature presents numerous techniques devoted to it. These are usually heuristic algorithms or computational intelligence techniques [26]. The use of machine-learning mechanisms in LoRaWAN node placement is a new approach here.
## 5 Reliable Data Forwarding Algorithm  
In a WSN that is deployed for control and monitoring purposes the reliability of a single packet is very important as critical alert messages may not be missed. Because using a protocol that requires buffering packets on a source node such as traditional TCP is not desirable, a distributed end-to-end scheme for WSNs is required to ensure reliability and availability.  
Based on the results on the importance of buffer space control, we propose a simple data forwarding algorithm. While there are other reliability schemes proposed for WSNs, such as WSN transport protocols [14; 15; 16; 17] and distributed TCP caching [35; 36], the benefit of the presented scheme is its simplicity and applicability to common wireless MAC and routing protocols. This algorithm does not make assumptions on the used routing protocols and does not introduce communication overhead.  
The reliable data forwarding algorithm relies on the co-operation and cross-layer design between MAC and routing layers. The algorithm uses anycast type forwarding and is an extension to the well known automatic repeat request scheme in which the number of repeats is not limited (ARQ-\(\infty\) scheme). Therefore, the algorithm can be utilized in any WSN that uses retransmissions. We extend the ARQ-\(\infty\) scheme by allowing recovery from a broken link and utilizing alternate links. This way, a packet is reliably forwarded to the target node unless a forwarding node fails completely, e.g. runs out of energy.  
Figure 16: End-to-end reliability in ZigBee with different buffer sizes (12 sources, 15 B/s offered load).  
Figure 17: End-to-end reliability in TUTWSN with different buffer sizes (12 sources, 15 B/s offered load).  
This simple scheme is feasible, as link failures can be assumed to be much more common than node failures.  
The algorithm is presented in Figure 18. A packet is passed to the MAC layer after the next hop selection. If the transmission fails due to link failure or because the maximum number of transmission attempts was reached, the packet is passed back to the routing layer and queued until a valid route is found. However, as the sensed values in a typical measurement network are highly redundant, it is sometimes more energy-efficient and therefore desirable to drop low priority packets than try to send them indefinitely. To allow this, the data forwarding scheme analyzes the type of the packet after the link failure and drops a low priority packet.  
To allow fast recovery from link errors, alternative links to the sink are maintained. If the transmission on the primary link fails, one of the alternative links can be used without the time consuming network scan. This way, a packet can be routed via an alternative link if the primary link is experiencing momentary interference or high load. Reactiveness in such situations can be increased by decreasing the number of transmission attempts per link e.g. by performing only one retransmission attempt. Thus, interference is detected and an alternative link is used earlier.
## 1 Introduction
We also define a set of metrics that capture the routing performance and rank the routing protocols based on their observed performance. All of this should tell us, in detail, how and why the mobility patterns affect the routing protocol performance in UAANET. Our goal in this paper is to investigate how mobility models affect the performance of UAANET in simulations. We perform this investigation in order to come up with conclusions/recommendations that provide a benchmark for future simulations of UAANET. No such benchmark exists in the literature for UAANET. Most of the work presented in the literature is a bit limited in that regard, and it only addresses relatively low mobility MANET. UAANET are very high mobility MANET and need to be addressed more specifically; which is what we are doing in the paper. Our work is based on existing protocols and models. We do not aim to propose a new routing protocol nor mobility model. We do not aim to provide mathematical analyses in the paper either. The remainder of this paper is organized as follows. Section 2 summarizes the related work. Section 3 presents the five mobility models and the routing protocols that we are considering. The evaluation metrics are presented in Section 4. Simulations and analysis are presented in Section 5, and concluding remarks are made in Section 6.
# Study on Seepage Effect of Roadway Based on Polyformaldehyde(POM) Fiber Concrete
###### Abstract  
This article presents the results of a study focused on enhancing the permeability resistance of roadways using polyformaldehyde fiber-reinforced concrete. The uniqueness of this study is its interest in polyformaldehyde fiber, which has not been widely studied in underground mining roadways, especially in relation to its impact on permeability resistance. The permeability resistance of polyformaldehyde fiber-reinforced concrete with different lengths (30 mm, 36 mm, 42 mm) and dosages (5 kg/m\({}^{3}\), 7 kg/m\({}^{3}\), 9 kg/m\({}^{3}\)) was tested by the step pressure method and seepage height method. The hydrostatic pressure and seepage height of polyformaldehyde fiber-reinforced concrete were analyzed, and the best polyformaldehyde fiber-reinforced concrete with the best permeability resistance was selected to carry out numerical simulation based on a phosphate mine in Yunnan Province. The changes in the pore water pressure, maximum principal stress, and displacement of the roadway's surrounding rock under the influence of groundwater seepage were analyzed. The results show that the addition of polyformaldehyde fiber can effectively improve the impermeability of concrete. With the increase in length and dosage, the impermeability of the polyformaldehyde fiber concrete increases first and then decreases. Under ordinary support conditions, the surrounding rock of the roadway is affected by the seepage of groundwater over time, which leads to the roadway strength's decline and creep deformation, necessitating the strengthening of the roadway's anti-drainage measures. Under conditions of reinforcement with polyformaldehyde fiber concrete, the displacement of the top of the roadway obviously reduces, which can effectively improve the permeability resistance and stability of the roadway.  
polyformaldehyde fiber; concrete; permeability; groundwater; roadway surrounding rock; seepage analysis +
Footnote †: journal: Applied Sciences
## 3 Humanoid Robot State Estimation Based on a Particle Filter
```
1:Initialize particle sets \(\bar{\mathbf{P}}_{t}=\emptyset\) and \(\mathbf{P}_{t}=(\mathbf{p}_{t}^{1},\mathbf{p}_{t}^{2},\cdots,\mathbf{p}_{t}^{Q})\);
2:for\(q=1\) to \(Q\)do
3: Update particles based on motion model: \(\mathbf{p}_{t}^{q}\leftarrow\mathbf{p}_{t-1}^{q}\);
4: Calculate particle weights: \(w_{t}^{q}\propto sim\);
5:\(\bar{\mathbf{P}}_{t}=\bar{\mathbf{P}}_{t}+<\mathbf{p}_{t}^{q},w_{t}^{q}>\);
6:endfor
7: Clear set \(\mathbf{P}_{t}=\emptyset\);
8:for\(q=1\) to \(Q\) resample particles do
9: Draw \(q\) from \(\bar{\mathbf{P}}_{t}\) with probability \(\propto w_{t}^{q}\);
10: Add \(\mathbf{p}_{t}^{q}\) to \(\mathbf{P}_{t}\);
11:endfor
12: Return \(\mathbf{P}_{t}\);
```  
**Algorithm 1**Particle filter for humanoid robot joint estimation  
The 25D state space leads to a very high computational cost of the particle filter. This hinders estimation of joint angles for humanoid robot imitation in real time. Figure 6 shows an example sequence of humanoid robot joint angle estimation in real time using this implementation of the particle filter. As it is difficult to estimate 25 joint angles in real time directly, we estimated the joint angles of only the left and right arms, reducing the state space to 12 dimensions. In this case, the number of particles \(Q=400\). Even when only estimating 12 joint angles, a delay exists, as seen in Figure 6. From Figure 6c-e, the human motion takes about 2 s, whereas the humanoid robot needs approximately 4 s to follow the movement, with a substantial lag of approximately 2 s. Note that the computational cost would be even higher when estimating the 25 joint angles. To reduce the computation time, we use GPDM learning to construct a reduced 3D state space with a humanoid robot dynamical motion model. Then, the humanoid robot joint angles are estimated in the learned 3D state space at low computational cost.
## 2 Results and Discussion
### Volatile Fraction Analysis
The Cayenna pepper presented a high amount of \(\alpha\)-copaene and \(\beta\)-chamigrene. The latter was also found in larger amounts in the Calabrian pepper. The Jalapeno chili pepper is the only one with an amount of (\(E\))-\(\alpha\)-bergamotene greater than 1%. Furthermore, the Cayenna Impala variety presented a higher amount of 2-isobutyl-3-methoxyyrazine than the other samples. This compound was found to possess an extremely potent odor (odor threshold of 2 \(\times\) 10\({}^{-6}\) mg kg\({}^{-1}\)), similar to that of fresh green bell peppers [15]. Regarding _Capsicum bacactum_ chili peppers, a great contribution to the volatile profile is given by alcohols (\(n\)-hexanol, (\(E\))-3-hexenol, (\(E\))-2-hexenol, (\(Z\))-2-butten-1-ol); aldehydes ((\(E\))-2-hexenal, \(n\)-hexanal, 4-methyl-2-pentenal); and esters (ethyl hexanoate, isobutyl 8-methylnon-6-enoate, 4-methylpentyl 3-methylbutanoate, 4-methylpentyl 3-methylbutanoate, ethyl lactate) (Table 2, Table S1 and Figure 3). The Aji variety has higher ketones, esters, and terpenents contents than the other chili peppers of the same species. The high percentage area encountered for terpenes, ketones, and esters is relative to (\(E\))-\(\beta\)-ocimene, 3-pentenone, and isopropyl acetate, respectively. The Erotic and Jimmi varieties are similar in respect to the amount of aldehydes, ketones, and hydrocarbons identified. Compounds, such as \(\alpha\)-ionone and \(\beta\)-ionone, which may be formed by oxidative degradation of \(\delta\)-carotene, \(\beta\)-carotene, and terpenoids [16], were particularly found in orange peppers belonging to this third species. Although the literature reports several studies on the aroma and the content of capsaicinids in chili peppers [17], to the best of our knowledge, capsaicinids [18; 19] and the volatile profile of chili-pepper-flavored extra virgin olive oil have not been investigated well [19]. For this reason, a study on the aroma profile of flavored EVOOs was carried out and compared to that of fresh peppers and unflavored olive oils. Table S2 reports the results of the volatile compounds identified in the flavored olive oils.
## 4 Applications in Optimization
### Particle Swarm Optimization (PSO)  
Particle Swarm Optimization is a population-based evolutionary algorithm inspired by simulations of social behaviour instead of the survival of the fittest individual. PSO is randomly initialized with a population of individuals, which correspond to potential solutions. Unlike the most evolutionary algorithms, each particle in PSO is associated with a randomized velocity that \"flows\" through the problem space. In PSO, a uniform probability distribution is used to generate random numbers. However, the use of other probability distributions may provide improved fine-tuning or even allow the algorithm to escape from local optima, such as the distributions presented in [60]. In the meantime, the use of chaotic sequences has been proposed to generate random numbers for updating the velocity equation.
# A Comprehensive Survey for Deep-Learning-Based
## 4 Abnormality Detection with Images over Long Range
### The Application of Satellites in Smart Grid Abnormality Detection
#### 4.1.2 Optical Satellite Images  
Remote sensing usually refers to the monitoring of the earth's surface by sensing and remote sensing, and resource management (such as trees, grassland, soil, water, minerals, farm crops, fish, and wild animals) through telemetry instruments on platforms such as artificial earth satellites and airplanes. As for optical remote sensing, this mainly refers to the remote sensing technology in which the working band of the sensor is in the visible light band, that is, the range of 0.38 to 0.76 microns. With the rapid development of remote sensing detection technology, the spatial resolution, time resolution, and spectral resolution have significantly improved, providing a more accurate data source for earth observation. The spatial resolution of satellite imagery has gradually increased from the meter level to the sub-meter level. The time resolution also realizes the fastest revisit cycle once a day with the combination of constellation satellites. The continuous improvement of the resolution of remote-sensing satellites provides more reliable, more accurate, and richer data for earth observation.
# Prediction of Residual Stress of Carburized Steel Based on Machine Learning
## 4 Results and Discussion
### Comparison of Measured and Predicted Values of Retained Austenite  
To verify the effect of metallographic image segmentation, the residual austenite was measured with an Italian GNR STRESS-X residual stress analyzer, and the results were compared with the predicted values. Figure 8a,b presents the carburized layer at 0-200 \(\upmu\)m, Figure 8c,d shows the carburized layer at 0-300 \(\upmu\)m, and Figure 8e shows the carburized layer at 0-400 \(\upmu\)m with the residual austenite. The differences between the measured and predicted values range between approximately 1% and 3.5%. This finding is attributed to the fact that in a carburized sample, the carbon content of the surface layer is a hypereutectoid carburized area, and white carbide will appear mixed between the needle-shaped martensite and residual austenite, as shown in Figure 8f. The precipitated carbides are observed, and the precipitated carbides on the surface layer are calculated by image segmentation to account for 1%-3.5%. To eliminate the influence of surface carbides, Figure 8a-e demonstrates that deep learning exhibits a high accuracy rate for image segmentation in the areas after the subsurface of the sample, and the error between the measured and predicted values of residual austenite is very small. An increase in the carbon content in the steel reduces the temperature at which martensite begins to form, leading to austenite stabilization and increasing the amount of retained austenite. The carbon content of the carburized layer gradually decreases from the surface to the center; therefore, the amount of retained austenite in Figure 8 gradually decreases from the surface to the center.  
Figure 8: _Cont._  
Figure 7: The segmentation effect of SegModel-MOS: Acicular martensite (black), retained austenite (red), and lath martensite (green). (**a–f**), the images range from the surface of the carburized layer to the core; (**h–m**), SegModel-MOS model segmentation picture.
# Energy Management Scheme for an EV Smart
## 6 Results and Discussion
### DG Power Fluctuation Case  
In this case, the DG changes its mean output power \(P_{dg}=150\) kW with a variation factor \(\Upsilon_{dg}\) of \(\pm 50\%\). The smart charger participates in V2G and G2V action to compensate for the power mismatches and keep the grid power \(P_{g}\) constant as shown in Figure 13. The load requirement is fulfilled from the  
Figure 12: A comparison of power allocation schemes. (**a–d**) Reference method discharging and charging. (**e–h**) Proposed method discharging and charging.  
DG, the grid and the EVs. Since the power transfer from the grid varies under DG power fluctuation, the load factor is calculated to be 57.14% without smart charger application. With V2G/G2V support, the load factor becomes 100%.  
Figure 14 shows that the change in power angle magnitude \(|\Delta\delta|\) surpasses the limit \(|\Delta\delta|_{max}\) of 0.0125 rad set by the DSO without a smart charger, while \(|\Delta\delta|\) stays approximately zero with EV support.  
The EMS calculates the active power reference \(P_{(inv)}^{*}\) to be given to the controller of the smart charger inverter, and at the same time, it checks the status of the EVs and calculates the power references for the EVs. Figure 15a shows a comparison of power supplied and consumed by the EVs for the two power allocation approaches during the DG output variance. The proposed method provides better coordination among the EVs and manages the energy appropriately, as compared to the reference technique. Figure 15b,c shows that during V2G, the \(EV_{2}\) is employed in the reference method till it touches the 20% low SoC boundary, and after that, the smaller \(EV_{2}\) is engaged and its SoC depleted to 44%. Subsequently, during the G2V period, only \(EV_{2}\) charges to 57% SoC, and \(EV_{1}\) is unable to participate.  
On the contrary, with the proposed method, both EVs are employed in accordance with their on-board battery energy and show improved SoC results at the end of the simulation period. Especially \(EV_{1}\) gets to participate in the G2V operation, and its final SoC is 60%, indicating an improvement from the reference case. The final SoC differences for \(EV_{2}\) is marginal for both schemes. Table 4 demonstrates that the proposed scheme is a distributed strategy, with the application of each EV in the aggregation indicating a utilization factor of 100%.  
Figure 14: Power angle variation magnitude with and without the smart charger.  
Figure 13: Three-phase active powers of the grid, DG, load and smart charger showing V2G and G2V action.