esa-sceva/satcom-chunk-collection huggingface dataset:
# Depth-Oriented Gray Image for Unseen Pig Detection in Real Time
## 6 Conclusions  
This study proposed a methodology utilizing DOG images and the Depth Anything model to achieve high object detection performance and cost efficiency even in unseen environments. The approach addresses the performance degradation issues of gray image-based detection models in complex backgrounds and unseen environmental conditions. Cost efficiency is significantly improved by generating depth information through Depth Anything without requiring additional depth sensors.  
Experimental results demonstrated that DOG images achieved up to a 6.4% increase in AP50 compared to gray images, with a processing time of 3.6 ms on a CPU approximately 53.8 times faster than the GPU-based Depth Anything's depth image generation time of 193.7 ms. These findings confirmed that DOG images resolve foreground-background distinction challenges in complex lighting conditions and backgrounds, proving their applicability in real-time object detection systems.  
Future research will focus on developing real-time video-based detection and tracking algorithms and enhancing generalization performance across diverse environments to expand the applicability of real-time monitoring systems in areas such as farm management and smart farming. Following approaches like [19], it is anticipated that generating DOG images on a CPU and processing object detection models such as YOLO on a GPU in a pipeline could enable real-time video monitoring even in embedded board environments. Furthermore, if the proposed method, initially designed for top-view operation, incorporates camera position estimation and 3D modeling for perspective transformation to a top view for the RoI setting, it is also expected to be applicable in tilted-view environments.  
Figure 10: CPU and GPU memory usage during the initialization step.  
**Author Contributions:** [PERSON] conceptualized and designed the experiments; [PERSON] wrote the paper and built the overall software; [PERSON] reviewed and edited the paper; [PERSON] and [PERSON] validated the proposed method. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by \"Regional Innovation Strategy (RIS)\" through the National Research Foundation of Korea (NRF) funded by the MOE (2021 RIS-004), the Korea Institute for Advancement of Technology (KIAT) grant funded by the MOTIE (P0024177, Development of RIC (Regional Innovation Cluster)), and the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. 2022R1F1A1062775).  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The Data are contained within the article.  
**Conflicts of Interest:** Author [PERSON] was employed by the company Info Valley Korea Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest\".
## 2 Design Aspects
#### 2.1.4 Heat Rejection System: Cooling Tower  
A heat rejection system is attached to the thermal absorption chiller in order to evacuate the heat from the absorber and the condenser of the chiller and eject it to the ambient air. In this paper, the counterflow mechanical wet cooling tower was selected with the Baltimore Aircoil Company (BAC, Madrid, Spain) [32], The selected tower is the FXT-26 model, which has the nominal operating conditions listed in Table 2 and it is capable of dissipating all the heat evacuated by the absorption chiller under any environmental conditions in the location of installation (in our case, Baghdad, Iraq). The counterflow, the forced draft-cooling tower can be modeled in TRNSYS, based on the number of transfer units (NTU) [33]:  
\[NTU=c\bigg{[}\frac{\dot{m}_{a}}{\dot{m}_{w}}\bigg{]}^{(n+1)} \tag{5}\]  
where: \(\dot{m}_{w}\) and \(\dot{m}_{a}\) are the mass flow rates of water and air, respectively, \(c\) and \(n\) are the coefficients of mass transfer constant and exponent; their values are given by the manufacturer's curves in this paper, the selected values of \(c\) and \(n\) are 0.5 and \(-\)0.856 respectively.
## 4 Experiments and Results
### Experimental Results
#### 4.4.1 Results and Analysis on the Vaihingen Dataset  
In our meticulous evaluation on the Vaihingen dataset, the proposed GLSNet was quantitatively compared to several state-of-the-art approaches, including both transformer-integrated models such as BSNet [9], Mask2 Former [23], and TransUNet [44], as well as traditional segmentation models like PSPNet [10], S-RA-FCN [45], DeepLabv3 [6], CCEM [46], RefineNet [13], EncNet [14], SegNet [11], UNet [8], and FCN-8s [45]. The assessment metrics included segmentation accuracy for large objects, small objects, and backgrounds, along with overall metrics such as mIoU, mean F1, and OA.  
Table 2 presents a detailed comparison of these methods. GLSNet demonstrates superior performance across the overall metrics, with a particularly notable mean F1 score of 90.2%, an OA of 90.9%, and a mIoU of 90.9%. These results showcase the effectiveness of our model in segmenting various object categories with high precision. For instance, GLSNet outperformed BSNet by achieving a 2.3% improvement in accuracy for impervious surfaces and showed a significant enhancement of 3.7% for cars. It is also worth noting that  
GLSNet's performance exceeds that of the popular Mask2 Former method introduced in recent years. When compared with Mask2 Former, which has gained significant attention for its innovative approach to segmentation, GLSNet demonstrates a higher mean F1 score and overall accuracy, indicating its robustness and potential for practical applications in scenarios requiring precise segmentation of ultra-high resolution images.  
However, we also observed that in scenarios with complex backgrounds or low-contrast objects, GLSNet's segmentation accuracy is slightly lower than in scenarios with clear object boundaries and distinct features. This observation indicates the necessity for enhancing the model's ability to adapt to challenging conditions. One potential avenue for improvement is expanding the diversity of the training data to include more examples of difficult-to-segment objects, which could help the model learn to better distinguish between similar textures and backgrounds.  
In conclusion, the Vaihingen dataset evaluation highlights GLSNet's strengths in accurately segmenting a wide variety of objects. However, the analysis also reveals areas for improvement, particularly in the category of low vegetation, where GLSNet's performance was less pronounced compared to other object categories. This suggests that the model faces challenges in distinguishing objects with similar textural features from their surroundings. Addressing these challenges, such as enhancing the model's adaptability to complex segmentation tasks and optimizing memory usage without affecting accuracy, will be pivotal in guiding the future development of GLSNet. Our commitment to refining these aspects is aimed at achieving even higher standards of performance and efficiency.
# CRC-Based Reliable WiFi Backscatter Communication for Supply Chain Management
## 2 System Model  
The system model for the CRCScatter system consists of an excitation source, a tag, and a single-AP receiver, as shown in Figure 2. The ambient excitation source broadcasts exciting signals to the backscatter tag. The tag transmits its data by backscattering and modulating the exciting signal. The CRCScatter receiver uses only one access point to receive the backscatter signal. Considering tag data transmission, tag data are encoded on the 802.11b WiFi signal by codeword translation, and the receiver uses backscatter packets to reverse original packets and decode the tag data. In this section, we present how the tag piggybacks data on a backscatter signal. This paper focuses on the 1 Mbps data rate, and other data rates will be implemented in the future work.
# CERP: Cooperative and Efficient Routing Protocol for Wireless Sensor Networks
## 4 Cooperative Efficient Routing Protocol Formulation
### Routing Mechanism for Proposed Cooperative Efficient Routing Protocol (CERP)  
CERP aspires to enhance network longevity and fortify its reliability as its paramount objective. The proposed protocol's architecture unfolds in three pivotal steps, as elucidated in Figure 4:  
1. Initial introduction and identification: in the initial phase, the cluster head (CH) presents itself to all other nodes within the cluster, identifying their neighbors. Although cluster formation is not the primary focus of this work and is presumed to pre-exist, each node submits its unique identifier to the CH for incorporation into the nodes matrix.
2. Execution of the preferred relay selection algorithm: detailed in Section 4.3, the CH calculates the preferred relay selection algorithm (PRSA) for each newly incorporated node \(N_{i}\), where \(i=1\ldots N-1\). The aim is to discern the most optimal and energy-efficient next-hop for forthcoming transmissions, enabling node \(N_{i}\) to attain a specific bit error probability (BEP), denoted by \(P_{e_{t}h}\), while optimizing power utilization in its transmissions.
3. Construction of the routing table: the identifier of the chosen relay is stored in a node's routing table, which is sustained by the node itself. Consequently, to transmit a packet to the CH, a node initially dispatches it to its favored relay within the tree. Subsequently, the packet is sent through a series of preferred relays until it reaches the CH. As a further step, we will put the suggested protocol into practice on an Arduino-based SN and test it to ensure it works as intended while gauging its dependability and power consumption.
# Safety Analysis of Secondary Lining of Yulinzi Tunnel Based on Field Monitoring
## 3 Data Monitoring  
### Monitoring Program  
To evaluate the safety of the secondary lining of the tunnel, we further calculated the axial force and bending moment of the secondary lining by monitoring the reinforcement stress of the secondary lining structure and finally obtained the safety factor of the secondary lining. To make the monitoring structure representative, the secondary lining structure of the section with poor surrounding rock conditions was selected for the layout. The monitoring sections selected were the positions of ZK280+325 and ZK280+328 at the entrance of the left hole, as shown in Figure 2.  
To truly reflect the internal force of different areas on the same section of the secondary lining structure, steel bar stress sensors were, respectively, arranged at the vault, arch shoulder, arch waist, and arch foot of the tunnel. When arranging the steel bar stress meter, it is necessary to arrange sensors at the corresponding positions of the steel bars on both sides of the section. There were 26 measuring points in the two monitoring sections, 6 in the vault area, 8 in the arch shoulder area, and 12 in the arch foot side wall area, as Figure 3 shows.  
Figure 1: Field survey photos.  
The sensor used in this measurement was the intelligent string-type steel bar stress gauge JMZX-416 AT, which is mainly used in the steel bar stress measurement inside the reinforced concrete structure. For data collection, a comprehensive collection module with data receiving and storage functions and a DTU wireless transmission module were adopted. The data were exported through the comprehensive collection module and then sent to the user's computer through the wireless transmission module, which could realize the remote monitoring of data.
## 4 Simulation and Analysis
### Comparison of HD-VLL-NCO and HD-VLL  
The HD-VLL-NCO is presented for the implementation of the HD-VLL, as discussed in Section 3.3. To demonstrate the effectiveness of the HD-VLL-NCO, the HD-VLL and HD-VLL-NCO are simulated and compared.  
The same sinusoidal motion parameters as those used in Section 4.1 are assumed, and a bandwidth of 5 Hz is adopted. The sampling frequency \(f_{s}\) is 17 MHz, the lookup table bit width \(N_{0}\) is 12, the velocity resolution \(\Delta v_{d}\) is 0.001 m/s, the acceleration resolution \(\Delta a\) is 0.1 m/s\({}^{2}\), and the jerk resolution \(\Delta\dot{a}\) is 1 m/s\({}^{3}\). Hence, according to (25), the truncation widths \(c_{0}\), \(c_{1}\), and \(c_{2}\) are set as 20, 17, and 21, respectively. The velocity estimate curves and velocity error curves of the two structures are shown in Figure 17.  
It can be seen that the HD-VLL-NCO has almost the same tracking results as the HD-VLL. Therefore, in order to preserve the multiplexing of the filter module, the HD-VLL-NCO can be adopted in practical engineering implementation, and the accuracy requirement can be realized by setting appropriate truncation widths.  
Figure 16: **The PLL tracking results of the HD-VLL-SA-PLL and VLL-SA-PLL. (a) The PLL discrimination curves. (b) The PLL filtering curves.**  
Figure 17: _Cont._
# Research on Model-Based Fault Diagnosis for a Gas Turbine Based on Transient Performance
###### Abstract  
It is essential to monitor and to diagnose faults in rotating machinery with a high thrust-weight ratio and complex structure for a variety of industrial applications, for which reliable signal measurements are required. However, the measured values consist of the true values of the parameters, the inertia of measurements, random errors and systematic errors. Such signals cannot reflect the true performance state and the health state of rotating machinery accurately. High-quality, steady-state measurements are necessary for most current diagnostic methods. Unfortunately, it is hard to obtain these kinds of measurements for most rotating machinery. Diagnosis based on transient performance is a useful tool that can potentially solve this problem. A model-based fault diagnosis method for gas turbines based on transient performance is proposed in this paper. The fault diagnosis consists of a dynamic simulation model, a diagnostic scheme, and an optimization algorithm. A high-accuracy, nonlinear, dynamic gas turbine model using a modular modeling method is presented that involves thermophysical properties, a component characteristic chart, and system inertial. The startup process is simulated using this model. The consistency between the simulation results and the field operation data shows the validity of the model and the advantages of transient accumulated deviation. In addition, a diagnostic scheme is designed to fulfill this process. Finally, cuckoo search is selected to solve the optimization problem in fault diagnosis. Comparative diagnostic results for a gas turbine before and after washing indicate the improved effectiveness and accuracy of the proposed method of using data from transient processes, compared with traditional methods using data from the steady state.  
fault diagnosis; gas turbine; transient performance; cuckoo search; modeling and simulation; deterioration 2018 8, 148; doi:10.3390/app8010148
## 5 Current Treatments to Avoid or Reduce PPO Activity in Fresh-Cut Produce
### Electromagnetic Radiations/Ultrasonication
#### 5.7.1 Gamma Irradiation  
Irradiation has been proved to be effective for inactivation of microorganisms, especially in vegetables such as lettuce, onions or carrots, increasing their shelf-life. Its effectiveness has also been studied for fresh-cut produce, where the decrease in PPO activity and microbial growth are always related, as explained in previous sections, possibly due to their common starting point when it comes to the degradation of fresh-cut produces (cell lysis).In this case, gamma-ray irradiation was effective for reducing both microbial growth and PPO activity (especially at the beginning of storage) in fresh-cut celery, with 1.0 kGy being the most effective dose and keeping global quality of this product up to 9 days of storage [127]. Similar results were found in fresh-cut lettuce, with 1.0 kGy being the most suitable dose to keep global quality, including vitamin C content or PPO activity [128]. In the case of fresh-cut red beet, low doses of gamma irradiation (1.0 and 2.0 kGy) were used, and the latter showed a PPO activity increase [41]. As a consequence, in most of these studies, low irradiation doses are recommended (pointing out, again, the parameter optimization for each case, as in the case of abovementioned treatments), andadditional treatments are suggested in order to control tissue senescence, being effective especially at the beginning of storage.
## 4 Implementation and Operations
### Providing Synchronous Access and the Node-Centric Communication  
If a physical sensor receives an URGENT packet, it promptly reports the requested sensing data using a GET packet. When an URGENT packet is used, _SyncSlice_ is allocated instead of the ordinary _Slice_. Since the _SyncSlice_ class is inherited from both Observer and _Slice_ classes, it can act as not only _Slice_, but also the Observer. It means that the GET packet fired by the URGENT packet is directly delivered to the corresponding _SyncSlice_ and then the _SyncSlice_ promptly reacts to process the packet. With this synchronous access mechanism, external sensor applications can get the latest data in the case of the query-driven reporting model. Additionally, the GET packet is also delivered to the _SensorPool_ and it is used to update the corresponding _VirtualSensor_'s data for efficiency.  
Figure 10: Dynamic action loading with the Abstract Factory pattern and the C\(++\) dynamic loading.  
When an external sensor application needs to bypass TinyONet and directly communicate with physical sensors, it can use the BYPASS request as follows:  
[BYPASS] [BYPASS_custom data]  
When TinyONet receives this request, it allocates a _SyncSlice_ to handle the BYPASS request and then the _SyncSlice_ sends the [BYPASS_custom data] to the target physical sensor using a BYPASS packet. Reversely, if a physical sensor needs to bypass TinyONet, the sensor can also use a BYPASS packet. If a BYPASS packet is transmitted from physical sensors, it is delivered to the listening _SyncSlice_ that is connected to the requesting external sensor application. Then, the _SyncSlice_ just relays the packet's data to the application. During the process, the _SyncSlice_ does not touch but just relay the data between two networks. Comparing to the synchronous access using the URGENT/GET packets, a BYPASS packet does not update _VirtualSensor_'s data because the BYPASS packet is designed to provide a direct passage that detours around TinyONet.
## 6 Overall Hardware Implementation
### Scheduling Diagrams  
In this part, based on the proposed model (equations), the final scheduling diagrams are created. In this approach, we have two basic variables (\(V\) and \(n\)) which are scheduled as two main hardware routes. For considering the proposed model in hardware form, it is essential that the hyperbolic terms of this model is reformulated (as a discretized form) as below:  
\[Func(V[i])=0.6[\frac{2^{0.65V[i]}-2^{-0.65V[i]}}{2}]+0.6[\frac{2^{0.65V[i]}+2 ^{-0.65V[i]}}{2}]-0.5 \tag{21}\]  
For implementing this function, the power2-based approximation can be used [68]. Generating the exponential functions (EXP. Unit) with powers of 2, is the key idea of this approach, which is realized by a logic shift. Replacing multipliers with logic shift operations leads to a significant low-cost hardware realization. As a result, in this approach, the hyperbolic terms will be achieved. As depicted in Figure 7, this hyperbolic function can be created.  
In the next step, using this \(FUNC(V)\) hardware, the hardware of the internal functions (\(F(V)\), \(G(V)\)) are realized, based on the following discretized equations:  
\[F(V[i])=\left\{\begin{array}{l}a_{n}(Func(V[i]))-c_{n}\quad;\quad V[i]<0\\ -a_{p}(Func(V[i])-4V[i]b_{n})+c_{p}\quad;\quad V[i]>0\end{array}\right. \tag{22}\]  
Figure 7: Realizationof the proposed \(FUNC(V)\) function.  
\[G(V[i])=\left\{\begin{array}{ll}k_{n}(Func(V[i]))+q_{n}&;&V[i]<r\\ k_{p}(Func(V[i]))+q_{p}&;&V[i]>r\end{array}\right. \tag{23}\]  
These two internal terms are realized, as can be depicted in Figure 8.  
Consequently, using the above internal functions, the scheduling diagrams of the proposed model can be designed based on the following discretized equations:  
\[V[i+1]=dt*\frac{\phi}{\tau}(F(V[i])-n[i]+I_{0}+I_{Stimulus})+V[i] \tag{24}\]  
\[n[i+1]=dt*\frac{1}{\tau}(G(V[i])-n[i])+n[i] \tag{25}\]  
The scheduling diagrams of the proposed equations are depicted in Figure 9.  
As can be seen from the scheduling diagrams, the proposed neuron model can be implemented as a digital system in a multiplierless state. All of the proposed terms and equations of the implemented model have been realized based on the primary blocks such as ADDs, SUBs, and digital SHIFTs. In other words, using the proposed method, the final digital costs will be significantly reduced. This approach causes a speed-up (by increasing  
Figure 8: Realization of the proposed \(F(V)\) and \(G(V)\) functions.  
Figure 9: Scheduling diagrams for realizing the basic variables, \(V\) and \(n\).  
the system frequency) in the digital hardware that can be implemented on FPGA platforms. One of the important issues in the neural networks is the large-scale realization of the brain network. Indeed, if the maximum number of digitally implemented neurons is increased, we have a real neuromorphic hardware that is capable of reproducing brain behaviors. Consequently, the proposed model can be considered as a low-cost digital system that is used in the brain network.