esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion  
The proposed method for detecting dead chickens in broiler farms utilizes a two-stage approach incorporating dynamic frame selection and knowledge distillation, resulting in significant improvements in accuracy, efficiency, and practicality compared to traditional methods. This combination effectively addressed the challenge of the absence of explicit annotations for dead chickens and the need for real-time performance in resource-constrained environments.  
The dynamic frame capture strategy enhanced the computational efficiency by reducing the number of processed frames without sacrificing detection accuracy. By adapting capture intervals based on the age of the chickens, the system optimized resource usage, which is crucial in large-scale farm operations. This allows for the less frequent monitoring of active younger chickens while closely observing older, more inactive birds.  
The two-stage architecture, combining stationary object detection with a knowledge-distilled vision transformer classifier, was crucial for both performance and efficiency. Given the lack of annotated data for dead chickens, traditional image processing was first used to detect stationary regions, reducing computational load. However, since not all stationary objects are dead chickens, the second stage applies a classifier to confirm whether the detected object is a chicken, improving accuracy. For the classifier, EfficientNet-B0, as the teacher model, provided robust feature extraction, while DeiT-Tiny, the student model, achieved comparable accuracy (98.7%) with lower computational demands. Knowledge distillation retained much of the teacher's performance while optimizing the system for real-time deployment.  
Performance evaluations demonstrated that EfficientNet-B0 outperformed other teacher models like ResNet-50 and EfficientNet-B1, reinforcing its selection as the teacher model. Among student models, DeiT-Tiny proved most computationally efficient, with faster inference times and smaller memory requirements than DeiT-Small and DeiT-Base.  
The system's lightweight architecture supports deployment on inexpensive hardware while maintaining high accuracy. The dynamic frame selection mechanism enhances efficiency by reducing unnecessary processing and focusing on critical frames, thus minimizing the need for labor-intensive manual inspections. This scalable approach improves overall farm management and animal welfare, making it well suited for commercial broiler farms.
## 2 Quantum Key Distribution
### Photon Number Splitting (PNS) Attacks  
The PNS attack is a powerful attack designed to take advantage of the multiphoton vulnerability in order to obtain a full copy of [PERSON] and [PERSON]'s shared secret key bits without introducing errors and thus increasing the QBER [19; 20]. A brief introduction to the PNS attack is given here, with a detailed, yet easily understandable engineering-oriented explanation available in [9].  
Figure 2 provides a simplified depiction of the eavesdropper \"[PERSON]\" conducting a PNS attack against the QKD system (i.e., [PERSON] and [PERSON]). In accordance with QKD security proofs, [PERSON] is an all-powerful adversary limited only by the laws of quantum mechanics [4]. She is allowed full control of the quantum channel to introduce losses or errors and may eavesdrop on, but not fabricate, messages exchanged on the classical channel. In order to conduct the PNS attack, [PERSON] replaces the quantum channel with a quantum teleportation channel which enables the lossless transmission of photons from [PERSON] to [PERSON] using the properties of entangled quantum systems [21]. An [PERSON]\({}^{\prime}\) entity is also required in close proximity to [PERSON] to regulate the lossless transmission of photons as to not exceed [PERSON]'s expected detection rate; thus, avoiding obvious detection.  
For each pulse [PERSON] generates, [PERSON] performs a specialized Quantum Non-Demolition (QND) measurement to determine the number of photons in each pulse \(n=0,\ 1,\ 2,\ 3,\dots,\ \mathrm{N}\)[22]. If \(n\leq 1\), [PERSON] blocks the pulse and sends nothing to [PERSON]. If \(n\geq 2\), [PERSON] splits one photon from the pulse and stores it in her quantum memory. She then quantum teleports the remaining \(n-1\) photons to [PERSON]. This attack scheme allows [PERSON] to store an identical encoded copy of each photon sent to [PERSON] without introducing additional errors (which are typically used for detecting eavesdroppers). Once [PERSON] and [PERSON] complete their quantum exchange, they must announce measurement basis information over the classical channel where [PERSON] is able to listen. [PERSON] can then correctly measure each stored photon, and thus, obtain a complete copy of the QKD-generated \"secure\" key bits.
## 2 Materials and Methods  
### Feedback and Inoculum Characteristics  
Sugar beet by-products were a mixture of exhausted pulp (85%) and molasses (15%). They were provided by the sugar processing company in the south of Spain (El Portal, Jerez de la Frontera, Andalusia). The used SBB have a total solids content of 80-90%, which was adjusted to 8% of TS to avoid the non-homogenization of the reactor content due to the rheological behavior of SBB [24]. For this purpose, the SBB as dried pellets were rehydrated with deionized water for 24 h previously to their use. Cow and pig manure were collected from two different livestock facilities in the same zone. The inoculum for the start-up of the single AD of SBB (Inoc1) was provided from a laboratory-scale reactor under semi-continuous operation that was fed with the organic fraction of municipal solid wastes (OFMSW), which was first adapted to the new substrate SBB for a long period before starting the herein studied assays [25]. The effluent from the reactor of single digestion of sugar beet by-products was used as inoculum for the start-up of co-digestion assays (Inoc2). In co-digestion reactors, the mixture between the two co-substrates has been established as 25% of SBB and 75% of pig manure or cow manure (v/v) [26; 27]. The SBB, CM, PM, and inocula characteristics are presented in Table 1.  
The organic content of SBB was the highest since the total solids were almost all volatile (90%). The VS content of manure was also higher (75-77%) with a high fraction of nitrogenous material. The C/N ratio of SBB was much higher than for animal manure, which led us to expect a complementarity between the two kinds of substrates in terms of carbon and nitrogen contents. Concerning the lignocellulosic content of SBB and manure, all of them could be considered as lignocellulosic substrates with a high cellulose fraction in SBB and CM. Moreover, the mineral content of PM and CM is much higher than for SBB. Trace elements are also necessary for the AD process and are considered to stimulate the digestion of cellulosic material [26]. Therefore, manure are natural sources of trace elements and their mixture with SBB gives rise to an appropriate medium for the growth of microorganisms. [PERSON] et al. [28] reported that supplementation of trace elements in anaerobic reactors leads to stable and more efficient methane production processes while deficits in these elements may cause process imbalances. Both manure have a high mineral content and important alkal
# The Contributions of Climate and Human Activities to Water Use Efficiency in China's Drylands
## 4 Discussion
### Factors in the Spatial Drivers of WUE  
The trend of the spatial distribution pattern for WUE was consistent with the findings of previous studies [20]. The results of the factor detection analysis revealed that the top three factors contributing to the spatial variation in WUE in the five time periods were Elev, Geomt, and Soil. These findings indicated that topographic factors and soil conditions played a major role in the spatial variation in WUE in the drylands of China. Different soil types possessed varying soil water contents, soil organic matter, and soil textures. Previous studies have demonstrated that soil water content and organic matter are crucial drivers of WUE [50]. The application of organic matter was a significant factor in determining WUE, and the combined influence of these factors rendered WUE more sensitive to Soil. Meanwhile, the results of the interaction detection demonstrated that the factors Geomt and Soil, as well as Soil and Elev, had the greatest explanatory power, which suggested that the integrated conditions of topography and soil were the primary drivers of the spatial differentiation of the WUE driving force. In addition, among the climatic factors, Tem, Solra, Relah, and VPD had more substantial impacts on WUE. However, Prec did not emerge as the primary determinant of spatial WUE divergence in China's drylands, indicating that the influence of the Tem gradient on GPP and ET may offset the moisture stress-induced spatial WUE [20]. Among the anthropogenic factors (GDP, POP, and Lanct), Lanct appeared to exert the most significant impact on WUE, and there was a discernible upward trend over time, suggesting that the impact of human activities on the spatial disparities in WUE was progressively intensifying. However, we recognize that the interactions among the dominant factors of spatial differentiation are subject to further future research.
## 3 Network Coding Enabled Network Tomography
### Loss Estimation
Additionally, the design of the probe coding schemes is modified: coding operations over a larger alphabet and coding coefficients satisfying specific constraints are introduced in order to ensure path identifiability, which refers to the ability of uniquely mapping each possible observation (received probes at all receivers) to the state of the paths (i.e., which paths operated during the measurement period and which failed). Assuming a set \(\mathcal{I}\) of links, the goal is to estimate the success probability for all links in \(\mathcal{I}\) at the minimum bandwidth cost, given a set \(\mathcal{S}\) of nodes that can act as sources, a set \(\mathcal{R}\) of nodes that can act as receivers, and the set \(\mathcal{I}\) of links whose success rates are of interest. This optimization problem can be solved in polynomial time using _linear programming_ (LP), whereas the minimum cost routing problem when performing tomography with multicast trees (MTs) is NP-hard. To ensure identifiability in that case, the notion of _conceptual flows_ is introduced: each path corresponds to a flow of fixed rate \(\rho\). To ensure minimum cost, these flows have to use the minimum resources possible. If the success rate of _all_ links is to be estimated, then the scheme used is simpler and there is no need to solve the above LP problem: each source sends a probe, and each intermediate node forwards a combination of its incoming packets to its outgoing edges. Thus, each edge of the graph is utilized exactly once per time slot and the required total bandwidth is minimum. An orientation algorithm is proposed. It requires some flexibility regarding the selection of source nodes and receiver nodes, and it leads to the construction of an acyclic graph with a small number of receiver nodes (this is desired for efficient data collection), which can be represented as a factor graph where BP estimation algorithms can be applied. The proposed algorithm guarantees the identifiability of all links in a general undirected graph consisting of logical links, for any choice of sources. Another challenge regarding general topologies besides the presence of cycles rises from the fact that the design of the code affects identifiability. Therefore, the authors present a result determining the lower size of the alphabet (i.e., probe size), which ensures path identifiability. However, having a large alphabet size is necessary but not sufficient to guarantee path identifiability. It is also crucial to assign coefficients so that the failure of every subset of paths leads to a distinct observable outcome (received probe content). Finally, the authors employ a low complexity algorithm that offers fast estimation of link loss rates, that is a suboptimal BP approach. In the following section, we shift our focus on tomographic techniques based on compressed sensing, which, as already mentioned, constitute an independent family of network tomography that could be used in combination to other approaches.
# Research on Rapid Recognition of Moving Small Targets by Robotic Arms Based on Attention Mechanisms
## 5 Conclusions  
This paper improves the issues of low efficiency and high rate of missed detections and false pickups of a robotic arm when trying to identify and grasp small high-speed moving targets, based on the attention mechanism. The improved algorithm in this study not only enhances the accuracy and stability of tracking, but also enables the robotic arm to perform adaptive target tracking in complex and dynamic environments.  
The method of machine vision is used in this study to allow a robotic arm to quickly grasp the target. The identification effect is significantly improved by modifying the YOLOv8 algorithm. The model's generalization ability is enhanced by means of image enhancement, while the introduction of the EMA attention mechanism greatly increases the model's success rate in recognizing moving small targets. In addition, local optima are avoided by improving the optimization of the loss function.  
Finally, the experimental results on the small sample conveyor belt item dataset show that the improved YOLOv8 network proposed in this paper, based on the attention mechanism, can quickly and accurately identify targets. The statistical data of the experimental performance validate the effectiveness of the proposed method.  
Conceptualization, B.C. and A.J.; methodology, B.C.; software, Aishan jiang; validation, J.S., B.C. and A.J.; formal analysis, B.C.; investigation, A.J.; resources, J.S.; data curation, B.C.; writing--original draft preparation, A.J.; writing--review and editing, A.J.; visualization, A.J.; supervision, J.S.; project administration, J.L.; funding acquisition, J.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data are contained within the article.  
The authors declare no conflicts of interest.
# State Estimation for DC Microgrids using Modified Long Short-Term Memory Networks
## 7 Discussion of Performance Evaluation
The DC bus with the real simulated voltage measurement has the highest weight value, while the DC bus with pseudo-measurement has the lowest weight value. The addition of these weights further enhances the estimation output of the LSTM-based method. The computation time of each state estimator is shown in Table 3. The training time is the computation time in the offline training stage for the deep-learning-based estimators, while the testing time is the computation time required for each state estimator to estimate the voltage magnitude of DC buses to its highest accuracy in the online testing stage. Based on the table, overall, it took longer for the conventional WLS state estimator to estimate the voltage magnitude of DC buses. For the deep-learning-based estimators, although the training processes took quite some time, the computation times in the online testing stage are significantly faster than the conventional WLS state estimator. The modified LSTM-based state estimator has the longest training time, followed by the normal LSTM-based state estimator and the MLP-based state estimator. Looking at the structure of the network, the LSTM network has a more complex structure compared to the MLP network. Consequently, the computation times for LSTM-based state estimators are longer than the MLP-based state estimator. The implementation of the proposed state estimator as the deep learning method in the real power system might have some drawbacks. The deep-learning-based state estimator need to be trained with a lot of measurement data, as this is necessary for good approximation of the system and is generally not possible for bulk power systems. Additionally, for topology changes in the system, the learning process for SE needs to be repeated. However, for microgrids, the acquisition of measurement data is rather easy, as the metering technology has become more advanced. Consequently, this makes the modified LSTM-based SE more applicable for microgrids systems. Overall, based on the analysis, in terms of accuracy and computation time, the proposed modified LSTM-based state estimator has the best results. This proves the superiority of the proposed method in estimating the DC microgrid's state compared to the other state estimators tested in this paper.
## 5 Material and Methods
### Fieldwork
#### 5.1.2 The Recording of Benzu Cave  
Several factors limited data collection in Benzu cave. First, the total lack of natural light could not be solved by installing artificial light sources, owing to the small distance between floor and ceiling. Photogrammetry was ruled out for the interior of the cave, as well as the use of GNSS which proved impossible to install. Therefore, the cave could only be recorded through 3D scanning (Figure 11A, Appendix A). Only safe stations were chosen, so some small areas could not be recorded (Figure 11B, Appendix A). Fortunately, these small 'gaps' could be filled with information from earlier topographical maps [26].  
Difficult access conditions also prevented the technician from leaving the cave during scanning to avoid the presence of foreign elements. For this reason, the top point cloud resolution was selected (1.9 mm-10 m accuracy and a level of noise of 0.4 mm at the same distance), 360 degrees scanning, VIS technology for real-time recording, and double scanning, which allows the suppression of all elements that do not match in both takes. This doubled scanning time but ensured the absence of interferences.  
Despite the virtual absence of natural light inside the cave, scanning was undertaken when the sun was not visible through the entrance to avoid saturations and strong light contrasts in the use of 360\({}^{\circ}\) photographs. To procure a minimum amount of light, a small lamp was installed in the bottom part of the scanner. Since the main goal was to generate an up-to-date plan of the Benzu cave, the main aim was to obtain a good outline of its boundaries and so the almost total lack of light was not a problem.
## 2 System Model  
The scenario under consideration in this paper consists of \(U\) wireless EH-STs, where EH-STs send data packets via a shared wireless channel to a GEO satellite, as shown in Figure 1. These EH-STs are placed in the outdoor field to monitor the environment and post the acquired data to the satellite [32]. The data packets received by the satellite are forwarded to the ground gateway station transparently with independent backhaul channels, which are assumed to be error free. The energy harvested by each EH-ST from the environment is stored in a rechargeable battery for the radio frequency unit and sensing apparatus. Each EH-ST is equipped with a processing unit to manage the available energy. The following parts of this section introduce the data model, MAC protocol, energy consumption, energy storage, and energy harvesting models of the system in detail.
## 6 Data Transmission  
During the data gathering period, sensors in the WSN collect their surrounding information and send them to the base station hop-by-hop. In this section, to calculate the energy consumed by transmitting and receiving, the possible data transmission paths and their probabilities are analyzed according to the network topology and the routing protocol mentioned in Section 3.2, and the expected amount of data transmitted and received at each sensor location is derived. Moreover, the network success transmission rate, one constraint in our optimization models in Section 3.2, is computed as well.
# A Quantum-Resistant FHE Framework for Privacy-Preserving Image Processing in the Cloud
## 4 Experimental Evaluation
### Post-Quantum Cryptographic Performance  
The CRYSTALS-Kyber implementation is highly efficient across all primary operations. The time results in Table 1 demonstrate the practicality of post-quantum key setup for interactive applications, adding only a sub-millisecond overhead to the session establishment.
## 2 Methods
### Analysis of Directional Hemispherical Reflectance  
With the use of the SOC-410 Directional Hemispherical Reflectometer (USA), the assessments of THR were performed. The apparatus measures reflectance within seven wavelength bands, including 335-380 nm, 400-540 nm, 480-600 nm, 590-720 nm, 700-1100 nm, 1000-1700 nm, and 1700-2500 nm, i.e., from ultraviolet to near-infrared. The results for THR were obtained for the beam at an angle of 20\({}^{\circ}\). For each tablet analyzed, three measurements were performed.  
The calibration coupons (mirror and diffuse) certified by the American National Institute of Standards and Technology (ANIST) were the control group in our study and were used to calibrate the device. The calibration process was repeated after testing every 4 tablets, and each time we obtained the same values of reflectance for the coupons. The repeatability of the results was 99.9%. In addition, we repeated the measurements of the tablets after two weeks and found that the values of reflectance were comparable between the analyses, the difference was less than 2%.  
The accuracy of the directional reflectance measurement method using a reflectometer is below \(\pm\)3% and has been specified by the manufacturer in the entire spectral range for all materials that are not transparent or hemitransparent, and this applies to both electrics and dielectrics.