esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Shot Detector: Lightweight Network Based on Deep Learning Using Feature Pyramid
## 4 Experiment Results
### PASCAL VOC and MS COCO Datasets Detections Results  
A prediction was performed with the test set of each public data set to test the detection performance of ESDet. Figure 6 shows the detection result of the PASCAL VOC 07 test, and Figure 7 shows the detection result of the MS CO-CO2017 minival set.  
The data set has different distributions of large and small objects. Objects with a fixed size proportional to the feature map size will only be detected if only a single head is used. It was confirmed that both large and small objects were detected since the proposed ESDet uses five heads with different scales. In the case of multiclass classification, the number of objects in the data set is not constant. Therefore, the performance measurement results for each class are shown in Table 6 to check the classification accuracy for each class.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**aero** & **bike** & **bird** & **boot** & **bottle** & **bus** & **car** & **cat** & **chair** & **cow** \\ \hline
86.5 & 87.5 & 87.8 & 80.9 & 62.3 & 85.4 & 87.2 & 88.4 & 68.8 & 84.5 \\ \hline
**table** & **dog** & **horse** & **mbike** & **person** & **plant** & **sheep** & **sofa** & **train** & **tv** \\ \hline
69.0 & 88.3 & 87.9 & 86.2 & 83.8 & 66.1 & 85.9 & 78.2 & 89.5 & 83.8 \\ \hline \hline \end{tabular}
\end{table}
Table 6: ESDet-baseline Performance (mAP %) on the PASCAL VOC 2007 Test Set.  
Figure 6: Detection results of the PASCAL VOC 2007 test data set. The network used in the experiment uses an input resolution of 512 × 512, and EfficientNet-B0 is used as the backbone network. (Number of classification classes: 20).  
This is the detection performance table of the proposed ESDet-baseline. Most classes has more than 80% AP. However, the bottle, plant, and table objects showed relatively low detection performance. In fact, it is difficult to detect objects when analyzing the data set because the objects are smaller or occluded than other classes. The detection result in Table 6 can be expressed as a precision-recall curve as shown in Figure 8.  
As shown in the curve graph above, the precision value of the objects mentioned above and small or occluded objects decreased as the IoU threshold increased.
# A Set of Active Disturbance Rejection Controllers Based on Integrator Plus Dead-Time Models +
## 3 Extended State Observer  
In real time control, deviations always appear between the behavior of real plants and their models. Such differences may be explained by considering impact of some equivalent disturbances acting at the plant output or input. Use of output disturbances was typical for the internal model control (IMC). Since it lead to several problems in control of integrative and unstable plants (as unobservable output disturbances of integrative plants, see e.g., [21; 41], or unbounded disturbances of unstable ones [42]), it is more appropriate to deal with input disturbances \(d\), as in ADRC. There, all the plant-model discrepancies and external actions are merged into an equwivalent input disturbances. By merging the unknown disturbance with the plant state to be reconstructed by an Extended State Observer (ESO), it is possible to construct controller which does not need to identify the internal plant feedback as in traditional linear and nonlinear controller design. Such lumped disturbances and uncertainties are represented by an additional state without identifying a specific input responsible for its changes. In order to keep the system model simple, effects of particular state variables on input are not considered, even in situations, when they may be supposed to be known.  
For tracking all the \(n+1\) state variables (including the input disturbance \(d\)) of real \(n\)th order systems, it is required to tune \(n+1\) ESO coefficients.
# A Comparative Study of Local Descriptors and Classifiers for Facial Expression Recognition
## 2 Related Works
### Classifiers
#### 2.2.3 K-Nearest Neighbors  
K-Nearest Neighbors (KNN) is a non-parametric method that classifies a new sample based on a majority vote system: a new data point is assigned to the class with the most representatives within the k nearest neighbors of that point. The neighbors are taken from the training set consisting of sample points for which the class is known.  
Despite being the simplest machine learning algorithm, KNN has achieved encouraging results in the FER problem. [PERSON] and [PERSON] [31] developed an approach based on eleven feature points representing the principal muscle actions and the KNN classifier to recognize six basic facial expressions, achieving an average accuracy of 90.76%. [PERSON] and [PERSON] [32] proposed a new feature descriptor based on LBP and Asymmetric Region LBP and classification was done using KNN. When classifying seven expressions, an overall recognition accuracy of 95.1% was achieved on the JAFFE database.  
It should be noted that the KNN algorithm does not construct a general internal model but simply stores training data instances. Hence, no explicit training is required. Hence, there are zero training costs. However, it becomes computationally expensive to find the k neighbors when classifying high-dimensionality features. In this study, scikit-learn's implementation of the KNN classifier was used [29].
# System Design and Reliability Improvement of Wireless Sensor Network in Plant Factory Scenario
## 2 Materials and Methods
### Design of Reliable Cluster Routing Protocols  
#### 2.5.1 K-Means Algorithm  
Clustering is a critical technique for enhancing the performance of a WSN. Among various clustering algorithms, the K-means algorithm is widely recognized for its simplic  
\begin{table}
\begin{tabular}{c c} \hline
**Monitoring Area** & **45 m \(\times\) 45 m** \\ \hline Sensor node sensing radius & 5 m \\ \hline Maximum number of iterations & 500 \\ \hline \end{tabular}
\end{table}
Table 2: Parameters of PSO algorithm for network coverage optimization.  
ity, computational efficiency, and effectiveness in partitioning sensor nodes into optimal clusters. The K-means algorithm is an unsupervised machine learning algorithm for cluster analysis [33]. The K-means clustering algorithm divides n points into artificially specified K clusters according to the magnitude of the Euclidean distance between each point to the K centers of mass, and each point belongs to the cluster corresponding to the center of mass with the closest Euclidean distance. The Euclidean distance is calculated as  
\[d=\sqrt{\left(x_{i}-x_{j}\right)^{2}+\left(y_{i}-y_{j}\right)^{2}} \tag{8}\]  
where (x\({}_{i}\),y\({}_{i}\)) is the coordinates of the ith sensor node and (x\({}_{i}\),y\({}_{i}\)) is the coordinates of the jth center of mass, i.e., cluster head.  
For the K-means clustering algorithm's process, K points were randomly selected as the initial centers of mass and the Euclidean distances from the points to each center of mass were compared. When each point was classified to the center of mass with the closest Euclidean distance, the initial K clusters were formed at this point. The Euclidean distance from each point to each center of mass was computed and the points were reassigned to the cluster with the closest distance, and the center of mass of each cluster was re-selected based on the existing points in the cluster. The process was repeated until some termination condition was satisfied, which could be any of the following:  
1. No points were reassigned to different clusters;
2. No center of mass changed again;
3. The sum of squared errors was minimized locally.
# Microcontroller Unit-Based Wireless Sensor Network Nodes: A Review
## 3 MCU-Based Hardware Platforms for Wireless Sensor Nodes
### Seismic Monitoring Applications  
#### 3.5.1 Jennic  
The piezo-based Jennic wireless sensor node [37] was explicitly designed for impact detection and the monitoring of buildings. Concerning the processor and memory specifications, the Jennic mote is built around a 16 MHz JN5139 microcontroller containing 96 kB RAM and 192 kB read-only memory (ROM). The JN5139 device is also capable of operating at 32 MHz. Its architecture is based on a 32-bit RISC CPU. Pertaining to the communication capabilities, a wireless transceiver that is compliant with the 2.4 GHz IEEE 802.15.4 protocol is incorporated in the node. A piezoelectric ceramic (PZT) is utilized for impact detection.  
Regarding the power supply, a 9 V battery is needed for the wireless coordinator and a 3 V AA battery is required for the sensor. Dual voltage supplies are necessary in order for piezo-based applications to successfully perform sensing and actuating functions. The piezo-based sensor node consumes a maximum current of 37 mA at 3.6 V supply. The sleep current of the node is 2.6 uA when the sleep timer is ON.
# A Survey of Analog-to-Digital Converters for Operation under Radiation Environments
## 1 Introduction
After an exhaustive search in the literature, we have gathered a collection of ADCs without degradation of electrical performance below 50 krad(Si) of TID and with a latch-up LET\({}_{th}\) (linear energy transfer threshold) above 50 MeV\(\cdot\) cm\({}^{2}\)/mg. We have collected for each ADC several performance data from data-sheets, scientific publications and radiation reports (specially data considered in [15], to allow the comparison with scientific ADCs). The main contributions of this study with regard to previous analyses [16; 17; 18; 19; 20] are the inclusion of transients and upsets, the comparison of electrical performance patterns with scientific ADCs, the analysis of the COTS subset and the definition of new FOMs considering radiation performance. The methodology of our work described in this paragraph is graphically schematized in Figure 1.  
This article has been organized such that: Section 2 summarizes how ADCs are evaluated electrically under radiation and proposes new figures of merit; Section 3 presents the selected ADCs for analysis; Section 4 analyses the selection of radenv-ADCs and compares it with the state of the art of scientific ADCs; Section 5 explains how to take advantage of the previous analysis while designing radenv-ADCs; overall conclusions are drawn in Section 6.
## 4 Overview and Functioning of the Generator
### Background Description of an HHCRSP  
A typical HHCRSP consists of a set of caregivers starting and ending their work at an HC center. The goal is to find a valid plan for an \(n\)-day period for each caregiver. The resulting planning indicates which job should be carried out by which caregiver and when the job should start. Each caregiver has available time for carrying out patients' jobs with the possibility of working just a part of the day or the whole day. Consequently, jobs are assigned according to the feasible time of each caregiver. Depending on how geographical constraints are dealt with, caregivers use the same or various modes of transportation. Each job has specific qualifications or skills, and they are always considered as hard constraints. The preference relationship can be defined between patients and caregivers as a ratio or score between them. An HHC center can decide that patients should be visited by their preferred caregivers in order to ensure patients' satisfaction. Besides, when job schedules are made, legal constraints have to be respected as well. Usually, caregivers possess a full-time or part-time agreement. Then, for each caregiver, a maximum and a minimum working duration are set up by his/her own agreement. The working time of each caregiver starts from the beginning of the first scheduled job and ends at the completion of the last job. Other legal regulations can be considered such as breaks, especially lunch breaks. Breaks are flexible in the caregiver' schedule. Furthermore, the HHC company aims to improve the planning of caregivers' routes by balancing the working time among caregivers, in order to obtain a fair plan [11; 35; 36].  
As a result, the HHC company seeks to optimize a multi-objective function. Usually, it aims at minimizing the total travel cost and the fixed cost of caregivers by minimizing the total overtime work (in hours) of caregivers and by limiting unscheduled visits [14; 27]. Depending on how the HHCRSP is treated, different constraints can be raised, but some of them are always present, such as [13; 27; 35]:  
* Only one HHC caregiver can visit a patient.
* Each patient must receive his/her requested HHC service provided by the appropriate caregiver.
* Each caregiver starts and ends at the HHC center.
* Each caregiver must leave the patient' home once he/she serves it.
* An unqualified caregiver cannot perform a qualified job.  
However, some constraints are deeply linked to how the HHCRSP is tackled:  
* The planning horizon is assumed to be \(n\)-days.
* Routing part: delivery and pick-up.
* Total working hours and route length limits for caregivers.
* Consistency of the working time with the possibility of a break between two successive jobs.
* The service time of a caregiver at a patient location.
* Patient time windows in the schedules of caregivers' routes.
* The feasibility of each job according to its time window.
* Patient/caregiver preference requirement.
* Continuity of care for all patients.
* Synchronization between two caregivers for a specific job.
* Biological sample production
* Precedence requirement.
## 4 Security Benefits of Directional Antennas in WSNs
### Wormhole Attack
The scheme consists of three stages: NC broadcasting phase; network node response/authentication; and, NC time-delay analysis. By using signature based authentication, transmission time information  
Figure 5: Wormhole attack. and antenna direction information, the SND scheme can efficiently prevent and detect the wormhole attacks. Another method that uses directional information gathered by trusted nodes to cope with wormhole attack is described in the case of MANETs [51], but can be also used in WSNs. The mechanism in based on symmetric node IDs, round trip response times and real time location information obtained by directional antennas. These data, encapsulated in integrated cryptographic keys are used in a Strict Friendliness Verification (SFV) of neighbors protocol, before multi hop packet routing.
# Camera-Adaptive Foreign Object Detection for Coal Conveyor Belts
## 4 Experiments
### Camera Adaptability Evaluation  
We conducted a cross-camera detection to assess the model's generalization in new camera coal mine environments. Specifically, we trained the model using images taken by Camera 1, and performed inference on images by Camera 2, Camera 3, and all the cameras (the entire dataset). The experimental results are summarized in Table 2. In the table, \"Impr\" refers to the performance improvement of our model over Deformable DETR, while \"Mean Impr\" indicates the improvement of our model relative to the average performance of all baseline models.  
The results show that all baseline models achieve good precision on the subset of Camera 1. However, due to the significant differences between the backgrounds of other cameras and the training data, the detection performance on these images drops sharply.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Method** & **Camera 1** & **Camera 2** & **Camera 3** & **All** \\ \hline Autoassign (2020) [18] & 80.09 & 50.40 & 36.27 & 53.42 \\ Sparse-RCNN (2021) [22] & 70.01 & 20.17 & 26.68 & 34.37 \\ Deformable DETR (2021) [27] & 65.97 & 24.66 & 23.89 & 34.03 \\ VarifocalNet (2021) [10] & 78.41 & 47.43 & 28.37 & 40.03 \\ Centripetalnet (2021) [21] & 77.90 & 33.70 & 26.32 & 41.93 \\ O2F (2023) [17] & 70.02 & 33.53 & 19.04 & 35.55 \\ CEASC (2023) [19] & 76.40 & 36.25 & 25.13 & 42.14 \\ AFPN (2023) [20] & 73.21 & 30.38 & 27.35 & 39.28 \\ CAFOD (ours) & 82.92 & 52.53 & 41.62 & 56.45 \\ Impr. & 20.44\% & 53.06\% & 42.60\% & 39.72\% \\ Mean Impr. & 10.15\% & 34.20\% & 36.01\% & 27.20\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Camera Adaptability Performance Comparison (measured by mAP50).  
This indicates that the baseline models have weak generalization ability in view of new camera scenes.  
However, as shown in Table 2, our proposed method outperforms other methods in terms of camera adaptability. The second-to-last row highlights the performance improvements achieved by our method over the Deformable DETR. Specifically, the improvement for Camera 2 and Camera 3 is notably higher compared to Camera 1, demonstrating the enhanced ability of our model to adapt to different camera perspectives. The last row of the table compares the average performance of our method against all other methods. Again, our method shows superior performance in terms of camera adaptability, confirming the effectiveness of the proposed strategy in improving the model's ability to generalize across different camera perspectives.
# Recent Advances and Applications of Semiconductor Photocatalytic Technology
## 4 Common Preparation Methods
### Gas Phase Method  
The gas-phase method refers to direct use of gas or a means to change a substance into a gas--making it physically or chemically react in a gas state--to agglomerate and grow to form nanoparticles in the cooling process. In the gas phase method, nanoparticles having high purity, and good nanoparticle properties can be produced. However, the gas phase method also requires advanced techniques and equipment. Vapor phase methods include physical vapor deposition (PVD), chemical vapor deposition (CVD), and molecular beam epitaxy (MBE).
## 3 SLD-GEDIR Protocol
### Segment Vehicles  
In SLD-GEDIR, each vehicle has a predefined maximum transmission range. All the vehicles within the transmission range of a specified vehicle are called neighboring vehicles of the specified vehicle. Each vehicle sends a hello message to all its neighbors to collect their speed, location, direction and time information. Segment vehicles are those vehicles that are present within the segment area. The segment area of a vehicle \(S\) is shown in Figure 4 as the area with dashed lines. The segment area \(S_{area}\) of a vehicle \(S\) can be calculated as given in Equation (1):  
\[S_{area}=\text{Area of Sector }SAB-\text{Area of }\Delta\text{ }SAB\] \[S_{area}=\frac{\alpha}{360^{0}}\pi R^{2}-\frac{L}{4}\sqrt{4R^{2}-L^ {2}} \tag{1}\]  
where \(L=AB,M=SC\)_and \(\alpha=2\times\tan^{-1}\left(\frac{L}{2M}\right)\). By substituting these assumptions, the Equation (1) can be further simplified as given in Equation (2):  
\[S_{area}=\left(R^{2}\times\tan^{-1}\left(\frac{L}{2M}\right)\right)-\left( \frac{L}{4}\times\sqrt{4R^{2}-L^{2}}\right) \tag{2}\]  
Figure 4: Segment vehicles and neighbor vehicles. 1.1 Probabilistic Analysis of the Presence of Vehicles in the Segment Area in Non-Shadowing Environments  
In this section, the circular transmission range of the vehicles has been considered. To setup one-hop links for forwarding packets, the sender vehicle needs to find at least one NHV in the segment area. The presence of vehicles in the segment area depends on three parameters vehicle density \(\lambda\), sector angle \(\alpha\), and transmission range of vehicle \(R\). One of the main objectives of SLD-GEDIR is to analyze the impact of the parameters \(\lambda\), \(\alpha\) and \(R\) on the probability of finding at least one vehicle in the segment area. To achieve this objective, different values are assigned to \(\alpha\) in increasing order until a vehicle is found in the segment. The vehicles are moving in two-dimensional network area and availability of vehicles in the network area follows a Poisson distribution with vehicle density \(\lambda\). Given the mean density of vehicles in the network, the number of vehicles present in the segment area is calculated using a Poisson distribution. Further, the arrival of each vehicle is independent. SLD-GEDIR uses the position information of vehicles for routing. The position information of a vehicle is represented by both \(x\) and \(y\) coordinates on a plane. Therefore, a 2D network model has been considered, as used in [31,33,49]. Various studies have been done to find the optimal transmission range for VANET environments. These studies clearly reveal that the transmission range requirement decreases with increasing vehicle density. The high density vehicular traffic environment requires a smaller transmission range to avoid simultaneous conflicting transmissions resulting in collisions [50,51]. Therefore, a smaller value of \(R\) (\(250\)\(m\)) has been considered for the analysis.
# A Comprehensive Survey on Real-Time Applications of WSN
## 3 Applications of WSN
### Habitat Monitoring  
Researchers in the life sciences are becoming increasingly concerned about the potential impacts of human presence when observing plants and animals in field conditions. In life sciences, huge numbers of researchers are based on the impact of the human presence of observing animals and plants in field conditions [44]. WSN is also employed in monitoring the habitat. Of late, it has been observed that due to the activity of trespassers in the habitat area, a disturbance in the breeding of the plants or sometimes in the animal husbandry area has been created. The unwanted stress has resulted in a low breeding rate within the specific species that has been monitored by the WSN [45]. Table 5, represents the examples of real-time applications for habitat monitoring that utilize WSNs.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**References** & **Application** & **Sensors** & **City Deployed in** \\ \hline [PERSON] et al. (2007) [41] & Automated Cattle Monitoring & Motion Sensors, ECH\({}_{2}\)O & Queensland, Australia \\ \hline [PERSON] et al. (2014) [42] & Prevent Cattle Rustling & Markov Process, GSM, Wi-Fi & Johannesburg, South Africa \\ \hline [PERSON] et al. (2017) [43] & Cattle Recovery System &
\begin{tabular}{c} Mobile Embedded Sensors, \\ Worldwide Interoperability for \\ the Microwave Access (WiMAX) \\ \end{tabular} & Nairobi, Kenya \\ \hline \hline \end{tabular}
\end{table}
Table 4: Examples of prototyped applications for livestock. WiMAX: Worldwide Interoperability for the Microwave Access.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline [PERSON] et al. (2009) [36] & Gas Meter & Pressure Sensors & Taipei, Taiwan \\ \hline [PERSON] et al. (2011) [37] & Convenience Store & \
\begin{tabular}{c} Temperature, Humidity, \\ Micro-Electro-Mechanical Systems \\ (MEMS), flow Sensors \\ \end{tabular} & Taipai, Taiwan \\ \hline [PERSON] et al. (2012) [38] & Monitoring for Coffee Factory &
\begin{tabular}{c} NO, SO\({}_{2}\), CO, pH \\ And Water Temperature \\ \end{tabular} & Madrid, Spain \\ \hline \hline \end{tabular}
\end{table}
Table 3: Examples of prototyped applications for industrial and business uses.  
[PERSON] et al., 2002 investigated the application of the real-time sensors to visualize and monitor seabird nesting and its ambiance in California, USA. They deployed the sensors on a small Island off the coast of Maine for gaining an insight into live data on the web. They identified key areas of the research such as data sampling, communications, network rescheduling, and health monitoring.