esa-sceva/satcom-chunk-collection huggingface dataset:
the United States (U.S.) [1,2]. Native to Central America and first identified as a pest of cultivated maize in Colorado in 1909 [3,4], populations are now found throughout the midwestern U.S. and Europe [5,6]. Once achieving pest status, various control tactics have been used to reduce damage caused by WCR larval feeding including crop rotation, soil-applied insecticides, and maize hybrids expressing insecticidal proteins from the soil bacterium _Bacillus thuringiensis_ Berliner (_Bt_) [2,7,8]. However, managing WCR has been historically challenging due to its remarkable ability to evolve resistance to all available management tactics throughout various local areas in the U.S. Corn Belt [2,7,9,10,11,12,13,14]. Economic analyses estimate costs associated with control strategies and yield loss exceed USD $2 billion annually [1,15]. Four insecticidal _Bt_ proteins are currently available for WCR management: Cry3 Bb1, mCry3A, eCry3.1 Ab, and Cry34 Ab1/Cry35 Ab1 (now classified as Gpp34 Ab1/Tpp35 Ab1 [16]) [17, 18, 19, 20]. Field-evolved resistance to Cry3 Bb1 and mCry3A was first reported in 2011 [21] and has subsequently been confirmed in various areas of the U.S. Corn Belt [22, 23, 24, 25, 26, 27]. Cross-resistance between Cry3 Bb1, mCry3A, and eCry3.1 Ab has been widely demonstrated [21,23,25,26,28]. More recently, resistance to Cry34 Ab1/Cry35 Ab1 was identified [27,29,30], highlighting the urgency for alternative approaches for WCR management. Recent review articles highlight the history, use of, and evolution of resistance to synthetic insecticides [7] and _Bt_ traits [8].  
_In planta_ expression of double-stranded RNA (dsRNA) in the shape of a hairpin RNA triggers an RNA interference (RNAi) response within the insect, representing a new mode of action for WCR control [31,32]. In June 2017, the U.S. Environmental Protection Agency (EPA) registered the first transgenic maize product with an RNAi-based plant-incorporated protectant (PIP) for WCR management [33]. This product expresses three _Bt_ proteins (Cry3 Bb1 and Cry34 Ab1/Cry35 Ab1) and a dsRNA [32]. This review summarizes our current knowledge of RNAi for WCR management, including studies on the various RNAi traits explored to date, mode of action, susceptibility of field populations, resistance evolution, environmental risk assessment, and mammalian safety.
# Monitoring the Health and Residence Conditions of Elderly People, Using LoRa and The Things Network
## 3 Research Method
### Data Model  
From an application point of view, monitoring should occur in an environment controlled by official and authorized entities, including police and civil protection authorities. Therefore, it is necessary to have a database-supported system that allows real-time analysis of the data obtained and recorded in history. Figure 2 shows the conceptual schema of the database proposed for the IS4 HMET system, which consists of several relations that allow obtaining the data through the TTN network, guaranteeing access only to authorized persons. In addition, the database will allow storing a history of the operations performed on the database, maintaining an ACL (Access Control List) for each type of user.  
Figure 2: Entity Relational Diagram for IS4 HMET.
## 4 Simulation Analysis
### Simulation Result
#### 4.3.2 Task 2  
Take the task of controlling PCM to reach the target point (\(22\), \(-166\), \(736\)) mm as the new task. To evaluate the generalization performance, both the policy initialized using the MAML meta-reinforcement learning and the baseline policy are trained to adapt to new tasks using the REINFORCE algorithm, and their generalization performances are compared. The training processes for both policies employ the same hyperparameters. The training result is shown in Figure 9.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **Training Time (min)** & **Maximum Absolute Error (mm)** \\ \hline MAML meta-reinforcement learning & 5 & 2.82 \\ PPO & 28 & 41.12 \\ Actor-Critic & 17 & 23.97 \\ PILCO & 47 & 4.6 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of training results in task 1.  
Figure 8: The position error changes of the PCM after training by Actor–Critic in Task 1.  
Figure 7: The position changes of the PCM after training by PPO in Task 1.  
As illustrated in Figure 4, when faced with a new task, the policy initialized with MAML meta-reinforcement learning reaches convergence in the excepted reward after 60 updates with a training time of 5 min. The baseline policy reaches convergence in the excepted reward after 180 updates with a training time of 15 min. By comparison, it is evident that the MAML meta-reinforcement learning algorithm has learned effective initial weights for the policy network from historical experiences. These initial weights enable the policy to adapt to new tasks with minimal updates, improving the adaptive capability for end-position control of the PCM.  
After the training of the policy initialized by MAML meta-reinforcement learning is completed, we use the trained policy to control the PCM. The position changes and position error changes of the PCM are shown in Figures 10 and 11, respectively.  
The position error is (2.77, \(-\)2.82, \(-\)2.78) mm, and the maximum error is within 1.4%. Thus, the policy initialized with MAML meta-reinforcement learning can effectively control the PCM to reach the new target point after training.  
To compare the performance with other reinforcement learning algorithms, the control policies for PCM are trained using the PPO algorithm, the Actor-Critic algorithm, and the PILCO algorithm. The hyperparameters of the PPO algorithm are as follows: the clipping parameter \(\varepsilon~{}=~{}0.2\), the learning rate for the policy network \(l_{a}=1\times 10^{-4}\), and the learning rate for the value network \(l_{c}=5\times 10^{-3}\). Both the policy network and the value network have one hidden layer, and the number of neurons in the hidden layer is 256. The  
Figure 10: The position changes of the PCM after policy adaptation in Task 2.  
Figure 9: Result of Task 2.
# Analysis on Response of a Single Pile Subjected to Tension Load Considering Excavation Effects
## 2 Pile Response after Excavation  
Figure 1 shows the pile response considering excavation effects. The pile response analysis can be divided into two stages: the excavation unloading and pile loading stages. During the excavation unloading stage, the pile moves upward due to the basal heave. The neutral level, defined as the depth at which the shaft resistance changes from negative to positive, will emerge. Above the neutral level, the pile heave is smaller than the soil heave, resulting in positive shaft resistance. Nevertheless, the pile heave is larger than the soil heave, resulting in negative shaft resistance below the neutral level. Tension force will develop in a pile, and it has a peak value at the neutral level. The relative displacement \(\omega_{sp1}\) at the pile-soil interface is equal to soil greenfield heave displacement \(\omega_{s0}\) minus pile displacement \(\omega_{p1}\) and shear-induced soil displacement \(\omega_{s1}\):  
\[\omega_{sp1}=\omega_{s0}-\omega_{p1}-\omega_{s1} \tag{1}\]  
where subscript 1 represents the variables in the excavation unloading stage; \(\omega_{sp}\) is the relative displace between pile and soil; \(\omega_{s0}\) is the soil greenfield heave displacement. The greenfield soil displacement is defined as soil heave that occurs without the presence of a pile; \(\omega_{p}\) is the pile displacement; \(\omega_{s1}\) is the shear-induced soil displacement. The shear-induced soil displacement \(\omega_{s1}\) is defined as the soil displacement caused by the shear stress between the pile and surrounding soil.  
During the pile loading stage, a tension load is applied on the pile head, and the pile will move upward. The positive shaft resistance above the neutral level will change to negative shaft resistance, and the negative shaft resistance below the neutral level will increase. Consequently, the neutral level will move upward and disappear during the loading stage, and negative shaft resistance will develop along with the pile. The relative displacement at pile-soil interface \(\omega_{sp2}\) is equal to the pile displacement \(\omega_{p2}\) plus pile-soil relative displacement at the excavation unloading stage \(\omega_{sp1}\) minus shear-induced soil displacement \(\omega_{s72}\) and residual pile-soil relative displacement \(S_{p}\) caused by excavation unloading:  
\[\omega_{sp2}=\omega_{p2}+\omega_{sp1}-\omega_{s72}-S_{p} \tag{2}\]  
where subscript 2 represents the variables in the pile loading stage; the pile displacement \(\omega_{p}\) only refers to the pile displacement caused by pile loading. \(S_{p}\) is the residual pile-soil relative displacement. This represents the residual displacement after pile-soil interface unloading. The determination of the residual pile-soil relative displacement \(S_{p}\) is shown in the following section.
# Error-Resistant Movement Detection Algorithm for the Elderly with Smart Mirror
## 5 Experimental Results
### Result of Experiment 1  
The result of all scenarios is represented in Table 3. In a single video, the detection rate is calculated by (number of frames the method returned as'moved')/(number of total frames). In the FMV and SMV scenarios, as the ground truth value of each frame is \"moved (1)\", methods must record a higher detection rate while it must record a lower detection rate in the NMV scenario. Each cell represents the average/standard deviation value of each scenario, which contains 15 video sequences.  
In FMV scenarios, the detection rate increased to 10\(\sim\)30 percentage points between AFSM and VSM. In SMV scenarios, the detection rate increased greatly above 50 percent point between AFSM and VSM. It is presumed that the reason for the low detection rate in the FMV scenario of CGM and AFSM is that we set the threshold value too high. However,  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Scenario** & **Description** \\ \hline (FMV) Walking & Walk towards camera back and forth, 1\(\sim\)3 m. \\  & Controlled speed to 100\(\sim\)150 cm/s. \\ (FMV) Freehand Exercise & Squat down and stand up. \\  & Controlled time per action to 2\(\sim\)3 s. \\ (SMV) Walking & Walk towards camera back and forth, 1\(\sim\)3 m. \\  & Controlled speed to 33\(\sim\)50 cm/s. \\ (SMV) Freehand Exercise & Squat down and stand up. \\  & Controlled time per action to 12\(\sim\)15 s. \\ (NMV) Standing-Low Error & Standing still, no actions. \\  & Detection error rate is less than 3\%, Natural error is stable. \\ (NMV) Standing-High Error & Standing still, no actions. \\  & Detection error rate is more than 3\%, Natural error is unstable. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Scenario list. Each have 15 video sequences.  
even with this high threshold value, the detection rate of VSM was quite successful. In the next experiment, the reason why we cannot set the threshold value lower will be shown.
# SiamCAM: A Real-Time Siamese Network for Object Tracking with Compensating Attention Mechanism
## 4 Experiment  
Our proposed tracking algorithm is evaluated on the generic tracking datasets VOT2018 [43], OTB2015 [44], and LaSOT [45]. At the same time, ablation experiments are performed on different components. Some of the state-of-the-art tracking algorithms, such as the Siamese family of algorithms, DiMP, ATOM, and the algorithms in this paper, are selected for comparison. Then, the effectiveness of different domain attention is evaluated at different layers of the network and in different combinations. Our method is trained with stochastic gradient descent (SGD). The experimental environment is 128G of running memory and 4 GTX 1080 ti. We use synchronized SGD over 4 GPUs with 128 pairs per minibatch (32 pairs per GPU), which takes 12 h to converge. We use a warmup learning rate of 0.001 for the first 5 epochs to train the RPN braches. The Siamese network framework used is the PySOT [46] framework based on PyTorch 0.4.1 [47]. For the last 10 epochs, the whole network is end-to-end trained with a learning rate exponentially decayed from 0.005 to 0.0005. The parameters of the whole network are released for training later. One iteration processes 64 images with a CPU work thread of 1. The whole training is supported by the YouTubeBB [48], GOT10k [49], VOT2018, and DET [50] datasets.
# Cryptographic Encryption and Optimization for Internet of Things Based Medical Image Security
## 3 Proposed Method
### De-Signcryption Process  
This chapter shows how the recipient can decrypt an image that the sender has already sent. In this stage, the recipient receives the message's signed image (\(C_{I},M_{I}\) and \(P_{I}\)) from the sender and decrypts it.  
1. Sender values (\(M_{I}\) and \(P_{I}\)) recipients private key (\(A_{K2}\)), recipient public key (\(A_{K2}\)), and to create a hash that would produce 128 bits. \[O_{H}=Hash\Big{(}B_{k2}*i^{M1}\Big{)}^{P_{1}^{*}}A_{k1}P_{N}\] (24)
2. The 128-bit output is divided into two 64-bit parts, much like a sign-encryption operation, which would show the (\(O_{H1}\), \(O_{H2}\)) key match. This key combination and the key match used to sign the image would be identical, making it impossible to tell them apart.
3. The image is then provided by the receiver after using the key \(O_{H1}\) to decode the cypher image \(C_{I}\). \[Dec_{Image}=Dec\ O_{H1}(C_{I})\] (25)  
If they match, it means that [PERSON] actually marked and sent the message. If not, [PERSON] will recognize that [PERSON] either did not mark the message or that [PERSON]'s message was blocked and altered by an outsider. It advances and is authentic when the next level, the de-signcryption, indicating that the received image m is authentic. when the signcrypted image (\(C_{I},M_{I}\) and \(P_{I}\)) received from the reliable outsider has to supply keys. It is strongly advised to encrypt images using a powerful block cypher. The best possible keys were created to safeguard the medical imaging.  
Two cloaks, the mystery cover and the even masks, are used in a relentless movement to incorporate the decryption process. Thus, decoding an image entails using an unscrambling system to each encrypted estimate of a pixel in the encrypted image. When a simple encryption scheme is being used, all image handling operations may only be performed on the unencrypted image.
# Numerical Modeling, Analysis, and Optimization of RFID Tags Functioning at Low Frequencies
## 3 Software Program Used for Determining Possible Structures
Following the analysis with this software for circular coils with a maximum exterior diameter of \(D_{\epsilon}\) = 20 mm, 1883 possibilities were obtained, of which 299 met all the imposed conditions. Among these, the structures with the smallest outer diameters--namely 13 mm, 14 mm, and 15 mm--were modeled. These structures were modeled using Ansys Q3D Extractor and Ansys HFSS(HEADQUARTERS Southpointe 2600 Ansys Drive Canonsburg,  
Figure 7: The MATLAB interface for determining structures of interest: (**a**) the interface of the algorithm for determining the characteristic parameters for coil structures; (**b**) determination of the structures that meet the criteria in tabular and graphical form. PA 15317, USA), resulting in values very close to those obtained analytically, as shown in the graphs in Figure 8. The errors between the calculated values and those obtained through numerical modeling were between 4% and 6%, while for the capacitance values, the errors were between 4% and 10% due to the fact that the ports of the structures had different dimensions, which influences their values. Therefore, optimizing the length of the ports is necessary. The same process was carried out for the square structures, where, after applying the filters, 358 viable structures were obtained out of a total of 1883. For the hexagonal structures, 318 structures that met the constraints were obtained, and for the octagonal structures, there were 314 possible structures. Concordance between the analytically and numerically obtained values for all analyzed shapes can be observed in Figures 9-11. It can be seen that for the capacitances, the errors increased, but it was observed that the positions and dimensions of the connecting wires between the capacitor and the coil somewhat influenced the capacitance values, so these differences are justified. Figure 8: Comparison between the analytical results and the results obtained through numerical modeling for (**a**) inductance and (**b**) capacitance. Figure 10: Comparison between the numerically and analytically obtained values for the inductance and capacitance required to achieve resonance at 13.56 MHz for the hexagonal coil structures: (**a**) inductance; (**b**) capacitance. Figure 9: Comparison between the numerically and analytically obtained values for the inductance and capacitance required to achieve resonance at 13.56 MHz for the square coil structures: (**a**) inductance; (**b**) capacitance. For the square planar structures, the errors between the calculated values and those obtained for the inductance through numerical modeling were between 2% and 5%, while the errors for the capacitance values were between 1% and 10%. For the hexagonal and octagonal planar inductors, the errors were a little bit higher due to their complexity. The values of the errors can be seen in Table 2 for a better comparison.
## 3 Research and Development of the Experimental Setup  
### Establishing the Traceability Chain  
To obtain scientific, accurate, and peer-reviewed experimental data, the parameters of the instrument and the basis of construction must have a complete and accurate traceability chain to ensure that each parameter can be traced directly or indirectly to international units. Figure 3 shows the transfer chain of liquid density measurements from the in-line vibrating tube.
# Improved Probability Prediction Method Research for Photovoltaic Power Output
## 3 Probability Prediction Combination Model
### Combination Method with Variable Intervals and Weights  
In order to fully utilize the characteristics of each model at different time periods, a combination method with different weights of intervals is proposed. For example, the accuracy of method A at interval 1 is higher than that of method B, and the prediction accuracy at interval 2 is lower than that of method B, so method A and method B will have different weight coefficients in different time intervals.  
When the day is divided into H intervals, the corresponding combination model will be respectively established. In this paper, the daily time period is divided into four intervals. Interval I is from 06:00 to 09:00, interval II is from 09:00 to 12:00, interval III is from 12:00 to 15:00, and interval IV is from 15:00 to 18:00. In the actual experiment, we first need to determine which time interval the predicted data belongs to, then use the combination model to predict the power generation. In this paper, a flow chart of the combination method is given, shown in Figure 2.
## 3 Materials and Methods
### Mathematical Model  
As mentioned earlier, this study addresses the delivery routing problem of the Chiang Rai post office. The problem is classified as a single-depot VRP, in which all vehicles that depart from the post office must return to the post office. For each operational day, a vehicle must decide on the delivery route for a set of different customers. All customers must be visited within an operational day with no specific time window. The traffic conditions were not considered in this case, due to the fact that there was little traffic in the investigated area. Thus, the delivery time and cost were primarily based on the total travel distance, and the objective of this problem was to find optimal routes with the lowest total travel distance. In addition, the capacity of the parcel pouches was assumed to be infinite, since the postman used two large pouches that could always contain all parcels, and there has been no case where the capacity was insufficient.  
In this study, the postman delivery routing problem is represented as an integer programing (IP) model. The parameters and decision variables used in formulating the model are defined as follows:  
Indices  
\(i,j\): Post office and customer locations (\(i,j=1\) for the post office location; \(i,j=2,3,\ldots,n\) for customer locations).  
\(k\): Vehicle (\(k=1,2,\ldots,m\)).  
Parameters  
\(d_{i,j}\): Distance from \(i\) to \(j\)  
\(U_{l}\): \(U_{j}\): Constant number used to avoid a subtour.  
\(N\): Number of locations.  
\(M\): Number of vehicles.  
Decision Variable  
\(x_{i,j,k}\): If vehicle \(k\) travels from \(i\) to \(j\) or \(0\) otherwise.  
The minimization of the total travel distance is expressed as  
\[Min\ Z=\ \sum_{i=1}^{n}\sum_{j=1}^{n}\sum_{k=1}^{m}d_{ij}*x_{ijk} \tag{1}\]  
The constraints are as follows:  
\[\sum_
# HI-Sky: Hash Index-Based Skyline Query Processing
## 3 Hash Index-Based Skyline Query Processing
### Hash Index-Based Skyline Query Processing
#### 3.2.1 Background
Due to the characteristics of Lemma 2, data in relatively smaller GLADs have at least one dimension where they have a better value than data with relatively larger GLADs, and are thus not dominated by larger GLADs. For example, in Figure 2, data point \(B\) belonging to GLAD 5 (0101) has a larger value in dimension 1 than data point \(D\) in GLAD 7 (0111), but it is guaranteed through the GLAD that it has a smaller value in dimension 2. Likewise, data point \(B\) has a larger value in dimension 2 than \(C\) in GLAD 9 (1001), but a smaller value in dimension 1 is guaranteed through the GLAD. Therefore, it is never the case that data belonging to a relatively large GLAD, such as \(C\) and \(D\), dominate \(B\) belonging to a relatively small GLAD. The proof of this is in Lemma 3. **Lemma 3**. : _Given arbitrary GLADs \(a\) and \(b\), if \(a<b\), data belonging to a are not dominated by those belonging to \(b\)._  
Proof. : As evidenced in the Lemma 2, if \(a<b\) is satisfied, \(a\) has a smaller value than \(b\) in at least one dimension. Therefore, data belonging to \(b\) cannot dominate data belonging to \(a\) according to Definition 1.