esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Review of Model Construction and Parameter Determination
### Strength Constraints
Similarly, knee flexor and extensor torques expressed as a function of kinematics at both the knee (primary joint) and hip (secondary joint) were more accurate than a monoarticular function at secondary hip angles other than that used during knee monoarticular dynamometer measurements [114]. Monoarticular torque actuators at the hip may overestimate maximal flexion torque and underestimate maximal extension torque with the knee extended, compared to biparticular representations [115]. An alternative approach to determining contractile and series elastic component parameters have been via optimization during the model evaluation phase [11]. For example, the isometric strength parameter within a surrogate torque profile from an elite male gymnast was varied to match two recorded performances by a different athlete in the takeoff phase of springboard diving [11]. This approach assumed maximum activation during the recorded performances, and so prevented greater activation in subsequent optimizations. Due to limitations in the accuracy of strength parameters, this approach should be reserved for situations where individual-specific strength measurements are not feasible. Despite the advantages of torque-driven models, this approach mostly [116; 117] restricts individual-specific forward-dynamics simulation models to two-dimensions. At present, a technique for determining and describing individual-specific maximal torque profiles at joints such as the hip and shoulder collectively about three axes (e.g., flexion-extension, abduction-adduction, internal-external rotation) has not been established. The advantage of torque-driven models over muscle-driven models--that individual-specific maximal strength parameters can be determined in vivo--is therefore only true for two-dimensional representations. This has resulted in a divided approach to methodological advancement in the simulation modeling community: incorporating effects of nonplanar movements by increasing the complexity of torque-driven models [118]; or improving the individual-specificity of parameters within muscle-driven models [119; 100; 101; 102].
###### Abstract  
Recently, attribute-based access control (ABAC) has received increasingly more attention and has emerged as the desired access control mechanism for many organizations because of its flexibility and scalability for authorization management, as well as its security policies, such as separation-of-duty constraints and mutually exclusive constraints. Policy-engineering technology is an effective approach for the construction of ABAC systems. However, most conventional methods lack interpretability, and their constructing processes are complex. Furthermore, they do not consider the separation-of-duty constraints. To address these issues in ABAC, this paper proposes a novel method called policy engineering optimization with visual representation and separation of duty constraints (PEO_VR&SOD). First, to enhance interpretability while mining a minimal set of rules, we use the visual technique with Hamming distance to reduce the policy mining scale and present a policy mining algorithm. Second, to verify whether the separation of duty constraints can be satisfied in a constructed policy engineering system, we use the method of SAT-based model counting to reduce the constraints and construct mutually exclusive constraints to implicitly enforce the given separation of duty constraints. The experiments demonstrate the efficiency and effectiveness of the proposed method and show encouraging results.  
attribute-based access control; policy engineering; visual authorization representation; separation-of-duty constraints +
Footnote †: journal:  
0000-0001-5000-0000-0000][PERSON]  
0000-0001-5000-0000][PERSON] and [PERSON]
## 5 Platform Testing and Validation  
For the testing and validation of our proposed platform, we have developed a testbed scenario within the context of the European Project STOP-IT ([[https://stop-it-project.eu/](https://stop-it-project.eu/)]([https://stop-it-project.eu/](https://stop-it-project.eu/)), accessed on 5 June 2021), composed of an attacker machine, a victim infrastructure and a CLAP instance.  
As depicted in Figure 2, the attacker initiates passive and active attacks to identify the active devices on the network and exploit some vulnerabilities (1). Once the IDS detects the intrusion, it generates events to feed the XL-SIEM (2). As soon as radiofrequency anomalies are detected by the Jamming Detector (3), it sends an event to the XL-SIEM (4). In parallel, the NTSA is listening to the network to check for anomalies on the traffic (5). Once an anomaly is detected, it sends an event to the XL-SIEM (6). Events from all sensors are correlated by XL-SIEM and shared with the RTAD for further processing (7). The remainder of this section will describe each of the components from the testbed scenario.
## 1 Introduction
The question of under what conditions, with the increase of packet loss rate, the convergence speed of iterative learning algorithm will not slow down is solved. Firstly, the ILC model of a heterogeneous multi-agent system is established, and then the frequency domain model is obtained from the time domain model. When different communication packets are lost between agents, the previous iterative data is used to form the current iterative data. ILC in frequency domain is a two-dimensional system in which information propagates on two independent axes: frequency axis and iterative axis. The convergence of ILC is reduced to the convergence of any frequency in the iterative dimension, which is realized by the transformation of frequency and parameter iteration. After the exchange of variables and parameters, Z-transform is performed in the iterative domain. The convergence of the system error is analyzed by using the eigenvalue of the characteristic equation. Since the characteristic equation is in matrix form, it is difficult to get the eigenvalue of the characteristic equation. We use the generalized Nyquist criterion and [PERSON]'s disk theorem to analyze the eigenvalue range of the system and obtain the condition of the system convergence. The simulation results show that the compensation method proposed in this paper can make the system error convergence, and the convergence speed will not slow down. This paper consists of the following parts. In Section 2, an ILC model in frequency domain is established for heterogeneous multi-agent systems and the control objective for formation tracking of a multi-agent system is given. A dropout compensation ILC method is proposed in Section 3. In Section 4, the convergence conditions are analyzed. The results are verified by numerical simulation in Section 5.
## 4 Experiments and Results
### Ablation Results  
To further verify the effectiveness of the main components in the NDRE, we conducted an extra ablation study under three testing modes:  
* Single: Only bags composed of single instance were selected for relation extraction.
* Multiple: Only bags composed of more than one instance were selected for relation extraction.
* Whole: The whole dataset was used for relation extraction.  
Figure 5 shows the PR curves of the compared models under three testing modes (single, multiple, whole), and the corresponding P@Recall and AUC values are listed in Table 2.  
In particular, NDRE(-Label) denotes removing the noise-detection strategy, and NDRE(-MHA) denotes removing the multi-head self-attention mechanism. REDS2 is the first work to fuse NYT and web tables data, which is the dataset we use.  
From these results, we can observe that: (1) The performance of the NDRE declines significantly when its different components are removed. Especially when the noise detection strategy is removed, it shows a 9.9%, 7.6%, and 9% decrease in terms of AUC under the three testing modes, respectively. The overall performance is NDRE > NDRE(-MHA) > NDRE(-Label) > REDS2, which shows that our sentence encoder can better capture semantic features and that the noise-detection strategy can greatly improve DSRE. (2) Under the \"Single\" testing mode, NDRE and NDRE(-MHA) had improvements of 12% and 4.8%, respectively, under AUC compared with REDS2. This demonstrates that the noise-detection strategy can effectively detect and filter out the noisy bag consisting of single instances. (3) Under the \"Multiple\" testing mode, NDRE and NDRE(-MHA) outperformed REDS2 and their AUC was improved by 14.7% and 9.2%, respectively. It indicates that the noise-detection strategy can availably detect the wrongly labeled instance in the bag and then make full use of the information contained in the true positive instance. (4) The AUC of the NDRE under the \"Whole\" testing mode was 0.5094, which comprises a new state-of-the-art performance.  
Figure 4: Aggregate precision–recall curves of our model and compared baseline methods.  
Figure 5: Aggregate precision–recall curves of REDS2, NDRE(-Label), NDRE(-MHA), and NDRE under different testing modes: (**a**) single, (**b**) multiple, and (**c**) whole.
## 4 Discussion  
Through a series of experiments using UAV data linked to ground measurements, we have analysed and tested approaches for minimizing spatial scaling biases and uncertainties when monitoring wheat using Sentinel-2 20-m resolution data. We quantified variation in the spectral reflectance of fields at scales below the resolution of a Sentinel-2 and found that heterogeneity within crops is an important consideration for calibrating satellite sensors. We then showed that UAV data could provide a  
Figure 8: Example LAI maps and distributions of three fields. From the top row, fine-scale UAV LAI estimates (row 1), Sentinel-2 estimates derived from the single-stage GPR model calibration (row 2), estimates from the two-stage calibration (row 3) and plots comparing the distribution of LAI values amongst the UAV and GPR model estimates (row 4).  
robust bridge from in-situ data resolution to satellite pixel scale. We developed a robust calibration approach to take account of and correct for heterogeneity, and to effectively quantify retrieval error for LAI. This approach should be valid across other vegetation types for producing higher-quality biophysical retrievals.
## 6 Relating Cancer Diagnosis and Dates
### Choosing the Proper Linkage  
The next challenge to be solved after linking dates to cancer entities is to choose the appropriate linkage. As for the same patient, different documents are being annotated from different moments in time; the main challenge is to decide which events refer to diagnosis, and to then eliminate those linkages in which the diagnosis is not clear. This is a challenge because for each patient, hundreds of annotations containing cancer concepts and dates can be extracted. Table 2 shows a possible set of records extracted for the same patient in which we can see instances of triplets: _cancer diagnosis concept, date and event_. Observing the instances one can see that:  
* There are different concepts indicating cancer diagnosis entity. Some of them are very generic (\"Cancer\"), while others are specific terms (\"Small cell lung carcinoma\").
* There are different dates associated to cancer diagnosis entities.
* Not every record contains an event entity.  
Consequently, among all the records associated with a patient, some heuristics are required to find the proper record that matches the diagnosis date. To solve this problem, we propose: (i) to choose the most specific diagnosis, (ii) if more than one record exists associated to this diagnose, but with different dates, then we need to discriminate the most proper date.  
Figure 7: Parse tree: linking dates and cancer diagnosis.  
* _Choosing the cancer diagnosis:_ A ranked list of UMLS identifiers is used. This list contains UMLS codes and their respective cancer diagnosis sorted according to those that more specifically describe the diagnosis. In this list, the concept _\"Squamous cell lung carcinoma\"_ is more relevant than the concept \"Lung cancer\" because the former describes more specifically the patient's diagnosis.
* _Choosing the diagnosis date:_ two heuristics are applied to disambiguate the date for the chosen diagnosis:
* Annotations containing events other than _\"diagnosed event\"_ are eliminated.
* Clinical notes are first ordered chronologically and then classified according to their type: (_Anannesis_, _Clinical Judgment_, _Medical Evolution_, _treatment_, _etc_.). According to this classification, the date is assigned taking into account the earliest annotation coming from a document classified as Clinical Judgement or alike.
# Optimal Waveform of the Partial-Respond Signal Based on Minimum Out-of-Band Radiation Criterion
## 3 Simulation and Realization of the System  
### Scheme of the Modulation and Demodulation Systems  
Figure 6 illustrates the block diagram of the adopted partial-respond quadrature modulation scheme. Three serial-to-parallel \((S/P)\) converters convert the group of symbols \(d_{i}\), \(i=0,1,\ldots\), \(\infty\), with a symbol duration of \(T\) into four blocks, each taking one of the values \(\pm 1\), where the delay time is a different integer multiple of \(T\) while the symbol duration has increased to \(4T\). The symbols enter cross-correlator, and according to the results of the cross-correlation operation, the symbol attenuation factor \(r\) was introduced. Then, the output signals of the pulse shaping filter provides the signals \(I(t)\)  
Figure 5: Normalized power spectra curves of the optimized signals. Curve 1 corresponds to the MSK and curves 2–5 correspond to \([n=1,m=4]\), \([n=3,m=5]\), \([n=5,m=6]\), and \([n=7,m=7]\), respectively.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Waveform** & \(  
and \(Q(t)\) in two channels. The signals \(I(t)\) and \(Q(t)\) were multiplied to the quadrature carrier and added to achieve the modulated signal.  
Figure 7 depicts the block diagram representation of the coherent demodulation of the signals. To remove the high-frequency components of the received modulated signal, it is coherently demodulated through the quadrature carriers and the low-pass filters. Since the signal in each channel introduces intersymbol interference, the baseband signal is filtered by a match filter. The signal on each channel is integrated, where the integration time is controlled by the integrator strobe pulse. Finally, the demodulation process follows the sampling, decision, and a \(P/S\) converter.
## 2 Related Works
### IP Lookup Algorithms Based on Bloom or Cuckoo Filter  
As mentioned above, both the Bloom filter and cuckoo filter have no false negatives but do have false positives, which would typically only cause unnecessary access to the set. Therefore, the filter structures are commonly used in this scenario, in which multiple direct accesses (e.g., external memory) are quite costly for the search of an element. Only if the membership of the element has been confirmed will the external memory be accessed. In particular, the filter structures can effectively improve longest prefix matching (LPM) for IP packet forwarding, which is also the focus studied in this paper.  
[PERSON] et al. first introduced the Bloom filter into the LPM algorithms for IP route lookup [14]. In their scheme, \(W\) distinct Bloom filters associated with IP address prefix sets of each length are maintained on-chip, where \(W\) is the number of different prefix sets, thus called the parallel Bloom filter (PBF) architecture. When performing IP lookups, first input each IP address into all of the Bloom filters based on the length in parallel, in order to perform membership queries, then detect off-chip forwarding tables in order of a priority of length according to the positive results of the filters and, finally, determine the next-hop information (NHI) of IP packet forwarding.  
[PERSON] et al. developed a vectored-Bloom filter (VBF) structure for IP route lookup [15]. The VBF structure stores output ports on-chip and can be implemented parallel in the lookup architecture. Since the off-chip hash table (i.e., NHI table) is rarely accessed, the memory efficiency is increased, which improves the search performance.  
[PERSON] et al. proposed a new filter structure called the length-aware cuckoo filter (LACF) in ref. [26], which realizes faster IP lookup with limited memory requirements. The basic idea of the LACF is to use different numbers of hash functions to insert and query routing entries based on the prefix length popularity. Due to the double insertion mechanism for the prefixes of less popular lengths, the LACF has a lower false positive probability.  
Based on the above discussion, the properties of the Bloom filter and cuckoo filter are summarized in Table 1.
# Comparative Analysis of Image Classification Models for Norwegian Sign Language Recognition
## 3 Methods
### SVM-Model
#### 3.4.5 Experiment 1: 64 \(\times\) 64 Images  
The result of the performance from the top three models for each kernel is displayed in Figure 16. All the models managed to obtain above 99% accuracy. Because there was very little difference in accuracy for the different models, we decided to calculate which model had the best accuracy compared to training time. This was calculated by dividing the accuracy of each model and then their training time to find the optimal model. By considering both accuracy and training time, we found that the kernel that strikes the best balance between performance and computational efficiency is the linear kernel with a Cvalue of 1. This kernel, along with the corresponding hyperparameters, was chosen as the final SVM model for our application.  
Using these hyperparameters for our SVM model, we split the data into a 70/30 split and trained the model with a linear kernel with a C value of 1. The result of the model on the unseen data is an incredible accuracy of 99.9%.  
Table 6 provides a comprehensive performance report of the Support Vector Machine (SVM) model for each sign. The precision, recall, and F1-score metrics, all at 1.00, offer a detailed understanding of the model's performance. However, a few signs such as 'D', 'L', and 'W' have shown minor errors.  
Precision is at 1.00 for all signs, with a slight dip to 0.99 for 'V'. This suggests a marginally lower accuracy when predicting this sign. Recall also maintains a high score of 1.00 for most signs, with a minor decrease to 0.99 for 'V', indicating slightly lower completeness when identifying this sign. The F1-score is at 1.00 for all signs, indicating a balance between precision and recall for all signs. The training time for this model was 43 s, and the time it took to make predictions on the unseen data was 53 s, indicating a relatively efficient model.
# TMD Damping for Structures with Uncertain Modal Parameters
## 2 Modelling  
### Equations of Motion  
The mitigation efficiency of a TMD is computed for the targeted modal component of the primary structure because TMDs are efficient in one modal component only [3; 6; 7; 8; 9; 10; 11; 12]. The equations of motion of the primary structure in modal coordinates with TMD are shown as follows:  
\[m_{1}\ddot{x}_{1}+c_{1}\dot{x}_{1}+k_{1}x_{1}=f_{e}-c_{2}\big{(}\dot{x}_{1}- \dot{x}_{2}\big{)}-k_{2}(x_{1}-x_{2}) \tag{1}\]  
\[m_{2}\ddot{x}_{2}=c_{2}\big{(}\dot{x}_{1}-\dot{x}_{2}\big{)}+k_{2}(x_{1}-x_{2}) \tag{2}\]  
where \(\ddot{x}_{1},\dot{x}_{1},x_{1}\) denote the acceleration, velocity, and displacement of the primary structure relative to inertial system; \(\dot{x}_{2}\) is the absolute acceleration of the TMD mass; \(\big{(}\dot{x}_{1}-\dot{x}_{2}\big{)}\) and \(\big{(}x_{1}-x_{2}\big{)}\) are the TMD relative velocity and TMD relative displacement; \(m_{1}\) describes the actual structural modal mass; \(c_{1}\) and \(k_{1}\) are the viscous damping and stiffness coefficients of the primary structure; \(f_{e}\) denotes the excitation force; and \(m_{2},c_{2},k_{2}\) describe the TMD mass, TMD viscous damping coefficient, and TMD stiffness. The stiffness of the primary structure is given by the actual eigenfrequency \(f_{1}\) of the target mode, which is shown as follows:  
\[k_{1}=m_{1}(2\pi f_{1})^{2} \tag{3}\]  
As assumed by [PERSON] [3], the structural damping ratio \(\zeta_{1}\) is set to zero, whereby the viscous damping coefficient is zero as well.  
\[c_{1}=2\zeta_{1}m_{1}(2\pi f_{1})=0 \tag{4}\]  
For the study under consideration, the steady state displacement response of the primary structure for harmonic excitation is of interest. Therefore, the steady state displacement amplitude of the primary structure \(X_{1}\) is analytically computed by the well-known transfer function as follows [3]:\[\frac{X_{1}}{X_{1,static}}=k_{1}\sqrt{\frac{\left(k_{2}-m_{2}\;\omega_{\varepsilon}^ {2}\right)^{2}+c_{2}^{2}\omega_{\varepsilon}^{2}}{\left\{(-m_{1}\omega_{ \varepsilon}^{2}+k_{1})(-m_{2}\omega_{\varepsilon}^{2}+k_{2})-k_{2}m_{2}\; \omega_{\varepsilon}^{2}\right\}^{2}+c_{2}^{2}\omega_{\varepsilon}^{2}(-m_{1 }\omega_{\varepsilon}^{2}+k_{1}-m_{2}\;\omega_{\varepsilon}^{2})^{2}}} \tag{5}\]  
where \(\omega_{\varepsilon}\) denotes the excitation radial frequency, and \(X_{1,static}=F_{1\alpha}/k_{1}\) is the static deflection of the primary structure due to the excitation force amplitude \(F_{1\alpha}\).
# Kinematic Analysis and Motion Planning of Cable-Driven Rehabilitation Robots
## 2 Structure of Rehabilitation Robot and Human Lower Limb Model  
### Structure of Robot  
The overall design of the cable-driven rehabilitation robot is shown in Figure 1. The design mainly includes 4 columns, 5 rail brackets, 2 crossbeams, 6 synchronous belts, 8 servo motors (\(S1\)-\(S8\)), 6 DC motors, and 6 cable winters (\(A1\)-\(A6\)). As shown in Figure 1, each rail bracket is equipped with a synchronous belt with a cable winder (two cable winters are installed on the middle rail brackets, and only one cable is shown in the Figure 1), and each cable winder is equipped with an encoder, which is directly connected to the pulley. The DC motors and cable winters are assembled together, and the DC motors are used to drive the cable winters to realize the retraction and release of the cables. The cable will make the pulley turn, therefor, the encoder can directly measure the cable length. The connection of the cable winders with the rail brackets is rotatable, the exit point of the cable can be automatically adjusted according to the direction of the cables. The servo motors drive the synchronous belt to move the cable winters on the rail brackets. In addition, servo motors \(S3\) and \(S7\) also move the rail brackets on both sides up and down along the columns, and \(S8\) allows the middle rail bracket to move left and right along the crossbeams. Therefore, while moving along the rail brackets, the cable winders \(A4\) and \(A5\) can move up and down, \(A1\) and \(A6\) can move left and right, which effectively improves the workspace of rehabilitation. The cable winters do not directly contact the limb, but indirectly control limb movement by monitoring the cables. One end of the cable is connected to the drum of the cable winder, and the other end is connected to the patient's limbs through a flexible belt. On the one hand, it can detect cable tension; on the other hand, it can play a buffering role, which improves the safety of rehabilitation training. The rehabilitation robot can provide multi-posture rehabilitation training, and can freely choose horizontal, sitting, vertical, and other training postures according to different rehabilitation states of the body and joint adaptation. The movement of the cable winders and the retraction and release of the cables is controlled by selecting different rehabilitation actions and the number of cables. Consequently, the patient's limbs can move in three-dimensional space and adjust in accordance with the changes of the interaction between the limbs and the rehabilitation robot to complete the rehabilitation treatment action in real time.  
Figure 1: Cont.