esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancing Recognition of Human-Object Interaction from Visual Data Using Egocentric Wearable Camera
## 4 Experimental Results and Analysis
### Evaluation Metrics and Validation Method  
For evaluating the proposed framework, we used accuracy (acc.), precision (pre.), recall (rec.), and F1-score (F1) as the performance measures. In addition, we focused on the computational time to assess the computational overhead. For validation purposes, we adopt a fivefold cross-validation method by dividing our dataset into five equal parts. We used a class-wise approach to stratify these parts by ensuring that each part reflected the overall distribution of target classes found in the complete dataset. This means that the proportion of each class was consistent across all folds, thereby preserving the balance of the dataset during both training and testing phases. This distribution was vital for keeping the class balance consistent in both the training and testing phases. Furthermore, this validation approach ensured the use of the entire dataset for training and testing purpose, but in different iterations, which allowed us to train and test our model robustly, and thus, avoid any biases that might arise from how the data were split.
## 2 Related Work
### Optical Character Recognition  
We chose Calamari [13] as our OCR engine since it is available under an open-source license and previous tests have demonstrated its advantages compared to other OCR engines regarding recognition capabilities and speed [9]. It focuses solely on the OCR training and recognition step using Tensorflow [14] as its backend and does not offer any preprocessing, segmentation, or post-processing capabilities, which is why it will not be covered in the upcoming comprehensive discussion of tools that provide a full OCR workflow.  
Evaluation of OCR Engines  
A thorough comparison of a shallow LSTM (OCRopus 1) and a deep CNN/LSTM hybrid (Calamari) was given in [9]. Three early printed books, printed between 1476 and 1505 in German and Latin, were used as training and evaluation data. The results showed, as anticipated, that the advantage of the deep network grew with an increasing number of lines used for training, yielding an average improvement in CER of 29% for 60 lines and 43% for 1000 lines.  
In [13], [PERSON] et al. compared Calamari, OCRopus 1, OCRopus 3, and Tesseract 4 on two public datasets: first, the UW3[15] and the DTA19 dataset, which is a part of the GT4 HistOCRorpus ([[https://zenodo.org/record/1344132](https://zenodo.org/record/1344132)]([https://zenodo.org/record/1344132](https://zenodo.org/record/1344132))). On the UW3 dataset, Calamari achieved a CER of 0.155%, considerably outperforming OCRopus 1 (0.870%), OCRopus 3 (0.436%), and Tesseract 4 (0.397%). The inclusion of confidence voting improved Calamari's result by another 26% to a CER of just 0.114%. Evaluations on the DTA19 dataset led to similar observations, with Calamari reaching a CER of 0.221% (0.184% with voting) compared to the significantly higher 1.59% of OCRopus 1 and 0.907% of OCRopus 3. When using a GPU, [PERSON] required 8 ms to train and 3 ms to predict a line, which proved to be considerably faster than OCRopus 3 (10 ms and 7 ms), while OCRopus 1 (850 ms and 330 ms) and Tesseract 4 (1200 ms and 550 ms) were far behind due to their lack of GPU support.  
A case study [16] dealing with German 19 th Century Fraktur scripts from various source materials (mostly novels, but also a journal, different volumes of a newspaper, as well as a dictionary) was performed. Mixed models for Calamari and OCRopus 1 were produced by training on a very extensive corpus of German Fraktur data from the 19 th Century, which was completely distinct from the material of the evaluation set. Altogether, ABBYY, despite making full use of its post-correction capabilities, achieved a CER of 2.80%, but was significantly outperformed by the raw OCR output of Calamari (0.61%) and even OCRopus 1 (1.90%).
## 1 Introduction
This algorithm provides a relatively general idea for non-linear control to a certain extent, but as neuron cardinality increases, the computational complexity increases geometrically. The multi-dimensional Taylor network is a newly proposed control structure. Due to its simple structure and convenient application, some promising results have been achieved. For example, the authors of [20] studied non-linear time-delay systems with uncertainties. However, the use of the MTN control algorithm for a strict feedback system has not been thoroughly studied. Spurred by this, this paper proposes an output feedback control method for a strict non-linear system based on an MTN so that the system's output can automatically track the desired signal. Our method initially transforms the original non-linear strict feedback system and redefines the state variables to obtain the new standard form. The state observer then completes the identification process of the adaptive system with the MTN's good approximation characteristics. The adaptive control law completes the system's tracking output based on this. Finally, a numerical simulation of a servo-hydraulic system model is carried out, verifying the effectiveness of the proposed algorithm. The main contributions of this paper are as follows:  
1. The traditional MTN control method relies on the unique performance of its basic structure and is designed and used for general controlled objects. Therefore, some characteristics of the controlled object itself are not fully considered and utilized. Therefore, this paper applies the MTN to strictly nonlinear feedback systems for the first time, taking full advantage of the characteristics that different parameters of the MTN can have different outputs with the same result and that processing two sets of internal parameters at the same time can effectively improve control efficiency. 2. In the control process, a set of variable representation rules is designed so that the general strict feedback system can be expressed in a standard form. On this basis, an adaptive parameter-adjustment rule based on a state observer is designed to bring the tracking error close to 0. Thanks to the simple structure of the MTN, compared with a neural network algorithm, it can effectively reduce the number of calculations. The remainder of this paper is organized as follows. Section 2 introduces the strict feedback system and transforms the original system into a standard form. Section 3 presents the design of the state observer, while Section 4 introduces the multi-dimensional Taylor network and its basic structure. Section 5 introduces a parameter identification method based on a multi-dimensional Taylor network, and Section 6 presents the controller's design and a stability analysis of the system. Section 7 illustrates the effectiveness of the proposed control scheme through a numerical simulation of a hydraulic control system. Finally, Section 8 concludes this paper.
## 4 Experiments and Results  
### Experimental Environment  
This chapter describes the performance evaluation of the ZSM technique proposed in this paper. The performance evaluation of the proposed protocol was conducted using the ns-2 simulator. The network size was 1000 m \(\times\) 1000 m, and the data transmission range was 250 m. In the network area, 50 and 80 mobile nodes were configured, and the initial location was randomly selected. The mobile node, as a condomwaypoint model, had a maximum moving speed of 20 m/s and a pause time of 10 s.  
In order to minimize packet collisions, a random delay between 0 and 10 ms was applied before retransmitting broadcast packets. The source node is randomly selected and transmits 10 packets per second. During the simulation, 5 attack nodes were allowed to perform 10 black hole attacks. Multicast group nodes can freely move within the multicast group zone during the simulation, allowing them to join or leave the group at any time. The total simulation time was 300 s. Additionally, it was assumed that the initially selected authentication node was not an attack node, and the energy consumption of the mobile nodes was not considered. Table 2 shows the parameter values used for performance evaluation.  
Figure 5: The process for secure multicasting.
## 3 Experiments and Results
### Spectral, Luminance and Crosstalk Measurements
Thus, we defined further parameters of the spectral transmission for every of the six subpixel positions of the stripe image. With these parameters, we take into account the differences in color intensity of the demonstrator. They are caused by the spectral profile of the backlight and the special filter characteristics of the primaries. Figure 12 shows the simulated and measured graphs of luminance and their crosstalk profiles. The new defined parameters were careful modified to adapt them to the original measurement graph. The difference in luminance between simulation and measurement is caused by the spectral intensity losses of our mock-up arrangement. However, the simulation of the measurement is well approximated for the green image parts. The spectral transmission was estimated to about 55% by integration of the overlapping area under the primary peaks of Displays 1 and 2; see Figure 10. The low maximum transmission leads to crosstalk values round 8% for a one-channel-arrangement and a min. of 15% for a three-channel arrangement in Figure 13.  
Figure 11: **Left**: experimental measured luminance for views 1 and 2; **Right**: crosstalk green for views 1 and 2, one channel green/one channel black. Furthermore, in Figure 13, the measurements and simulation results with three-times wider channels (three neighboring sub-pixel columns are bright) show a three-fold sub-structuring of luminance peaks instead of the expected luminance plateaus. Actually, the reason for this sub-structuring can be determined by the simulation software. It is caused by the structure of subpixels, shown in Figure 9. The palmate sub-structure in the graphs of Figure 13 is dependent on the size and alignment of the gaps between the subpixels. The proposed geometric design approach was verified by the experiment, but the 3D display demonstrator also shows an inadequate amount of color filter crosstalk. It has been demonstrated that a very specialized display design is necessary when a fast switchable barrier with high aperture, high transmission and excellent color separation has to be planned. Therefore, a novel color filter based on common nematic liquid crystals (NLC) was developed and investigated.
# A Moment-Fitted Extended Spectral Cell Method for Structural Health Monitoring Applications
## Appendix A Constraints for the Quadratic Programming Problem  
The constraints for the nonlinear moment fitting of enriched diagonal weights \(\mathbf{w}_{22}\) (Equation (34)) were derived by setting a lower bound \(\lambda_{\text{min}}\) for the block eigenvalues \(\lambda_{1,2}\) (Equation (33)):  
\[\begin{split}\frac{\rho}{2}&\left[w_{i,11}+w_{i,22}F _{i}^{2}\mp\sqrt{(w_{i,22}F_{i}^{2}-w_{i,11})^{2}+4w_{i,12}^{2}F_{i}^{2}}\right] >\lambda_{\text{min}}\\ \iff&\begin{cases}+\sqrt{(w_{i,22}F_{i}^{2}-w_{i,11} )^{2}+4w_{i,12}^{2}F_{i}^{2}}>\frac{2\lambda_{\text{min}}}{\rho}-w_{i,11}-w_{i,22}F_{i}^{2}\\ -\sqrt{(w_{i,22}F_{i}^{2}-w_{i,11})^{2}+4w_{i,12}^{2}F_{i}^{2}}>\frac{2\lambda _{\text{min}}}{\rho}-w_{i,11}-w_{i,22}F_{i}^{2}\end{cases}\\ \iff&(w_{i,22}F_{i}^{2}-w_{i,11})^{2}+4w_{i,12}^{2}F_{i}^{2}< \left[\frac{2\lambda_{\text{min}}}{\rho}-w_{i,11}-w_{i,22}F_{i}^{2}\right]^{2} \\ \iff&w_{i,22}^{2}F_{i}^{2}-2w_{i,11}w_{i,22}F_{i}^{2}+w_{i,11}^{2 }+4w_{i,12}^{2}F_{i}^{2}<\frac{4\lambda_{\text{min}}^{2}}{\rho^{2}}+w_{i,11}^{ 2}+w_{i,22}^{2}F_{i}^{2}\\ &\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad -\frac{4w_{i,11}\lambda_{\text{min}}}{\rho}-\frac{4w_{i,22}F_{i}^{2}\lambda_{ \text{min}}}{\rho}+2w_{i,11}w_{i,22}F_{i}^{2}\\ \iff&w_{i,22}>\frac{\lambda_{\text{min}}^{2}-\lambda_{\text{min}} \rho w_{i,11}-\rho^{2}w_{i,12}^{2}F_{i}^{2}}{\rho F_{i}^{2}(\lambda_{\text{min} }-\rho w_{i,11})},\\ \end{split} \tag{15}\]  
where it was assumed that \(\lambda_{\text{min}}<\rho w_{i,11}\).
# Dynamic Response Analysis of Structures Using Legendre-Galerkin Matrix Method
## 4 Worked Examples and Discussion of Results
### Two-Degree-of-Freedom (2 DOF) Structure  
A structure with two degrees of freedom (2 DOF) is analyzed in different dynamic conditions. The structure is first analyzed under free vibration with no effective damping, and then with additional damping. Successively, the analysis is further performed with the assumption of forced vibration, with and without damping. Basic input data and parameters are summarized as follows:  
* Free vibration, without damping: it is \(\mathbf{M}=\left[\begin{array}{cc}1.5&0\\ 0&2\end{array}\right]\), \(\mathbf{C}=0\) and \(\mathbf{K}=\left[\begin{array}{cc}300&-300\\ -300&800\end{array}\right]\) for mass, damping and stiffness parameters. The initial conditions are \(u(0)=\left[\begin{array}{cc}1\\ 1/2\end{array}\right]\) and \(\dot{u}(0)=\left[\begin{array}{cc}0\\ 0\end{array}\right]\).
* Free vibration, with damping: it is \(\mathbf{C}=\left[\begin{array}{cc}1.628&-0.256\\ -0.256&2.512\end{array}\right]\), while all the other parameters are equal to the undamped case.  
The solution of the motion equation using [PERSON]'s method for \(u_{1}(t)\) and \(u_{2}(t)\) are respectively shown in Figures 3 and 4. Moreover, the LGM results are compared with the exact method, giving evidence of acceptable consistency. It is worth noting that the  
Figure 2: Comparison of LGM and exact solutions for an SDOF system.  
exact solution is obtained using the Solver of Systems of Equation in the Maple software. Comparisons are then made with the approximate solution.  
Successively, the examined structure is analyzed under forced vibration. In this case, it is:  
\[p(t)\,=\,\left[\begin{array}{c}\sin 3t\\ 0\end{array}\right],\,u(0)\,=\,\left[\begin{array}{c}0\\ 0\end{array}\right]\,\text{and}\,\dot{u}(0)\,=\,\left[\begin{array}{c}0\\ 0\end{array}\right]\]  
Once again, the structure is analyzed with and without damping, using the developed LGM method. The analytical results obtained from the LGM method for \(u_{1}(t)\) and \(u_{2}(t)\) are shown in Figures 5 and 6, respectively, and compared with the exact solution, proving again a rather acceptable consistency in the examined time interval.  
Figure 4: Free vibration analysis for a 2 DOF system. Comparison of \(u_{2}(t)\) as a function of time, as obtained from the LGM method or the exact solution.  
Figure 3: Free vibration analysis for a 2 DOF system. Comparison of \(u_{1}(t)\) as a function of time, as obtained from the LGM method or the exact solution.
## 4 Simulation and Discussion
### Simulation II  
This second simulation involves testing the accuracy of the proposed approach using the Stanford Arm and Barrett Technology's WAM7R robot manipulator. The specific values of the target joint variables (\(\mathbf{\Theta_{\text{goal}}}\)) and the initial joint variables (\(\mathbf{\Theta_{\text{in}}}\)) of the Stanford Arm and the WAM7R manipulator are shown in Table 4. According to Equation (1), the goal configurations of the end-effector frames of the two robot manipulators relative to \(\mathbf{\Theta_{\text{goal}}}\) are described as  
\[\begin{split}\mathbf{T_{\text{goal}}}&=\begin{bmatrix}0.664 1&-0.0505&0.7459&549.2836\\ 0.6213&-0.5177&-0.5882&-42.3986\\ 0.4159&0.8541&-0.3124&582.0788\\ 0&0&0&1.0000\\ \end{bmatrix},(\text{Stanford Arm}),\\ 0&0&0&1.0000\\ \end{split} \tag{78}\]  
Figure 2: The position and angle errors of the initial selected joint angles of the SCARA and the UR5 versus the convergence times of the NICCD and the Newton–Raphson (NR) methods.  
In the case of closing the convergence judgment, 150 iterations of the NICCD and NR methods were conducted. The position and angle errors of the initial selected joint angles of the Stanford Arm and the WAM7R manipulator versus the number of iterations of the NICCD and NR methods are given in Figure 3, where the error is represented on the logarithmic axis. Indeed, from Figure 3, it is clear that the approach proposed in this paper is more accurate than the NR method with respect to the position error and angle error.
## 5 Performance Evaluation
### Performance Evaluation for the Packet Delivery Ratio  
From Figure 11, LECAR achieves the highest packet delivery ratio compared with the considered routing protocols. The LER is the second-highest performer because it follows the same procedure as LECAR except for the buffer occupancy consideration. We believe that a lack of awareness of congestion leads to a performance decline in LER compared to LECAR. The GPSR performs the worst because it does not store the packet and tries to send it immediately, failing a maximum of times. The packets are delivered in GPSR only when the destination is a neighbor of the sender. GPSR-Q also lags far behind in the packet delivery ratio, indicating that if the routing is decided only based on the available location information, without considering a strategy for the sparse network scenario, the routing protocols suffer severely in achieving a considerable packet delivery ratio.  
Spray and Wait performs far better than GPSR or GPSR-Q but slightly worse than LAROD-LoDiS. In Spray and Wait, the sender broadcasts packets to all neighbors, the neighbors broadcast to their neighbors, and so on until the packet reaches its destination.  
Figure 11: Performance comparison of routing protocols for the packet delivery ratio when the buffer size is (**a**) 25 MB and (**b**) 50 MB.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter Name** & **Parameter Value** \\ \hline Observation area & 10,000m \(\times\) 10,000 m \\ Scan area for each UAV & 400m \(\times\) 400 m \\ UAV speed & 55–70 m/s \\ Transmission range & 800 m \\ Wireless standard & 802.11 b \\ Number of UAVs & 5–30 \\ Number of targets & 0–10 \\ Simulation time & 60 min \\ Packet size & 524 KB \\ \hline \hline \end{tabular}
\end{table}
Table 2: Key parameters in the simulation experiments in Network Simulator 3.26.  
This spreading often causes significant congestion in the buffer and leads to high packet losses. We believe this high packet loss is the key reason behind the poor performance of Spray and Wait in this experiment.  
In addition, LAROD-LoDiS performs better than Spray and Wait but worse than LER and LECAR. Although LAROD-LoDiS tries to keep the number of copies per packet low, it fails to do so from time to time, leading to congestion. Moreover, it lacks the location estimation mechanism that highly contributes to poor performance compared to LER and LECAR.
## 7 Results and Discussion
### Relative Comparison of BWO with Conventional PSO  
The implemented BWO method has been compared with conventional PSO to assess its performance. The voltage profile of the IEEE-33 test bus after the integration of WTGS-DGs as well as EV loads is graphically shown in Figure 8. Moreover, Table 2 illustrates the comparison between the determined DG and EV load locations and their appropriate sizes using BWO and PSO. It is apparent that the presented methodology works comparatively more effectively than conventional techniques by stabilising bus voltages at weak locations in a distribution system, as shown in Figure 9, which eventually leads to a higher rate of efficiency. In conclusion, this method achieves the best network voltage profile with the fewest system losses.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**BWO**} & \multicolumn{4}{c}{**PSO**} \\ \hline DG locations (Bus Nos) & 16 & 17 & 18 & 32 & 16 & 17 & 18 & 32 \\ DG size & P (Mw) & 0.9425 & 0.6812 & 0.01 & 2.5 & 0.3108 & 1.1555 & 0.01 & 2.5 \\  & Q (Mvar) & \(-\)0.4975 & \(-\)0.4975 & \(-\)0.4975 & \(-\)0.0289 & 0.0455 & 0.0559 & 0.055 & 0.0549 \\ \hline EV locations (Bus Nos) & 1 & 1 & 2 & 11 & 1 & 1 & 21 & 33 \\ EV load size & P (Mw) & 0 & 0.5082 & 0.0651 & 0.6629 & 1.9445 & 3.0799 & 2.0022 & 0.113 \\  & Q (Mvar) & 0 & \(-\)1.694 & 0.0701 & \(-\)2.2097 & \(-\)2.0711 & 0.5229 & \(-\)1.5308 & \(-\)0.3767 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of BWO with PSO.  
Figure 8: Voltage profile of modified test system using BWO and PSO.
## 2 Materials and Methods
### Determination of Saliva IL-6 Concentration  
The concentration of IL-6 in the saliva samples was determined using the Salivary IL-6 ELISA Kit (Salimetrics, State College, PA, USA), which is a sandwich immunoassay for the quantitative measurement of salivary IL-6. This is a sandwich ELISA kit wherein the IL-6 standard (Salimetrics' High and Low IL-6 Controls) and tested samples bind to the antibody binding sites on a microtiter plate. After incubation, the unbound components were washed away. Next, biotin conjugated to goat antibodies and to human IL-6 was added and attached to the bound IL-6. After incubation, the unbound components were washed away, and streptavidin (conjugated to horseradish peroxidase) was added (and binds to the biotin conjugated and the goat antibodies), and the levels of detected IL-6 were measured by the reaction of the horseradish peroxidase enzyme to the substrate tetramethylbenzidine. The effect of this reaction is a blue colour in the solutions in the plate wells. Next, a yellow colour was formed after stopping the reaction with an acidic solution, and then the optical density was measured at 450 nm using a microplate reader (EonTM Microplate Spectrophotometer; BioTek, Winooski, VT, USA). The corresponding concentration of IL-6 in pg/mL was determined from a standard curve (four-parameter nonlinear regression curve fit) using the average optical density values of the controls and saliva samples. The functional sensitivity of the Salivary IL-6 ELISA Kit is 2.08 pg/mL.
# Energy Efficiency in Short and Wide-Area IoT Technologies--A Survey
## 4 Classification of Energy Efficiency Methods
### Energy Efficiency in LPWANs
#### 4.2.2 Cellular IoT (cIoT)
Simulation results show that PBESM outperforms the standard NB-IoT procedure, which requires a RRC Connection procedure, achieving up to 34% energy consumption decrease. When compared to the ideal prediction scheme, having no error prediction of processing delay, PBESM showed a reasonable energy consumption increase of about 5%. Another method for energy-efficient uplink resource scheduling was proposed in [161], considering 3.75 kHz subcarrier-spaced, single-tone, and modulation and coding scheme (MCS). A decision problem, called Energy-efficient Ultra-reliable Scheduling Decision (EUSD), is thus formulated aiming to possibly find a number of repetitions so that devices can save most of the energy while ensuring reliable transmissions. EUSD is composed of two phases:  
* The first phase makes use of the _minimal energy cost_, which quantifies the energy consumed by each device. This phase determines the default parameters for each UE, such as the number of resource units the optimal number of repetitions, and the transmission parameters, so that QoS and transmission reliability requirements can be met. Once the consumed energy of each device is evaluated, the device with the minimal energy cost is selected;
* The second phase is called _Weighting Based Flexible Scheduling_, and it is based on a _score function_ to determine the optimal scheduling of the requests from the devices. The score function is used to evaluate which of the uplink transmission requests is the most urgent, and list them in a descending order. After this step, the scheme defines a Waste function, which determines the possibility that a device may waste the resource units that it has been allocated. Finally, the so-called _cost ratio_ is used to determine if the resource units and the MCS of a device satisfy the delay deadline, without incurring in extra energy consumption. During this phase, the importance is placed on finding a way to balance the resource units and MCS, while keeping the energy consumption within a predefined minimal cost. Simulation results show that the energy consumption increases with the number of requests, with the proposed scheme outperforming the standard NB-IoT random access procedure.