esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic SDN Controller Load Balancing
## 4 DCC Problem Formulation
### Optimization Problem: Dynamic Controllers' Clustering  
Our goal is to find the best clustering assignment as defined by \(Y(t)\) which minimize \(\varsigma\left(t\right)\) (Equation (6)) and at the same time fulfills the distance constraint (Equation (8)). Therefore, the problem can be formulated as follows:  
\[Minimize\;\;\varsigma\left(t\right) \tag{9}\]  
subject to:  
\[\sum_{j}^{M}Y\left(t\right)_{ji}=\frac{M}{K}\;\;\;\;\;,\;\;\;\;\forall_{i} \tag{10}\]  
\[\sum_{j}^{K}Y\left(t\right)_{ji}=1\;\;\;\;,\;\;\;\forall_{j} \tag{11}\]  
\[\eta\left(t\right)<Cnt \tag{12}\]\[Y\left(t\right)_{ij}\in 0,1\quad,\quad\forall_{i,j}\]  
Equation (10) ensures that each cluster has exactly \(M/K\) controllers at a given time while Equation (11) ensures that each controller is assigned to exactly one cluster at a time. Equation (12) puts a constraint on the maximum distance between controllers within the same cluster.  
Regarding the distance constraint, the problem is a variant of a \(k\)-Center problem [27].  
On the other hand, the load balancing problem is a variant of a coalition-formation game problem [28], where the network structure and the cost of cooperation play major roles.  
These two general problems are NP-Complete because finding an optimal partition requires iterating over all the partitions of the player set, where the number of these partitions grows exponentially with the number of players, and is given by a value known as the Bell Number [29]. Hence, finding an optimal partition in general is computationally intractable and impractical (unless \(P=NP\)).  
In this paper, we propose an approximation algorithm to solve these problems. We adapt the \(K\)-Center problem solution for initial clustering, and use game theoretic techniques to satisfy our objective function with the distance constraint.
## 2 Materials and Methods
### Research Method
#### 2.3.2 Adsorption Isotherm Model  
Data analysis and model fitting were performed using Origin software (2022). The version number of Origin 2022 is 9.9.0.225.
* Langmuir Adsorption Isotherm Model  
The Langmuir isotherm is an empirical model that assumes the thickness of the adsorption layer is one molecule (monolayer adsorption), and the adsorption is homogeneous, with each molecule having adsorption activation energy and constant enthalpy. All adsorption sites have the same affinity for the adsorbate, and there is no migration of adsorbate on the surface plane where no adsorbate is present. Therefore, as adsorption progresses, the number of adsorption sites gradually decreases, and the adsorption rate gradually slows down until reaching adsorption equilibrium [23], as shown in Equation (1):  
\[Q_{e}=\frac{Q_{m}K_{L}C_{e}}{1+K_{L}C_{e}}, \tag{1}\]  
Converted to a linear form:  
\[\frac{C_{e}}{Q_{e}}=\frac{C_{e}}{Q_{m}}+\frac{1}{Q_{m}K_{L}}, \tag{2}\]  
In the provided equations, the symbols have the following meanings: \(Q_{e}\) denotes the adsorbed amount (mg/g) of adsorbate per unit mass of the adsorbent at adsorption equilibrium, \(Q_{m}\) represents the maximum adsorbed amount in the saturated state (mg/g), \(C_{e}\) signifies the concentration of the adsorbate in the solution at adsorption equilibrium (mg/L), and \(K_{L}\) is the Langmuir constant (L/mg), which reflects the affinity of the adsorbent for the intercept.  
* Freundlich Adsorption Isotherm Model:  
The Freundlich model is an empirical model that assumes that the adsorption process is a monolayer adsorption that occurs on an uneven surface and is suitable for lower concentration. The disadvantage is that the maximum adsorption capacity cannot be estimated [24], as shown in Equation (3):  
\[q_{e}=K_{f}C_{e}^{1/n}, \tag{3}\]  
Converted to a linear form:  
\[logq_{e}=\frac{1}{n}logC_{e}+logK_{f}, \tag{4}\]  
In the given equations, \(K_{f}\) and \(n\) are Freundlich constants. The magnitude of \(K_{f}\) serves as an indicator of the adsorption affinity and the maximum adsorption capacity of the adsorbent to a certain extent. A higher \(K_{f}\) suggests a greater maximum adsorption capacity. The value of n reflects the level of difficulty in adsorption, with a larger \(n\) indicating easier adsorption. Typically, the value of n for an effective adsorbent falls within the range of 2 to 10 [25]. By plotting log \(Q_{e}\) against log \(C_{e}\) and applying linear fitting, n can be determined from the slope, and \(K_{f}\) can be obtained from the intercept [26].
## 4 Results and Discussions
### Preliminary Tests of Chlorophyll a Measurements
#### 4.4.1 Fabricated Device  
The portable and low-cost fluorescent detection system was fabricated using four subsystems: (1) the illumination system containing two commercial LEDs with maximum emission peak centered at 450 nm; (2) the photodetection system containing a low-noise and high-quantum-efficiency photodiode with a 680 nm optical bandpass filter for supressing the excitation signal at 450 nm and improving the sensitivity by drastically reducing the background noise; (3) the power source batteries; and (4) the electronics system for readout and control, as described in Section 4.4.2.  
The optical-electronic system was assembled in a 90\({}^{\circ}\) configuration using a 3D printed support ensuring the correct alignment and position of the optical components. It was designed with dimensions suitable for analysis in standard cuvettes. At this early stage of development, the control and readout electronics were soldered in a PCB placed externally to the assembled device (Figure 11).  
Figure 10: Size as a function of complexity for each of the phytoplankton species, obtained by flow cytometry: (**a**) FL1-525 nm (green); (**b**) FL2-575 nm (yellow); (**c**) FL3–620 nm (orange); (**d**) FL4–675 nm (red).
## 3 Results
### Modeling Method to Analyze the Index Differences between Two Softening Methods  
In order to compare the differences between the two methods of soft ripening better over time, based on 8 test indices, the equation modeling of the two methods over time was carried out. By comparing the similarity of the two curves, it is speculated whether the test index will produce significant differences due to different treatments. Figure 10a-h shows the mathematical model of each developmental index in the soft ripening stage of 'Ganlv-2'. We can observe that except for the titratable acid, the change trends of all other indices were almost the same, with only a certain difference in the degree of change and the time of the inflection point. The soluble solids, dry matter and flesh cohesiveness of the on-vine group overtook the cold group at 28 d, and the sensory evaluation was earlier, at 14 d. The soluble sugar content of the on-vine group exceeded the cold group at 48 d. Titratable acid showed opposite change trends with soluble sugars and soluble solids between the two groups, but the relative relationship shifted at 42 d. In general, the trends in most indices were similar, showing that the test indices do not show significant differences by treatment. The KMO measure of sampling adequacy of PCA was 0.87, which showed that the raw data were suitable for PCA. PCA showed that sensory evaluation was highly correlated with dry matter and flesh cohesiveness (0.028 and 0.013, \(p<0.05\)), and that dry matter and flesh cohesiveness have a greater impact on sensory evaluation. Among all factors, the eigenvalue of two factors were >1 (5.594 and 1.194), contributing 69.92% and 14.93%, respectively. The cumulative contribution rate of the first three principal components reached 92.05%, representing the vast majority of variables (Figure 10i-k).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Aroma Substance** & **KI\({}_{\text{CT}}\)/KI\({}_{\text{LT}}\)** & **Aroma Description** & **Qualitative Method** \\ \hline Hexanal & 1015/1040 & Herbaceous flavor & MS/KI/St \\ Octanal & 1243/1255 & Orange flavor & MS/KI/St \\ D-Limonene & 1008/Ne & Sweet fragrance, lemon flavor & MS/St \\
2-Carene & 1023/Ne & Herbaceous flavor & MS/St \\ \hline \hline \end{tabular} Note: ‘MS’ represents mass spectrum qualitative. ‘KI\({}_{\text{CT}}\)’ represents the calculated Kovats retention index. ‘KI\({}_{\text{LT}}\)’ represents the retention index of reference. ‘St’ represents the qualitative analysis of standard materials. ‘Ne’ indicates that no reference was found in the literature.  
\end{table}
Table 2: The aroma substances of evident characteristics.  
Figure 9: Results of the sensory evaluation. (**a**) On-vine group. (**b**) Cold group. The final score is the sum of the average scores of all organoleptic properties.
## 2 Modeling of Receiver Pulsed Environment Interference Effect Based on BER
### BER Formula under Pulse Signal Interference
According to the mathematical character of (17)  
Figure 4:2  
we can derive:  
Figure 4:3  
Its physical meaning is that when the SIR tends to zero, that is, when the interfering signal tends to infinite, the BER under this condition reaches the maximum value only related to the pulse duty cycle and the receiver symbol period.
## 4 DE-KELM-Based Gearbox Fault Classification
### DE-KELM-Based Fault Classification  
The kernel parameter \(g\) and regularization factor \(C\) are two important parameters determining training effects and classification accuracy of KELM; thus, their values need to be well determined.  
In this section, a Differential Evolution (DE) algorithm, conceptualized by [PERSON] in 1996 [46], was adopted to optimize the values of these two parameters because of its faster convergence speed and better robustness than GA [47].  
Like GA, the algorithm of DE consists of two phases: population initialization and evolution. In the latter phase, mutation, crossover, and selection operations are performed. During the selection operation, all vectors in the population are evaluated by a fitness function, and only vectors with high fitness values would be selected to the next generation. These three operations are repeated until a predefined termination condition is met. More detailed iterative process of DE can be found in References [46; 47; 48].  
When applying DE to optimize the kernel parameter \(g\) and regularization factor \(C\) of KELM, the workflow is shown in Figure 11.  
Figure 11: Workflow of DE-based KELM parameter optimization.
## 3 Results
### Radiation Dose of the Patient  
#### 3.2.1 Effective Dose of the Patient  
The ED of the patient was calculated using the parameters of the used imaging devices and a conversion factor. Statistical analysis showed a significant difference in the ED of patients operated with iCT and C-arm fluoroscopy (Figure 5). The median ED during surgeries with iCT was 8.869 (5.929-10.639) mSv, while during surgeries with C-arm fluoroscopy the ED of the patient was 2.274 (1.453-3.472) mSv. Therefore, the median ED of the patient was four times higher during surgery with iCT, compared to C-arm fluoroscopy.  
Figure 4: Effective dose (ED) of the anesthesiologist during an MI-PLIF procedure. The ED of the anesthesiologist (\(\upmu\)Sv) compared between surgeries assisted by iCT (\(n=24\)) or C-arm fluoroscopy (\(n=42\)). Extreme values are marked as \"\(\bigstar\)\" and outliers are marked as \"\(\circ\)\" on the boxplot. Significance (\(p<0.05\)) is indicated with \"\(\bigstar\)\" above the two boxplots.  
Figure 5: Effective dose (ED) of the patient during an MI-PLIF procedure. The ED of the patient (Sv) compared between surgeries assisted by iCT (\(n=24\)) or C-arm fluoroscopy (\(n=37\)). Extreme values are marked as \"\(\bigstar\)\" and outliers are marked as \"\(\circ\)\" on the boxplot. Significance (\(p<0.001\)) is indicated with \"\(\bigstar\)\" above the two boxplots.  
[PERSON]'s correlation analysis showed a moderate correlation (r = 0.438) between the patient's BMI and the ED of patients who had an MI-PLIF surgery performed with iCT. However, for patients who had an MI-PLIF with C-arm fluoroscopy, the correlation was only very weak (r = 0.071) (Figure 6).
## 6 Simulation Results
### Simulation Results of the Proposed 2 WF-SSRT Topology
#### 6.3.1 Disconnection of GS-VSC\({}_{1}\)  
Grid side VSC\({}_{1}\) is subjected to a permanent three-phase fault and thus it is disconnected from SS ring of 2 WF-SSRT following the operation of CB\({}_{34}\) and CB\({}_{12}\). Power flow and the dc voltage profiles of the proposed topology under this scenario are shown in Figure 13. Power extractions from the WFs remain constant while the power flow through GS-VSC\({}_{1}\) drops to zero as depicted with the dashed line. However, the flow of power via GS-VSC\({}_{2}\) and GS-VSC\({}_{3}\) increased smoothly to \(-1.0\) pu and \(-1.1\) pu from \(-0.7\) pu and \(-0.8\) pu, respectively, because of SS ring beauty. No sharp spikes are experienced. In addition, no violation of Great Britain's security and quality of supply standards is observed.  
Figure 12: Line–line dc fault on cable L\({}_{56}\).
# A Combined Full-Reference Image Quality Assessment Method Based on Convolutional Activation Maps
## 2 Proposed Method  
The proposed approach is based on constructing feature vectors from each convolutional layer of a pretrained CNN for a reference-distorted image pair. Subsequently, the convolutional layer-wise feature vectors are concatenated and mapped onto perceptual quality scores with the help of a regression algorithm. In our experiments, we used the AlexNet [4] pretrained CNN which won the 2012 ILSVRC by reducing the error rate from 26.2% to 15.2%. This was the first time that a CNN performed so well on ImageNet database. The techniques, which were introduced in this model, are widely used also today, such as data augmentation and drop-out. In total, it contains five convolutional and three fully connected layers. Furthermore, rectified linear unit (ReLU) was applied after each convolutional and fully connected layer as activation function.
## 5 Eliminating B-O LID during Cell and Module Fabrication
### B-O LID Mitigation during Module Encapsulation  
Alternatively, LID mitigation can potentially be performed during encapsulation. Doing so has the advantage that module encapsulation is the last process in the fabrication sequence, therefore avoiding any subsequent destabilisation of the passivated defects during fabrication. LID mitigation can be performed using either current injection or with illumination, noting that in the latter case, significant modifications to existing lamination tools are required to enable illumination, potentially using transparent diaphragms. As a result, it would appear that implementing LID mitigation processes during module encapsulation using current injection is a much simpler approach. Using this method, [PERSON] et al. demonstrated an 80% reduction in the extent of LID compared to a 60% reduction for regeneration processes performed at the cell level [15]. Although no processing details were given, the current requirements during lamination are on the order of 1 sun equivalent to have complete regeneration of the defects.
# Transitioning Broadcast to Cloud
## 2 Processing Chains in Broadcast and Cloud-Based Online Video Systems
### Video Formats and Elementary Streams
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Format Characteristic** & **Broadcast Systems** & **Online Platforms/Streaming** \\ \hline \multirow{3}{*}{Temporal sampling} & SD: interlaced (bff), telecine & \\  & HD: progressive, interlaced & \\  & (tff), telecine & \\ \hline \multirow{3}{*}{Framerates [Hz]} & 24,000/1001, 24, 25, & \multirow{3}{*}{\begin{tabular}{c} same as source, typically \\ capped at 30 Hz or 60 Hz \\ \end{tabular} } \\  & 30,000/1001, 30, 50, & \\  & 60,000/100,001, 60 & \\ \hline \multirow{3}{*}{Display Aspect Ratio (DAR)} & SD: 4:3, 16:9 & \multirow{3}{*}{\begin{tabular}{c} same as indicated by the \\ source \\ \end{tabular} } \\  & HD: 16:9 & \\ \hline \multirow{3}{*}{Sample Aspect Ratio (SAR)} & SD: 1:1, 12:11, 10:11, 16:11, & \multirow{3}{*}{\begin{tabular}{c} 1:1 or nearest rounding to 1:1 \\ SARs are usually preferred \\ \end{tabular} } \\  & 40:33, 24:11, 32:11, & \multirow{3}{*}{\
\begin{tabular}{c} 1:1 or nearest rounding to 1:1 \\ SARs are usually preferred \\ \end{tabular} } \\  & 15:11, 64:33, 160:99 & \\  & HD: 1:1 (most common), 4:3 & \\  & (1440 mode) & \\ \hline \multirow{3}{*}{Resolutions} & SD (NTSC-derived): 480i & \multirow{3}{*}{
\begin{tabular}{c} same as source + down-scaled \\ versions; additional \\ restrictions may apply [45; 46] \\ \end{tabular} } \\  & SD (PAL, SECAM-derived): & \\  & EBU [49; 50] & \\  & HD: ITU-R BT.709 [51] & \\ \hline \multirow{3}{*}{Color matrix} & SD: ITU-R BT.601 [50] & \multirow{3}{*}{ITU-R BT.709} \\  & HD: ITU-R BT.709 [51] & \\ \hline \multirow{3}{*}{Transfer characteristic} & SD (NTSC-derived): power & \multirow{3}{*}{ITU-R BT.709} \\  & law 2.2 & \\ \cline{1-1}  & SD (PAL, SECAM-derived): & \\ \cline{1-1}  & power law 2.8 & \\ \cline{1-1}  & HD: ITU-R BT.709 [51] & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of video formats used in broadcast and Internet streaming. Significantly different are also GOP (Group of Pictures) lengths. In broadcast, GOPs are typically 0.5 s, as required for channel switching. In streaming, GOPs can be 2-10 s long, typically limited by lengths of segments used for delivery. Broadcast streams also carry more metadata. They typically include relevant video bitstream verifier (VBV) [54] or hypothetical reference decoder (HRD) parameters [55], picture structure-, picture timing-, and colorimetry-related information [55]. They also carry CEA 608/708 closed captions [61; 62] and active format descriptor (AFD)/bar data information [63; 64; 65]. For streaming most may be omitted. Finally, there are also important differences in pre-processing. Broadcast encoders are famous for the use of denoisers, MCTF-filters, and other pre-processing techniques applied to make compression more efficient [24; 53]. In streaming, the use of such techniques is only beginning to emerge.
# Compliant Human-Robot Collaboration with Accurate Path-Tracking Ability for a Robot Manipulator
## 2 Problem Formulation and Methodology
### Inner Motion Control Loop
Moreover, (5) and (6) can be combined into one equation:  
\[\ddot{u}_{1}+\Lambda_{1}\dot{u}_{1}+\Lambda_{0}u_{1}=-(K_{s}\sigma+\rho) \tag{9}\]  
where \(\Lambda_{1},\Lambda_{0}\in\mathbb{R}^{n\times n}\) are chosen such that the left-hand side of (9) is a stable system. For example, \(\Lambda_{1},\Lambda_{0}\) can be chosen as diagonal matrices with positive diagonal elements. From (4) and (9) we see that, unlike traditional SMC, the switching term \(\rho\) does not appear in the joint torque directly. Instead, it is filtered by (9) before being incorporated into the joint torque. Therefore, the chattering phenomenon is significantly alleviated. Once the sliding surface is reached, i.e., \(\sigma\equiv 0\), the switching term is equivalent to the lumped effect of model uncertainty and external disturbance, and the closed-loop system can be expressed as  
Figure 2:0  
Figure 2:1  
For ease of illustration, let us temporarily assume that \(\Lambda_{1},\Lambda_{0}\), and \(C_{1}\) are diagonal matrices, i.e., \(\Lambda_{i}=diag(\lambda_{i1},\cdots,\lambda_{in})\), \(i=0,1\), and \(C_{1}=diag(c_{11},\cdots,c_{1n})\). Then, the closed-loop system (10) and (11) can be expressed as Figure 3 below. It is clear from Figure 3 that the closed-loop system is stable. In addition, the switching term, which is equivalent to uncertainty and disturbance in the sliding mode, is filtered by a second-order system \(\frac{s+\lambda_{ij}}{s^{2}+\lambda_{ij}s+\lambda_{ij}}\) and affects the tracking error through a first-order system \(\frac{1}{s+c_{ij}}\). Although the tracking error is not guaranteed to converge to zero, we can choose appropriate gain matrices \(\Lambda_{1},\Lambda_{0}\), and \(C_{1}\) such that the tracking error is as small as desired. Besides the chattering attenuation property, another appealing feature of the proposed modified SMC is that if the initial tracking error is known, we can set \(\eta(0)=-\ddv{\widetilde{q}}(0)-C_{1}\widetilde{q}(0)\). As a result, the system is on the sliding surface as it starts, which eliminates the reaching phase of the traditional SMC and reduces the transient response time. Experimental verification conducted in [30] showed that the proposed modified SMC has the best tracking performance in comparison with traditional first-order SMC (FOSMC), second-order SMC (SOSMC), and integral SMC (ISMC). Its chattering reduction ability is much better than FOSMC and ISMC, and similar to SOSMC. However, the proposed modified SMC does not need to find bounds to the time derivative of uncertainties as SOSMC does. Therefore, the proposed control law induces less vibration and achieves the best tracking performance with a simple structure.
# A Systematic Literature Review on Military Software Defined Networks
## 5 Additional Contributions
### Software Defined Radio
[141] discussed a range of issues that have been identified thus far within the European Commission Seventh Framework Programme project known as EULER, which seeks to demonstrate the benefits of software defined radio technology to support the resolution of natural disasters of significant stature. In particular, the perceived pan-European interoperability of public safety, and coordination with military devices and networks. Aspects of interoperability are also extended to the three dimensions of platform, waveform, and information assurance. [PERSON] et al. [142] analyzed if an added value can be provided to the operators, by SDRs hosting an \"enhanced\" legacy waveform, where this is introduced in a way that guarantees the interoperability with the legacy equipment. While the legacy waveform acts as base-layer, some enhancement-layers offer an extra budget for the transmission of additional information. This spare budget can be exploited in order to increase the data rate (i.e., throughput), the error robustness (and with this communication range), or both. [PERSON] et al. [143] presented mobile ad hoc networks in latency-and bandwidth-demanding mission-critical applications, while the authors analyzed and validated efficient and low-complexity remedies to those issues, and validated their results based on field experiments carried out using SDR platforms. Compared with the classical Mobile ad hoc network (MANET) routing schemes, autonomous cooperative routing (ACR) was shown to offer up to two times better throughput and more than four times reduction in end-to-end latency. Finally, [PERSON] et al. [144] studied the opportunistic utilization of low-altitude unmanned aerial platforms equipped with base stations (i.e., drone-BSs) in future wireless networks. In particular, the authors envisioned a multi-tier drone-cell network complementing the terrestrial HetNets. Furthermore, they investigated the advancements promised by drone-cells, and discussed the challenges associated with their operation and management.
## 6 Results and Discussion
### Test Using ISCXIDS2012
#### 6.1.2 Effect of Activation Function in Output Layer Neurons  
The effect of using different activation functions in output layer neurons of the BDLSTM-autoencoder model on DDoS detection was investigated using sigmoid ReLU or SoftMax (Table 5).  
Figure 4: Training and validation accuracy and confusion matrix of DDoS detection results under the BDLSTM-autoencoder model. (**a**) Training and validation accuracy for (60, 20, 20) splitting, (**b**) confusion matrix for (60, 20, 20) splitting. (**c**) Training and validation accuracy for (70, 15, 15) splitting, (**d**) confusion matrix for (70, 15, 15) splitting. (**e**) Training and validation accuracy for (80, 10, 10) splitting, (**f**) confusion matrix for (80, 10, 10) splitting.  
As shown in Figure 5 and Table 5, the experiments found that when the output layer neurons use the sigmoid activation function, detection accuracy is better than when using activation functions, such as ReLU, SoftMax, or tanh. The sigmoid function better solves the linear bottleneck problem, and the resulting model is easier to train.