esa-sceva/satcom-chunk-collection huggingface dataset:
# Towards Flexible Distribution Systems: Future Adaptive Management Schemes
###### Abstract
A holistic multi-level systemic view is required on the future enabling technologies [1, 2, 3], control and management methods, operation and planning principles, regulation as well as market and business models. In this way, the whole energy system costs are minimized, and the collaborative societal and customer value is maximized during the energy system evolution. However, this requires the combination and coordinated use of flexibilities from all voltage levels (LV, MV, and high-voltage, HV) and different energy sectors with different time scales. Digitalization and advanced ICT technologies such as data analytics, AI, and ML as part of future monitoring and management solutions can enable better fulfillment of customers' individual needs and realization of more accurate flexibility forecasts, new energy services, market schemes, and business models. In this paper, the evolution toward a flexible distribution system is divided into three stages. The evolution during these stages is strongly dependent on the simultaneous power system readiness level (PSRL), which consists of (i) technology/solutions, (ii) customer, (iii) regulation, and (iv) market and business model readiness, as shown in Figure 1.  
In order to achieve future flexible and digitalized electricity distribution systems as a key part of the whole power/energy system evolution (stage 3 in Figure 1), simultaneous complementary development of all areas (i)-(iv) (Figure 1) is required. For example, technology/solutions development alone will not enable the evolution. In this paper, however, the main focus is on the development and potential evolution of different technologies and solutions, especially regarding the evolution of DER/distribution network control and management schemes as well as compatible market structures. At first, Section 2 of this paper describes the potential three-stage evolution path toward fully flexible and digitalized electricity distribution networks. After that, Section 3 presents the potential evolution of future adaptive control and management methods for the improved provision of local and system-wide services by distribution network-connected flexibilities. The target of these methods is to maximize the availability of all distribution network-connected flexibilities for different DSO and TSO flexibility needs through future market schemes (Section 4). Finally, conclusions are stated in Section 5.
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Radar Site**} & **Haikou** & **Hualian** & **KLIX** & **KKAQ** & **Guangxi** & **Zhangbei** & **Datong** & \multirow{2}{*}{**Total**} \\ \cline{2-9}  & **(T, S)** & **(T, S)** & **(H, S)** & **(H, S)** & **(SL, S)** & **(SL, C)** & **(HS, C)** & \\ \hline POD\_ADDA & 98.68 & 98.32 & 99.04 & 99.3 & 96.55 & 91.73 & 92.42 & 95.78 \\ POD\_CIDA & 84.32 & 88.79 & 97.36 & 96.29 & 90.1 & 89.45 & 88.67 & 89.02 \\ FAR\_ADDA & 2.04 & 2.47 & 1.63 & 1.02 & 5.97 & 9.3 & 8.81 & 5.75 \\ FAR\_CIDA & 16.92 & 12.38 & 3.11 & 4.33 & 10.73 & 11.56 & 12.19 & 11.94 \\ CSI\_ADDA & 97.46 & 97.91 & 98.28 & 98.97 & 94.02 & 90.25 & 91.34 & 94.09 \\ CSI\_CIDA & 83.2 & 87.66 & 97.07 & 96.51 & 89.89 & 88.01 & 87.56 & 88.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: List of dealiasing results (unit %) for all seven cases using automated dealiasing for data assimilation (ADDA) algorithm (probability of detection (pod)_ADDA, false alarm rate (far)_ADDA, and critical success index (csi)_ADDA) and the original China radar network (CINRAD) improved dealiasing algorithm (CIDA) (pod_CIDA, far_CIDA and csi_CIDA). (T: typhoon, H: hurricane, SL: squall line, HS: hailstorm; S: S-band, C: C-band). Overall, the new ADDA algorithm exhibited a reliable performance for five S-band radar systems and two C-band radar systems, despite being from different networks with different scanning strategies. ADDA's ability to dealiase the C-band radar data was reasonably good. It is worth noting that no dealiasing algorithm can do a perfect job, but fortunately, most data assimilation systems have general quality check schemes to get rid of suspicious data using model backgrounds as a necessary step for cleaning up the data prior to assimilation.
# Network Traffic Prediction Incorporating Prior Knowledge for an Intelligent Network
## 5 Experiments and Analysis of Results
### Experimental Parameters and Evaluation Metrics  
In this experiment, a deep learning server was used to configure the experimental environment, where the CPU production type was AMDRyzen 52,600 and the memory size was 16 GB. In addition, Pytorch was used to build the network framework and Python was the programming environment. In the experiments, the optimizer was selected as the SGD optimizer, with outstanding speed in late iteration. The learning rate was set to 0.001, hidden_size was set to 128, batch_size was set to 64, and the number of training epochs was 20. The detailed model structure parameters were configured as shown in Figure 6.  
Figure 5: Flow diagram for BPS perspective in Agurim.  
Figure 6: Model structure parameter configuration.  
When training the model, the L2 norm was used as the loss function, and a regularization term was added to prevent overfitting. The formula is as follows:  
\[loss=\
orm{Y_{i}-\hat{Y}_{i}}{\
orm{2}+\lambda L_{reg}} \tag{16}\]  
where \(Y_{i}\) denotes the true value, \(\hat{Y}_{i}\) denotes the predicted value, \(\lambda\) is the hyper-parameter, and \(L_{reg}\) is the canonical term. To verify the validity of the model, common methods used in the field of traffic prediction were selected for experimental comparison, including four algorithms: historical average (HA), autoregressive integrated moving average (ARIMA), support vector machine (SVM), and long short-term memory (LSTM). These were evaluated using four common serial predictors, with \(y_{i}\) denoting the true value and \(\overline{y}_{i}\) denoting the predicted value.  
1. Squared absolute error (_MAE_): This indicator measures the mean absolute error between the error and the true value, taking values in the range of \([0,+\infty)\); the closer the MAE is to 0, the better the performance of the model: \[MAE=\frac{1}{m}{\sum\limits_{i=1}^{m}{{{\left|{y_{i}-\overline{y}_{i}}\right|}}}}\] (17)
2. Mean square error (_MSE_): This indicator reflects the prediction error of the model, taking a value in a range of \([0,+\infty)\); the smaller the error, the better the model performance: \[MSE=\frac{1}{m}{\sum\limits_{i=1}^{m}{{{\left({y_{i}-\overline{y}_{i}}\right)} ^{2}}}}\] (18)
3. The root mean square error (_RMSE_): This indicator reflects the prediction error of the model, taking a value in the range of \([0,+\infty)\); the smaller the error, the better the model performance: \[RMSE=\sqrt{\frac{1}{m}{\sum\limits_{i=1}^{m}{{{\left({y_{i}-\overline{y}_{i}} \right)}^{2}}}}}\] (19)
4. The absolute coefficient of correction (\(R_{adapted}^{2}\)): This indicator reflects the quality of the model fit, taking values in the range \([0,1]\); the closer to 1, the better the model performance. Here, \(m\) is the total number of samples and \(p\) is the number of features: \[R_{adapted}^{2}=1-\frac{(1-R^{2})(m-1)}{m-p-1}\] (20)
# A Novel Ranging Technique Based on Optical Camera Communications and Time Difference of Arrival
## 2 Brief Literature Review
### Photodiode-Based Visible Light Positioning
The proposed scheme uses the captured frames in an entirely different manner, allowing camera-based positioning algorithms combining the intrinsic angle estimation of image sensors and distance measurement.
## 5 Tomato Bioactives as Ingredients in Sun Oils for Skin Care
### In Vivo Studies--Main Findings
#### 5.3.3 L-Ascorbic Acid  
The skin is protected from UV light by the increased amounts of L-ascorbic acid obtained through topical application. L-ascorbic acid can reduce UVB- or UVA-induced photodamage, which is quantified histologically as sunburn cells; protection against UVA damage appears to be particularly satisfactory. L-ascorbic acid functions differently from sunscreens since it does not absorb UVB or UVA rays. Through the neutralization of reactive oxygen species produced by UV light, L-ascorbic acid appears to offer protection. Even sunburns can be treated with topical ascorbic acid, probably via reducing inflammation. According to the results thus far, topical vitamin C is a helpful addition to sunscreens. Because of its anti-inflammatory and photoprotective properties, topical vitamin C is employed. L-ascorbic acid is used for its ability to heal wounds since it is necessary for the creation of collagen [49; 50; 51; 52]. Patients having CO\({}_{2}\) laser resurfacing have also employed topical vitamin C. In half-face research that was published, the side that had topical I-ascorbic acid showed reduced erythema.  
In contrast to most sunscreens, vitamin C cannot be removed from the skin by rubbing, washing, or sweating. The protection offered by this vitamin remains unchanged, in the span of a couple of days. In addition, topical vitamin C inhibits UV immunosuppression, which has been linked to skin malignancies other than melanoma. It involves the following: using ascorbate's oxidation capacity to neutralize ROS radicals in the skin's aqueous compartments; minimizing the development of sunburn cells, erythema, and immunosuppression; preventing tyrosinase synthesis; and preserving hydration to safeguard the skin's epidermal barrier. Despite its positive effects, L-ascorbic acid shows insufficient skin penetration and instability [53; 54].
## 5 Physical Test Results  
The observed deformation and displacement vector after each stage of mining simulation are shown in Figures 9-12. After mining the ore body #1 (see Figure 9), the blocks that were right above the mining area collapsed and fell off, as expected, due to the loss of support. Then, the strata overlying this layer of blocks underwent a substantial downward movement, which caused separation of the overlaying strata along the flat joints. There was no change in the shape of slope surface possibly due to the formation of arching mechanism within blocks (Figure 9a), Figure 9b also shows that the strata deformation has not transferred to the slope surface. After mining the ore body #2 (Figure 10), the strata deformed further towards the open area created by the previous step of mining process. The strata right above this area of mining collapsed almost vertically, introducing significant subsidence pit on the slope surface. This mining process did not introduce any surface subsidence near the crest of the model slope, but the strata deformation transferred to the slope surface (Figure 10b). After mining the ore body #3 (Figure 11), the strata right above the mined area was extensively fractured and more blocks right above collapsed. An arch was formed to support the blocks away from the mined area (Figure 11a). Although there was little or no surface subsidence near the crest of the slope, two rock joints were expanded laterally in size, forming two prominent vertical cracks in this slope section. A toppling avalanche was found near the toe of the slope. The surrounding blocks displaced toward the mined area, and local sliding occurred along the slope surface right above the ore body #3. Finally, after mining the ore body #4 (Figure 12), the strata above the mined area collapsed further, though interestingly, the arch formed in the previous stage of mining remained intact and was apparently unaffected by this last stage of mining (Figure 12a). Substantial amount of rock mass was fallen into the mined area, resulting in large slope surface subsidence at middle of the slope. Blocks near the subsidence pit experienced intensive disturbance and started to slip along the flat joint surface. At the end of the mining processes, a subsidence pit was formed right above the mining area (Figure 13) due to the significant vertical downward movement of strata. Due to the existence of predominant joints, the observed failure mode differs from the trumpet-shaped subsidence experienced in slopes made of unconsolidated soil [30].  
Figure 9: Slope deformation and displacement vector after mining ore body #1.  
Figure 11: Slope deformation and displacement vector after mining ore body #3.  
Figure 12: Slope deformation and displacement vector after mining ore body #4.  
Figure 10: Slope deformation and displacement vector after mining ore body #2.
# Energy Efficient Load-Balancing Mechanism in Integrated IoT-Fog-Cloud Environment
## 5 Results and Discussion  
The rigorous performance evaluation of the above-suggested algorithm is carried out based on the above-mentioned parameters, as shown in Table 2, and compared with existing EEHFC and LBS techniques. The network lifetime of all nodes and all links used in the network is computed for the proposed technique and compared with other schemes, as shown in Figure 4. It is observed that the network lifetime of the proposed scheme is enhanced because it considers efficient energy consumption and load balancing among various devices at different levels in the network.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Values Used for Simulation** \\ \hline Total end devices & 50 to 100 \\ Total fog devices (F) & 10 to 100 \\ CPU frequency in cloud servers & \(100\times 100\) [5 cyc/sec] \\ Total gateway devices (G) & 2 \\ The average number of incoming tasks (\(\lambda\)i) & 50 [tasks/sec] \\ Maximum channel bandwidth & 30 MHz \\ Total cloud servers (S) & 5 \\ CPU frequency in end devices & \(500\times 106\) [cyc/sec] \\ CPU frequency in fog devices & \(50\times 109\) [cyc/sec] \\ Processing energy usage & 0.5 Joules \\ The transmission power of end devices & mW \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameter values used for simulation.  
Energy consumption has been optimized for the proposed scheme, as shown in Figure 5, as compared with other existing schemes. This is because loads are balanced across different levels. If fog nodes are processing the requests of IoT devices, then the data traffic is not forwarded to the cloud. Hence, cloud nodes are efficiently utilized. The energy consumption of the proposed EEHFC and LBS techniques is represented in Figure 5. Energy usage is computed with a varying number of devices, from 50 to 500. The energy of the nodes is drained out with an increase in data traffic.  
The average delay of various techniques is shown in Figure 6. Delay comprises transmission delay and computational delay at both the fog layer and the cloud layer. The proposed algorithm is focused on achieving load balancing between fog-cloud tiers. Delay is reduced as tasks are offloaded and uploaded from the fog to the cloud layer. It is visible  
Figure 4: Network Lifetime vs. Traffic Load.  
Figure 5: Energy Consumption vs. No. of nodes.  
in Figure 6 that the delay is increasing as more and more nodes and data flow through the network.  
The information shown in Figure 7 indicates that response time is reduced with the number of nodes as a result of load balancing. This is due to the deployment of fog devices in the fog layer, which reduces the response time by distributing the load among fog devices and forwarding traffic load to the cloud layer. The response time of the fog node altered as the load on the various fog nodes increased or decreased. The response time variation in many settings demonstrates the performance of the suggested algorithm, and the findings demonstrate that the proposed technique beats competing algorithms in different scenarios.
## 2 Methodology
### Recording Audio Data
The acoustic signals are divided into frames of 27 ms (1200 samples, empirically determined to encompass the duration of a typical touch input impulse, cf. Figure 3) and for every frame a number of'short term' features are extracted. These features include mel frequency cepstral coefficients (MFCCs), chroma vectors, zero crossing rate (ZCR), energy, energy entropy, spectral entropy, spectral flux, spectral roll-off, spectral spread, spectral centroid and chroma deviation. Altogether, 34 acoustic features (13 MFCCs, 13 Chromas, five spectral features, energy, entropy and zero crossing rate) were extracted for every frame totalling 68 features from both acoustic channels. The details regarding these features can be found in [20,21]. Two noteworthy sets of acoustic features used in training are MFCCs and chroma vectors with significant contribution towards touch input categorization. MFCCs focus on the perceptually relevant aspects of the audio spectrum commonly used speech/speaker recognition estimating energy in various region of audio spectrum over a set of overlapped non-linear mel-filter bank. Chroma vectors characterise energy distribution across cyclical frequency bins [22]. To facilitate feature extraction, we determine a time window around the detected impulse, distinguishing and excluding it from regions of silence or noise. Starting from the detected peak, an empirically determined time buffer is applied backwards to include the start of the impulse. ZCR across a sliding window is used to identify the start of the impulse: the start of an impulse is associated with a low ZCR value as shown in Figure 5, where we apply an empirically determined ZCR threshold of 0.015. To ensure we capture the whole impulse, we begin the window with an offset from the start of the ZCR index. An empirically determined window size of 1200 samples (27 ms) with an offset ratio of 30% of the sample number between the initial index and ZCR start (shown in Figure 6) is used to encompass the entire impulse, ensuring important signal features are captured. Figure 4: Signal peaks detected in the acoustic signal.Every tick on the horizontal axis is 500 milliseconds. Figure 5: Acoustic signal (**left**) and typical plot of Zero Crossing Rate (ZCR) values (**right**) for the selected region in the signal. ZCR values are typically quite large in region of silence (ambient noise) and decreases when a tap occurs creating an impulse. Data normalization was performed on the extracted features to ensure the contribution of each feature is equally weighted and this avoids bias that could occur across recording sessions.
# Receiver-Initiated Handshaking MAC Based on Traffic Estimation for Underwater Sensor Networks +
## 2 Applications, Motivations, and Challenges
### Challenges in Receiver-Initiated MAC  
Even though the receiver-initiated MAC design has its advantages over the traditional handshaking methods in many ways, it still faces great challenges in the implementation of data polling. Data polling is defined as the process whereby the receiver retrieves data from the neighboring senders. Since the receiver does not know the status of the neighboring senders, how to design an efficient data polling scheme becomes an open problem. The retrieval of data frequently results in energy waste when transmitting control messages, whereas slowing down the polling rate causes a long queuing delay because the data cannot be delivered timely. An efficient polling scheme needs to decide (1) when packets are available for the receiver to poll, and (2) how much data to request. For most of the receiver-initiated methods, _when_ and _how much_ are two fundamental challenges.  
Furthermore, how to design a protocol that can adapt well to the dynamics and heterogeneity of network traffic is still yet to be explored. On the one hand, the number of queued packets at the link layer is a random variable that may vary with time and among different senders due to the combined effects of all the upper layers, e.g., network layer, transport layer, and application layer. On the other hand, it is not practical in underwater networks to approximate the traffic distribution with a large number of data samples. This is because of the limited computation and memory capacity of underwater sensors and the time-varying feature of network traffic. In TERI-MAC, the cumulative density function (CDF) inversion sampling technique is employed to pull a number of samples from the large history dataset and utilize the most recently observed data to determine the trend of the network traffic. In this way, a lightweight traffic estimation can be achieved to support the adaptive data polling mechanism in TERI-MAC.
# Simulation-Based Reliability Design Optimization Method for Industrial Robot Structural Design
## 2 Simulation-Based Reliability Design Optimization Method (SBRDOM)
### Formulation of the Reliability Design Optimization Model  
The general reliability design optimization model for the industrial robot is presented as follows:  
\[\begin{array}{l}\min f\left(\mu_{\mathbf{x}}\right)\\ \text{s.t.}\Pr[g_{i}(\mathbf{x}_{r},\mathbf{p}_{r})<0]\geq R_{t}\\ \mu_{\mathbf{x}}^{\bot}\leq\mu_{\mathbf{x}}\leq\mu_{\mathbf{x}}^{\text{tr}}\\ i=1,2,\cdots,n\end{array} \tag{1}\]  
where \(f\) is the objective function, \(\mathbf{x}_{r}\) is the vector of the random design variable, \(\mathbf{\mu}_{\mathbf{x}}\) is the vector of the mean values of the random design variable, \(\mathbf{p}_{r}\) is the vector of the random design parameter, \(\Pr\) stands for the probability, \(g_{i}\) is the \(i\)th performance constraint function, \(n\) is the number of the performance constraint function, and the superscripts \(L\) and \(U\), respectively, stand for upper boundary and lower boundary.  
The performance constraint function was obtained by using the approximate model, and therefore \(g_{i}\) was an implicit function. The expression of \(g_{i}\) depends on the selection of the approximate model. Since different approximate models have different accuracy and efficiency, the approximate model to be selected relies on the specific context. In this  
Figure 2: Detailed operation of step 3.  
proposed method, the quadratic polynomial with cross terms was chosen for fitting the performance constraint function, which is given below.  
\[\hat{g}(\mathbf{x})=a_{0}+\sum_{i=1}^{m}b_{i}x_{i}+\sum_{i=1}^{m}c_{i}x_{i}^{2}+ \sum_{i=1}^{m}\sum_{j=i+1}^{m}d_{ij}x_{i}x_{j} \tag{2}\]  
where \(\hat{g}(\mathbf{x})\) stands for the approximation of the performance constraint function, \(a_{0}\), \(b_{i}\), \(c_{i}\) and \(d_{ij}\) are coefficients, \(x_{i}\) and \(x_{j}\) is the \(i\)th or the \(j\)th random design variable, \(m\) is the number of the random design variable.