esa-sceva/satcom-chunk-collection huggingface dataset:
# Communication Optimization Schemes for Accelerating Distributed Deep Learning Systems
## 5 How to Calculate the Threshold  
The threshold used in this paper is a value that can drop the number of gradients transmitted to the parameter server close to a predetermined ratio \(R\). It takes a very long time to calculate the threshold by reflecting all the gradients. Therefore, in this paper, the value of each gradient is replaced with the L1 norm of the gradients included in each hidden layer. This replaces very large amounts of gradients with very small amounts. The same effect can be achieved by using the L2 norm instead of the L1 norm. The L1 norm is the sum of the absolute values of each element of the vector. In the case of the L2 norm, it represents the magnitude of a vector in n-dimensional space, so it can be obtained as the square root of the sum of the squares of each element. Therefore, we use the L1 norm, which is relatively easy to compute. Using the obtained L  
is computed on each worker. Since the threshold value is necessary to compress the gradients, they are stored only in each worker, and the parameter server does not store the threshold information of workers. The calculated threshold value can be used by fixing the value obtained at the beginning of training, or it can be newly calculated at a certain step according to the DNN (Deep Neural Network) model, the type and number of data sets, and the decay setting of the learning rate. In our experiments, a new threshold is calculated every 100 iterations.  
Algorithm 1 is the pseudocode of the algorithm to find the threshold. First, the threshold is initialized to \(-1\). After that, inverse sorting is performed based on the representative value of each hidden layer. The representative value of each hidden layer we used is the L1 norm of the hidden layer. The L1 norm is the sum of the absolute values of the elements of the vector. Therefore, it is possible to express the parameter change due to the gradients included in each layer. When the hidden layer is aligned, the number of gradients to be dropped is initialized to zero. After that, the number of gradients contained in the \(i\)th hidden layer is accumulated and stored in the number of gradients to be dropped. When the number of gradients to be dropped is greater than the _total number of gradients_\(*\)\(R\), the iteration stops. The threshold is the representative value of the last added hidden layer. If the threshold is not found even though it is repeated as many times as the number of hidden layers, the threshold is \(-1\). Therefore, if the threshold is \(-1\), it means an invalid threshold.  
```
1 Result: Threshold \(threshold\leftarrow-1\)
2 Reverse sorting based on the representative value of the hidden layer
3\(i\gets 0\)
4\(numDrop\gets 0\)
5 while\(i<number\ of\ hidden\ layer\)do
6\(numDrop\gets numDrop+size\ of\ ith\ sorted\ layer\)
7 if\(numDrop>total\ number\ of\ gradients\ *\)\(R\)then
8\(threshold\leftarrow\textit{representative value of\ ith\ layer}\)
9 Break
10 end while
11\(i\gets i+1\)
12
13 end while
```  
**Algorithm 1** Threshold searching algorithm.
# A Service-Constrained Positioning Strategy for an Autonomous Fleet of Airborne Base Stations
## 6 Evaluation and Results
### Non-Static Scenarios
#### 6.4.3 Scattering of the Concentration of Users  
In this last non-static scenario, after 2500 iterations, the concentration of users is scattered from a Gaussian to a uniform distribution of users and maintaining the number of users in the scenario. This modification in the distribution of users is carried out by gradually increasing the standard deviation of the Gaussian spatial distribution of the users (0.1 units/iteration) and the number of users in the scenario is controlled with an specific gain that multiplies the Gaussian expression. This scenario could be, for instance, the end of an sporting event, where all the people leave the sporting venue.  
As can be seen in Figure 8, the UAVs tend to distribute uniformly again as in the initial state, which is the expected behavior as the distribution of users tends to also be uniform. Moreover, the results are good, as both the achieved fairness and the rate per user are still high (0.99999 and 9.10 kbps, respectively) and all the constraints are still fulfilled in average.  
Figure 7: Rearrangement of UAVs when subscribers move: positions and coverage areas (**top**); and evolution of the rate per user in each coverage area (**bottom**). Each color in the bottom figure represents one coverage area using the same colors as the ones assigned to letters in the top figure.  
Therefore, from the observation of the performance of the proposed strategy in the three non-static scenarios, it can be concluded that the proposed strategy is also robust to the changes in the scenario, achieving almost the same results as in the static scenario, which is a very important feature as in real life almost all scenarios undergo changes.
## 3 Results and Discussion
### Crystalline and Morphology
The textural features of the synthesized samples were evaluated by N\({}_{2}\) physisorption measurements. As expressed in Figure 6a, the adsorption-desorption isotherm curves of all prepared catalysts are type IV [46], which forms a closed loop with the previous adsorption curve at medium pressure. After analyzing the BET surface areas (Table 2) of the prepared catalysts, the values could be ordered as follows: Au/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\) (266.79 m\({}^{2}\).g\({}^{-1}\)) \(>\) Ir/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\) (230.35 m\({}^{2}\).g\({}^{-1}\)) \(>\) Ru/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\) (229.27 m\({}^{2}\).g\({}^{-1}\)) \(>\) Pt/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\) (227.60 m\({}^{2}\).g\({}^{-1}\))\(>\) Pd/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\) (226.79 m\({}^{2}\cdot\)g\({}^{-1}\)) \(>\) Rh/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\) (219.97 m\({}^{2}\cdot\)g\({}^{-1}\)) \(>\)\(\gamma\)-Al\({}_{2}\)O\({}_{3}\) (207.87 m\({}^{2}\cdot\)g\({}^{-1}\)). Moreover, in terms of pore volume, the catalysts can be ordered as follows: Pt/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\) (0.813 cm\({}^{3}\cdot\)g\({}^{-1}\)) \(>\) Rh/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\) (0.595 cm\({}^{3}\cdot\)g\({}^{-1}\)) \(>\) Au/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\) (0.589 cm\({}^{3}\cdot\)g\({}^{-1}\)) \(>\) Ir/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\) (0.510 cm\({}^{3}\cdot\)g\({}^{-1}\)) \(>\) Pd/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\) (0.502 cm\({}^{3}\cdot\)g\({}^{-1}\)) \(>\) Ru/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\) (0.468 cm\({}^{3}\cdot\)g\({}^{-1}\)) \(>\)\(\gamma\)-Al\({}_{2}\)O\({}_{3}\) (0.436 cm\({}^{3}\cdot\)g\({}^{-1}\)). Generally, a large pore volume is conducive to exposing more active sites, which would be profitable for reactant capture and for improving the anti-carbon deposition ability of the catalyst. As depicted in Figure 6b, the most probable pore sizes of all prepared materials were distributed between 2 and 4 nm, combining the existence hysteresis loop between a \(P\)/\(P_{0}\) of 0.6 and 0.8, suggesting the occurrence of a mesoporous structure.
## 3 Methods
### Experimental Protocol  
To verify the effectiveness of the developed KAEGO system and its motion-based gait recognition and control method, a 10 m walk test (WT) [29] and a 6 min WT [31] were conducted as shown in Figure 6.  
The 10 m walking test was performed four times each on a hemipega patient and one able-bodied person to validate the accuracy and reproducibility of the proposed motion-based gait intention recognition method. During the 10 m walk, the number of swings and the moving speed were measured, and the reliability was verified by comparing the variances between each data set. Additionally, the time difference between pre-swing and swing flexion in each experiment was measured to determine how quickly the proposed motion-based gait intention recognition method identified the gait intention. Comparisons with data from an able-bodied individual were made to confirm the applicability of the proposed gait intention recognition method.  
The 6-min walking test was conducted on one person with hemipega, comparing oxygen consumption (VO\({}_{2}\)) and distance traveled when walking with and without the developed gait-assist device to calculate the metabolic cost of transport (COT) [30]. This test verified the extent to which the developed gait-assist device contributed to the patient's comfortable and stable walking.  
All tests were conducted after ensuring that a patient with hemiplegia had sufficient rest and acclimatization. These tests were performed in accordance with the safety guidelines and approved by the Rehabilitation Engineering Research Institute's Institutional Review Board (RERI-IRB-221102). Additionally, prior to wearing the developed KAEGO, it was recommended that the participant use a bicycle-type lower limb exerciser to reduce muscle tension and enhance flexibility in the knee joint. From a functional perspective, the KAEGO system and existing rehabilitation robots or simple devices are complementary.  
Table 1 shows the characteristics of the patient's disability. The patient suffering from left-sided hemiplegia due to a traumatic brain injury has a Berg Balance Scale (BBS) of 38. He can walk independently with a cane but with caution due to the risk of falling. To adapt to the developed assistive device, the patient engaged in familiarization training for 60 min per session, three times a week, for about four weeks.  
The equipment used to calculate the COT was K4b2 (CosMed, Rome, Italy). Data were gathered while walking at a comfortable speed on a rectangular track for 6 min under the guidance of a professional therapist, as shown in Figure 6b.  
Figure 6: Experimental setup for 10 m and 6 min walking tests: (**a**) 10 m walking test environment; (**b**) 6 min WT and the 60 m track.
## 5 Discussion
### Information Processing: Retention and Recall  
The question of information retention and recall was addressed by testing the depth of knowledge processing. A significant improvement in learning was found between the pre- and the post-learning tests with a considerable improvement in the active recall test in both tests, thereby confirming Hypothesis 3.  
These findings correspond with the Levels of Processing model by [PERSON] and [PERSON] [18], which links the depth of information processing to its retention in memory and recall. The results of the tests, particularly the active recall test, indicate that a high level of processing of new vocabulary occurred within a remarkably short time.  
In addition, previous studies distinguished between levels of processing and found that memorization and repeated exposure were required only in cases of low levels of processing [64]. In contrast, when words were processed deeply, recall was enhanced, and long-term retention improved without the need for repetition or memorization [65]. For example, [PERSON] et al. [56] found that information displayed in space and associatively linked to elements presented in the learning environment increased the depth of processing and memory retention.  
The results of our study indicate that knowledge was retained at all levels of processing. Unlike previous studies, our learning method did not require memorization or repetition.
# Knowledge Sharing in Security-Sensitive Communities
## 7 Limitations and Future Research  
The global spread of the pandemic has created a unique situation. Intelligence groups, which typically develop information on secure platforms, have started working from home and used WhatsApp as their primary method for sharing information. This exceptional access has enabled us to conduct our research using real-world data. However, this study is limited by the lack of knowledge regarding the identity of each participant and the inability to read the actual messages. In addition, our analysis was limited to 18 groups. A more comprehensive study might examine a greater number of groups. Since most of the content was not visible to us, we could do a limited content analysis. For this reason, we expanded the analysis on the networks and tried to understand the little content that was visible to us.  
Besides lifting the veil from the participants and the content of discussions, future research should also expand on the dimension of usability and its delicate balance with the need for security [23]. Choosing WhatsApp as the means of communication may possibly be a sign of having to circumvent rigid systems with restrictive security measures [23]. Future research could address a triangle of challenges: security, information sharing, and usability.
# Adjustment Algorithm for Free Station Control Network of Ultra-Large Deepwater Jacket
## 3 Results and Discussion
### An Overview of the Control Network
#### 3.1.1 The First Phase of Observation  
The first observation phase was carried out in April 2022, when construction of the jacket had not yet started. The sight conditions were very good, with no obstructions between the survey stations and the control points at most locations. The total free station location is mainly located in the middle of the field, surrounded by the control points S1-S8, with a total of six stations, as shown in Figure 3.
## 4 Optimisation Configuration
### Design Variables: Scenario 2  
Regarding the second scenario, where the optimum designs are sought for the different suspension types, the design variables shown in Equations (30)-(32) are selected:  
For PS:  
\[K_{u},C_{u} \tag{30}\]  
For SH-2, ADD, GH-2, PDD and SH-PDD:  
\[K_{u},C_{min}\text{and}C_{max} \tag{31}\]  
For SH-ADD2:  
\[K_{u},C_{min},C_{max}\text{and}a_{f} \tag{32}\]  
while the upper and lower bounds set in the optimisation are illustrated in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{3}{c}{**Scenario 1**} & \multicolumn{3}{c}{**Scenario 2**} \\ \hline \multirow{2}{*}{**Design Variable**} & \multicolumn{3}{c}{**Bounds**} & \multicolumn{3}{c}{**Bounds**} \\ \cline{2-6}  & \(LB\) & \(UB\) & & \(LB\) & \(UB\) \\ \hline \(P[\text{KPa}]\) & 150 & 350 & \(K_{u}\) [N/m] & 15,000 & 60,000 \\ \hline \(c[\text{m}]\) & 0.14 & 0.22 & \(C_{u}\) [Ns/m] & 500 & 5000 \\ \hline \(d[\text{m}]\) & 0.27 & 0.35 & \(C_{min}\)[Ns/m] & 500 & 2500 \\ \hline \(b_{0}[\text{m}]\) & 0.12 & 0.19 & \(C_{max}\) [Ns/m] & 2500 & 5000 \\ \hline \(r_{w}\) [m] & 0.12 & 0.20 & \(\text{a}_{f}\) [rad/s] a  & 10 & 60 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Design variables and their bounds for Cases 1 and 2.
## 1 Introduction
Additionally, the hybrid Shuffled Frog-Leaping Algorithm and Teaching-Learning-Based Optimization (SFLA-TLBO) technique has proven its superiority in tuning PID controllers, achieving faster convergence to optimal controller gains and enhancing system performance under nonlinear conditions and load disturbances [29]. Furthermore, Harris Hawks Optimization (HHO), a bio-inspired algorithm, ensures reliable frequency control even under load disturbances and nonlinear conditions. This highlights the robustness of HHO in maintaining system stability and improving overall frequency control in complex power grids [30]. Altogether, these alternative optimization techniques offer significant advancements in LFC strategies for modern, renewable energy-integrated power systems. Recent developments have explored the use of deep learning to enhance PID controllers for LFC in power systems. A previous paper [31] proposed a multi-agent deep reinforcement learning (MA-DRL) method for cooperative LFC in multi-area power systems, combining centralized learning with decentralized implementation through deep deterministic policy gradient (DDPG). This approach effectively reduces control errors caused by load and renewable energy fluctuations. Following this approach, article [32] introduced a Knowledge-Aggregation-based Proximal Policy Optimization (KA-PPO) method for LFC in isolated microgrids. By integrating existing knowledge with deep learning, this method improves frequency stability, tracking accuracy, and overall control efficiency in systems with high renewable energy penetration. These advancements showcase the potential of deep learning in optimizing PID controllers for modern, renewable integrated power systems. After examining various optimization methods in the field of frequency regulation, this study focuses on applying a PID controller enhanced with the PSO algorithm. This approach improves system stability and response speed in power plants during load fluctuations. The key contributions of the research are summarized as follows:  
* The study employs the PSO algorithm to fine-tune the parameters of the PID controller, specifically targeting frequency control in power plants. The goal is to achieve a more stable system that can quickly recover from load changes, especially in the context of integrating renewable energy sources and facing sudden load variations. * Extensive simulations are conducted on interconnected multi-area power systems under various scenarios to assess the effectiveness of the PSO-PID controller. The simulation process will be conducted on the MATLAB/Simulink platform, version 2023. These simulations focus on evaluating the method's capability in maintaining frequency stability and minimizing deviations during load fluctuations. * The results demonstrate that the PSO-optimized PID controller significantly improves frequency regulation in large power systems. The proposed method exhibits high adaptability and robustness under unpredictable operating conditions, making it a reliable solution for modern power grids.
## 5 Conclusions  
This article proposes a short-term wind power prediction method for VMD-SSA-TCN-BiGRU. The proposed VMD-SSA-TCN-BiGRU prediction model reduces the SMAPE, MAE, and RMSE by 13.24%, 24.03%, and 35.90%, respectively, compared to the SSA-TCN BiGRU model. The hybrid model was proven to be effective in predicting wind power through four evaluation indicators and draws the following conclusions:  
1. VMD can reduce noise in the original power and alleviate the overall lag of the prediction model.
2. The TCN-BiGRU model can effectively extract temporal features and accurately predict wind power. And using SSA to optimize the hyperparameters of the TCN-BiGRU model further improves the performance of the model.
3. This paper verifies the model's validity through the measured data of wind turbine 44 in a domestic wind farm. It conducts a comparative analysis with the remaining eight models, and the results show that the model proposed in this paper has better stability and a higher accuracy.  
Wind power prediction is mainly used for power dispatch, but the information provided by wind power point prediction to power system personnel is limited. In the future, interval prediction [47] based on the point prediction model in this paper can provide more useful credit and better dispatch of the power system.  
Figure 11: Comparison of evaluation indicators of different models.  
**Author Contributions:** Conceptualization, Methodology, Validation, Writing, and Software, Y.Z.; Conceptualization, D.S., K.J. and Y.G.; Supervision and Funding Acquisition, Y.Z. and L.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The authors do not have permission to share data.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Technologies and Fabrication of Intelligent Packaging for Perishable Products
## 2 Intelligent Packaging Techniques
### Sensing Techniques
developed several different types of carbon dioxide (CO\({}_{2}\)) sensors such as optical sensors, polymer opal films, and polymer hydrogels that could intelligently monitor the changes in concentration of gas inside the food package [25]. [PERSON] et al. fabricated a highly flexible and low cost hydrogen (H2) gas sensor using inclined and vertically aligned zinc oxide (ZnO) nanorods. The ZnO nanorods fabricated using an affordable thin and highly flexible nylon substrate with a luminous flux of 15 lm using the hydrothermal method [26]. [PERSON] et al. developed wireless ammonia sensors with a high performance using ammonia (NH\({}_{3}\)) sensitive materials composed of rGO and nano-silver ink (Ag-ink) that were loaded on a high-gain microstrip patch antenna [27]. [PERSON] et al. Figure 3: A typical architecture involving the sensors. developed a woven textile that had temperature and humidity sensors on flexible plastic stripes. Two different methods were used for the fabrication of the sensors: a conventional photolithography patterning technique, and inkjet-printing (presented here as an effective and low cost alternative. A comparative analysis of two different fabrication methods was conducted in reference [28]). Flexible sensors can be fabricated by printing approaches for reducing the fabrication cost and the environmental impact of their production. For instance, [PERSON] et al. proposed printing processes for the fabrication of flexible sensors and discussed the integration of printed sensing devices on smart RFID tags [29]. Indicators cannot provide quantitative information about the quantity of the packaged products and cannot store the measurement and time data. Indicators can only provide immediate visual and qualitative (or semi-quantitative) information about the packaged food by means of a color change, an increase in color intensity, or diffusion of a dye along a straight path [4]. They do not need batteries and they are cheaper than sensors. For future intelligent packaging systems, electronical sensors are currently considered to be the most promising and innovative technology compared with simple indicators [8; 30]. However, such sensors need energy to power the devices and they are usually more expensive.
# A Simplistic Downlink Channel Estimation Method for NB-IoT
## 2 NB-IoT Channel Estimation Procedure
In the third step of the procedure, channel coefficients for the remaining eight non-NRS subcarriers are determined. Two variants were considered for this purpose. The first one assumes linear interpolation in the frequency domain, similar to the interpolation in the time domain. Only components located between NRS subcarriers would be calculated in this way, whereas for other subcarriers, zero-order hold is applied. For example, if the NRS subcarrier indices are 2, 5, 8 and 11, the channel coefficients for subcarriers 0 and 1 will have the same value as the coefficient calculated for the lowest NRS subcarrier, i.e., the one with index 2. The second approach is to apply frequency-domain averaging instead of interpolation. In this case, channel coefficients for all the subcarriers are equal, being the mean value of 4 coefficients estimated earlier for NRS subcarriers. This method is less complex than interpolation; however, its SNR performance needs evaluation. Frequency averaging is suitable for channels without inter-symbol interference (ISI), i.e., when the channel magnitude response is flat. To fulfill this requirement in the case of NB-IoT, the multipath delay spread should be shorter than the reciprocal of 180 kHz frequency bandwidth, i.e., 5.6 \(\upmu\)s. In 3 GPP multipath propagation models for LTE access link, such as extended typical urban (ETU), extended vehicular A (EVA) or EPA, the delay spread does not exceed 5 \(\upmu\)s [28,29]. It is therefore expected that the averaging approach can be applied successfully. Once the channel coefficients are estimated for the whole frame, they are passed on to the channel equalization block which compensates for the amplitude and phase distortions of particular resource elements. If transmit diversity is used for downlink transmission, channel equalization is joint with space-frequency block decoding [30].