esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive Multi-Grained Buffer Management for Database Systems
## 3 Adaptive Multi-Grained Buffer Management
### Theoretical Analysis  
In this section, we theoretically analyze the efficiency of AMG-Buffer. The parameters of the buffer manager are shown in Table 1, which will be used in the following analysis. First, we assume that the buffer size is 20% of the database size. The access pattern has a locality of 80-20, which is set according to the Pareto Principle, meaning that 80% of the requests access 20% of the tuples.  
Since the hot tuples are randomly stored in pages, we can infer that the rate of the hot tuples in one page is about 20%. For the traditional buffer manager, the number of the hot tuples that the buffer can hold is estimated by Equation (1).  
\[\frac{M}{P}\cdot\frac{0.2\cdot P}{T}=\frac{0.2\cdot M}{T} \tag{1}\]  
For AMG-Buffer, we assume that the sizes of the F-Buffer, the P-Buffer, and the S-Buffer are 20%, 40%, and 40% of the memory size, respectively. Additionally, the number of the hot tuples that the F-Buffer can hold is estimated by Equation (2).  
\[\frac{M}{P}\cdot\frac{(0.2+0.4)\cdot 0.2\cdot P}{T}=\frac{0.12\cdot M}{T} \tag{2}\]  
When a page is evicted out of the F-Buffer, the probability of the page's hot tuples having been accessed is about 80%. Thus, the number of the hot tuples that the S-Buffer can hold is estimated by Equation (3).  
\[\frac{M}{P}\cdot\frac{0.4\cdot 0.8\cdot P}{T}=\frac{0.32\cdot M}{T} \tag{3}\]  
As we assume that the access locality is 80-20, we can infer the hot tuples in the database is \(0.2\cdot D\), and 80% of the requests can be answered by these hot tuples. Thus, we can estimate the hit ratio of the traditional buffer manager and AMG-Buffer by Equations (4) and (5), respectively.  
\[\frac{\frac{0.2\cdot M}{T}}{\frac{0.2\cdot D}{T}}\cdot 0.8+\frac{\frac{(1-0 2)\cdot M}{T}}{\frac{0.8\cdot D}{T}}\cdot 0.2=\frac{M}{D}(M<D) \tag{4}\]\[\frac{\frac{0.44\cdot M}{T}}{\frac{0.2\cdot D}{T}}\cdot 0.8+\frac{\frac{(1-0.44) \cdot M}{T}}{\frac{0.8\cdot D}{T}}\cdot 0.2=\frac{1.9\cdot M}{D}(M<\frac{5\cdot D}{11}) \tag{5}\]  
We summarize the analysis result in Table 2, which indicates that AMG-Buffer can maintain more hot tuples than the traditional buffer manager. To this end, when processing the same requests, AMG-Buffer can maintain a higher hit ratio than the traditional buffer manager.
## 2 Related Work
### Efficient Beam Tracking in mmWave/THz Systems  
The problem of fast ray tracing is tightly related to the problem of efficient beam tracking in mmWave/THz systems [23]. Indeed, in the former case, one wants to interpolate or extrapolate the received signal strength based on the limited number of measurements, e.g., launched rays. In beam tracking, the core problem is to determine the current direction to BS/UE (User Equipment) based on the minimal number of measurements.  
Numerous ML-based beam tracking methods have been proposed in the past to provide high tracking accuracy continuously in various scenarios. The main two advantages of ML approaches are the ability to capture the dynamics of the channel and environment. The authors in [24] proposed the beam-training approach to acquire channel state information and select the optimal beam so that a target communication quality can be maintained. In [25], the authors proposed a prediction model based on a long short-term memory (LSTM) network. The model utilizes previously measured channel state information (CSI) samples to estimate the channel. The authors in [26] proposed a deep neural network (DNN)-based approach to determine the hidden relation between the received training signal and the mmWave channel state. The authors of [27] proposed a data-driven beam-tracking approach to find the beamforming/combining vectors. The proposed scheme searches all the beamforming and combining vector configurations to achieve the target quality of service (QoS) by minimizing the tracking error through a series of equivalent local dynamic linearizations. The authors showed that the proposed algorithm can achieve reliable tracking performance with a much shorter alignment time as compared to traditional schemes. In [28], the authors proposed to combine a deep learning-based tracking algorithm (deep sort) and beamforming in 5G NR to predict and track the UE's location. They showed that the predicted UE location can help in focusing data toward their location without any feedback mechanism. Thus, reducing the feedback improves the network efficiency.  
Similarly to the task of speeding up the ray-tracing simulations, most of the studies addressing the problem of efficiency did not carry out statistical studies to assess the potential for inter- and extrapolation of consecutive signals to reduce the scanning space. The goal of our work is to assess the correlation between the signals to accelerate the ray-tracing simulations and beam-tracking process.
In the case of the pilot-scale installation, atmospheric plasma made by a 400-W generator using HMDSO and acetylene was applied and the treatment line speed was 4-6 m/min. The plasma coating's uniformity appeared to be a critical factor influencing mottling in the offset printing. An interesting application of plasma was proposed by [PERSON] et al. [9], where it was used for paper restoration. Many paper documents are collected and stored in libraries. These materials can be contaminated by microbes as well as oxidized or acidified. Aged groundwood paper and a specially designed after-glow plasma chamber were used for the experiments. In the upper part of the chamber, 13.56 MHz RF plasma was generated. The upper chamber was separated from the lower chamber so that the plasma did not interact directly with the sample. After the plasma treatment, no visible degradation of the paper was found. The hydrogen plasma restored the original tensile strength, while the oxygen plasma caused greater inhibition of microbial activity. In our previous paper [10], plasma treatment was investigated in the case of the gluing of paper composite materials. A paper sheet was glued to metallized polyester foil and the effect of plasma treatment was investigated for the packaging industry. In this paper, the influence of plasma treatment on 270 g/m\({}^{2}\) paper was studied. The main goal was to determine whether plasma treatment can promote the gluing of offset printing paper sheets. Gluing these types of materials is a difficult task because the joints are usually weak and do not withstand extended use. The sheets are used as material for the production of cosmetic packages, and the edge that is glued is usually roughened, which leads to the loss of a large amount of material.
## 3 Controller Design  
### Traditional Super-Twisting Reaching Law  
The design of a sliding mode controller typically involves the following steps:  
* Selecting an appropriate sliding surface.
* Designing the control law to ensure that the system trajectory converges to the sliding surface under the influence of the control law.  
A conventional sliding surface is usually chosen as follows:  
\[s=e=\omega_{ref}-\omega_{m}, \tag{6}\]  
where \(s\) represents the traditional sliding surface, \(e\) denotes the speed tracking error, and \(\omega_{ref}\) is the reference speed value. The derivative of \(s\) is taken and substituted it into Equation (4) in the following equation:  
\[\dot{s}=\dot{e}=\dot{\omega}_{ref}-(a\dot{\omega}_{m}+b\dot{u}-\dot{d}). \tag{7}\]  
The traditional super-twisting reaching law is as follows [28]:  
\[\dot{s}=-k_{1}|s|^{\frac{1}{2}}\text{sgn}(s)-k_{2}\int\limits_{0}^{t}\text{ sgn}(s)d\tau \tag{8}\]  
where \(k_{1}\) and \(k_{2}\) are the switching gains of the sliding mode control. Combining Equations (7) and (8), the equation can be expressed as follows:  
\[\dot{\omega}_{ref}-(a\dot{\omega}_{m}+b\dot{u}-d)=-k_{1}|s|^{\frac{1}{2}}\text {sgn}(s)-k_{2}\int\limits_{0}^{t}\text{sgn}(s)d\tau. \tag{9}\]  
From Equation (9), the speed sliding mode control law can be obtained as follows:  
\[i_{q_{CSTRL}}=\frac{1}{b}\int\limits_{0}^{t}\left[k_{1}|s|^{\frac{1}{2}}\text{ sgn}(s)+k_{2}\int\limits_{0}^{t}\text{sgn}(s)d\tau+\dot{\omega}_{ref}-a\dot{ \omega}_{m}+d\right]d\tau. \tag{10}\]
## 2 Selected Wireless Sensor Networks Standards: State-of-the-art
### IEEE 802.15.1-based Standards
#### Network Elements  
WISA networks [11] can be deployed in cellular topology with up to three cells (Fig. 2). Each cell uses a different transmission frequency, and is composed of a _base station_ and up to 120 _end devices_ including sensors and/or actuators organized in a star topology. The end devices communicate wirelessly via standard Bluetooth transceivers, while the base station is equipped with a specific transceiver, which is able to receive up to four channels in parallel [12]. Additionally, the base stations exchange information with the network manager via wired fieldbus such as DeviceNet [13] and Modbus [14].
###### Abstract
EU waste policy contributes to the circular economy by extracting high-quality resources from waste to the best possible extent. This implies a more efficient utilization of resources, in addition to providing more respect and care for the environment compared to the traditional linear economy [37; 38]. EU biowaste--mainly food (about 60%) and garden waste (about 35%)--is a key waste stream with a high potential for contributing to a growing circular economy through delivery of valuable soil-improving material and fertilizers as well as biogas, a source of renewable energy [2]. Moreover, according to the Council Directive [39] on the landfill of waste, EU member states are obliged to reduce the amount of biodegradable municipal waste going to landfills. In 2017, the EU-28 generated 249 million tons of municipal waste, of which about 86 million tons (34%) was biowaste [2]. About 40% of the collected biowaste was effectively recycled into high-quality compost and digestate. In the last decade, the amount of generated municipal waste in Bulgaria follows a trend towards permanent reduction--from 3572 thousand tons in 2011 to 2838 thousand tons in 2019; for the same period, the amount of landfilled and recycled municipal waste also significantly decreased--70.0% and 81.2%, respectively--while municipal waste submitted for pretreatment increased by 78% [40]. Following the EU waste management policy, the main goal of the new Bulgarian National Waste Management Plan 2021-2028 is the transition from waste management to efficient use of waste as a resource for sustainable development [41]. One of the steps to achieve this goal is MGW composting--either alone or with other biodegradable waste, which in 2017 comprised about 42% of MSW [2]. Until 2021, eighteen facilities for aerobic GW composting were built and put into operation at the regional nonhazardous waste landfills; another thirty-five are under construction [41]. The quality and safety parameters of the compost, monitored by business operators according to Bulgarian and EU legislation, do not provide sufficient information about compost fertility as a soil fertilizer. The recently developed fertilizing index (FI) and clean index (CI) give a good opportunity to compare the quality of composts produced from different raw materials with different composting duration. The FI determines the fertilizing potential, based on total organic C, N, P, K and C:N ratio, whereas CI assesses the potential for contaminating the soil and the food chain, based on heavy metals content [42]. The purpose of this study was to make a quality assessment of industrial green waste composts produced in a composting facility of a regional nonhazardous waste landfill on the basis of: (1) physicochemical properties, heavy metals content and sanitary indicator microorganisms; (2) fertilizing potential through FI and heavy metal polluting potential through CI; (3) Bulgarian and EU legislation regarding soil fertilizers.
## 2 Materials and Methods
### Proposed Scheme
#### 2.5.3 Login Phase  
This phase enables the agriculture professional to authenticate to HGWNs using the smart-card SC. After inserting the smart-card into a specific card reader terminal, the SC transmits the login request message to the HGWNs by performing the following steps, which are shown in Figure 8:  
* Firstly, the agriculture user inserts their smart-card and inputs the username \(U_{ID}\), password \(U_{PW}\) and imprints their biometric \(U_{BIO}\) at the sensor. Then, the smart-card calculates using the error tolerance thresholds value \(\tau,\sigma_{i}^{*}=Rep(U_{BIO},\sigma)\), \(R^{*}=T_{i}\oplus h(U_{ID}\parallel\sigma_{i}^{*})\), \(PID_{i}^{*}=h(U_{ID}\parallel R^{*})\), \(PWR_{i}^{*}=h(U_{PW}\parallel R^{*})\), and \(R_{i}^{*}=h(PID_{i}^{*}\parallel PWR_{i}^{*}\parallel\sigma_{i}^{*})\). Then, it checks the condition of \(R_{i}^{*}\
eq R_{i}\), if invalid, terminates the session.
* Otherwise, the SC authenticates the user and generates a random once \(N_{i}\) and calculates a secret key \(SK_{i}=A_{th}^{*}\oplus h(PID_{i}^{*}\parallel PWR_{i}^{*}\parallel TID_{i} )\oplus h(\sigma_{i}^{*}\parallel R_{i}^{*})=h(HGW_{ID}\parallel PID_{i} \parallel HGW_{MSK})\) consistent with the HGWN of the \(U_{i}\). The \(U_{i}\) selects \(SN_{j}\) to have access to WSN services. Furthermore, smart-card computes \(W_{i}=h(PID_{i}^{*}\parallel TID_{i}\parallel N_{i})\) and \(CT_{i}=E_{SK_{i}}[HGW_{ID},SN_{ID},W_{i},N_{i},TS_{1}]\), where \(E_{SK_{i}}(M)\) signifies the plaintext message M's symmetric key encryption (e.g., AES) using the key \(SK_{i}\), and \(TS_{1}\) current timestamp. The SC finally sends \(M_{1}=[SN_{ID},TID_{i},CT_{i}]\) to HGWN publicly.  
Figure 6: Agriculture professional registration phase.  
Figure 7: Newly joined sensors phase.
# Improving Graphite Ore Grade Identification with a Novel FRCNN-PGR Method Based on Deep Learning
## 3 Proposed Novel FRCNN-PGR Model
### Filter Response Normalization (FRN)  
In the normalization method, Batch Normalization (BN) normalized the output of each neural network layer. BN can speed up the training speed of the model network and make the model converge faster and effectively reduce the possibility of gradient explosion. On the other side, the gradient disappearance can also prevent model training from overfitting. However, the BN layer is more suitable for large batches, and due to hardware limitations, the batch size set in this paper is small during training, so the FRN layer is introduced.  
FRN and BN have the same effect, but FRN performs better on large or small batches. [PERSON] et al. [19] comparative experiments were conducted on FRN, GN [29], and BN. He used the InceptionV3 [30] and ResnetV2-50 [31] models to classify the ImageNet dataset. He found that under the same experimental conditions, the model using the FRN layer performed better than the BN layer, with an improvement of approximately 1%. Additionally, by further reducing the Batch Size, the model with the FRN layer outperformed the GN layer on the same problem. Finally, he tested the model on the COCO dataset, and the experimental results showed that the FRN layer exhibited stable performance in both large and small Batch Sizes, with an improvement of 0.3% to 0.5% over other methods. The FRN layer structure is shown in Figure 12.  
As is shown in Figure 12, the FRN layer consists of two parts, Filter Response Normalization (FRN) and Threshold Linear Unit (TLU).  
Figure 11: RGA-C structure diagram.  
(1) The Filter Response Normalized (FRN) formula is represented in Equations (7)-(9).  
\[\upsilon^{2}=\sum_{i}\frac{x_{i}^{2}}{N}(x=X_{b,:,:,:,:}\in R^{N},N=W\times H) \tag{7}\]  
\[\hat{x}=\frac{\mathbf{x}}{\sqrt{\upsilon^{2}+\epsilon}} \tag{8}\]  
\[\mathbf{y}=\gamma\hat{\mathbf{x}}+\beta \tag{9}\]  
In Equation (7), \(x=X_{b,:,:,:}\in R^{N}\), the form of vector \(X\) is [B, H, W, C]; [B, H, W, C] is the input tensor of shape. The expression of \(N\) only contains \(W\) and \(H\), and FRN is not affected by the batch size. The \(\upsilon^{2}\) represents the value of \(\mathbf{x}\) Mean square norm; \(\epsilon\) in Equation (8) is a small normal quantity, which is used to prevent the dividend, that is, the denominator, from being zero; the calculation result expression is shown in Equation (9), where \(\gamma\) and \(\beta\) are both learnable parameters.  
(2) Threshold Linear Unit (TLU): The lack of mean centre in FRN may cause the activation to be any deviation from zero; such deviation combined with ReLU will bring a bad effect.  
Therefore, when using FRN, it is often combined with TLU. The calculation of TLU is shown in the Equation (10).  
\[\mathbf{z}=\max(\mathbf{y},\tau) \tag{10}\]  
where \(\mathbf{y}\) represents the output of the FRN in Equation (9); \(\tau\) represents a learnable threshold. TLU uses the learning threshold \(\tau\) based on ReLU to solve the problem of arbitrary deviation in FRN without the mean centre.
## 4 Flexible Route Planning Method Combining Q-learning and Graph Search
### Combination of \(A^{*}\) and \(Q\)-learning
#### 4.2.2 Q-Learning  
Q-learning is an effective method for problems modeled as Markov decision processes. In reinforcement learning, actions that maximize future rewards are learned. If the action value (Q-value), the value of taking action \(a_{t}\) in a certain state \(s_{t}\), is known, the action with the highest Q-value can be selected. In Q-learning, the Q-value is a function that expresses the validity of action \(a\) taken in a certain state \(s\) based on the policy \(\pi\), i.e., the action value function \(Q^{\pi}(s,a)\) is expressed by the following expression (10).  
\[Q^{\pi}(s_{t},a_{t})=E_{\pi}\Bigg{[}\sum_{k=0}^{\infty}\gamma^{k}r_{t+k}|s_{t} =s,a_{t}=a\Bigg{]} \tag{10}\]  
Here, \(r\) is the reward obtained by the state transition, and \(\gamma\) is the discount rate for the reward that is uncertain in the future. The goal of Q-learning is to maximize this action value function \(Q^{\pi}(s,a)\). The update of \(Q(s,a)\) is performed according to the following (11), which introduces a learning rate \(\alpha\).  
\[Q(s_{t},a_{t})+\alpha\bigg{[}r_{t}+\gamma\max_{a^{\prime}}Q(s_{t+1},a^{\prime })-Q(s_{t},a_{t})\bigg{]} \tag{11}\]  
The parameters of the learning rate \(\alpha\) and the discount rate \(\gamma\) are set to 0.8 and 0.99, respectively.
# Advanced Technologies in Optical Wireless Communications
###### Abstract  
Optical wireless communication (OWC) is expected to be a key component of future wireless communication networks, with a wide range of applications such as indoor visible-light communication (VLC) [1, 2], visible-light positioning (VLP) [3], underwater OWC [4], and satellite communications [5]. These diverse application scenarios highlight the potential of OWC technologies to complement or even surpass traditional radio-frequency (RF)-based wireless systems in terms of bandwidth availability, security, and energy efficiency. However, they also introduce new technical demands related to channel modeling, optical beam steering, device integration, and system optimization across varying environments. While significant progress has been made in developing related technologies over the past decade, many critical challenges remain unsolved or require further research.  
This Special Issue aims to address some of the ongoing challenges in OWC by presenting a collection of 16 peer-reviewed articles, which are listed in the \"List of Contributions\" section. These contributions come from research institutions and universities across nine countries, including Australia, Belgium, China, Japan, Germany, Greece, the Netherlands, Spain, and Ecuador, reflecting the global collaborative effort undertaken to advance research in this field. This Special Issue concludes by summarizing these articles and categorizing them into five topics: VLC, VLP, space optical communication, modulation and multiplexing techniques, and OWC with photonic integrated circuits (PICs).
# Low Power Consumption Signal Detector Based on Adaptive DFSD in MIMO-OFDM Systems
###### Abstract  
For a low complexity signal detector to reduce the power consumption for multiple input and multiple output orthogonal frequency division multiplexing (MIMO-OFDM) systems, the depth-first sphere decoding (DFSD) detection scheme was proposed. However, the DFSD detection scheme still has high complexity in the hardware implementation. The complexity is especially high when the signal-to-noise ratio (SNR) is low. Therefore, this paper proposes an adaptive DFSD detection scheme. The proposed detection scheme arrays nodes, sorting by ascending order of squared Euclidean distance (ED) at the top layer of tree structure. Then, the proposed detection scheme uses the different number of nodes according to thresholds based on channel condition. In the simulation results, the proposed detection scheme has similar error performance and low complexity compared with the conventional DFSD detection scheme. Therefore, the proposed detection scheme reduces the power consumption in the signal detector.  
MIMO; OFDM; thresholds; adaptive DFSD Article
# Overview of Predictive Control Technology for Permanent Magnet Synchronous Motor Systems
## 3 An Overview of the Recent Development for Predictive Control Methods of PMSM
### CCS-Model Predictive Control of PMSM  
#### 3.3.1 Generalized Predictive Control  
Generalized predictive control is the most representative algorithm in model predictive control [155,156]. The cascaded generalized predictive control strategy for motor systems is proposed in reference [157]. The generalized predictive controllers of the speed loop and current loop are set, respectively. In [158], the stator voltage and current are always kept within the limits by designing external correction links, and the equivalence between the unconstrained optimal solution and constrained optimal solution can be guaranteed, so that the multivariable design scheme can be directly adapted to realize the generalized predictive control strategy of the motor drive system.  
The constraints of the system under consideration are complex, so the use of LQR and amplitude-limiting links have been studied a lot. Reference [159] proposes a PMSM-constrained state feedback speed control method based on CCS-MPC, bringing a new posterior constraint introduction method. Based on the voltage equation, this method updates the model at each sampling time, calculates the boundary value of the control signal, and provides the allowable value of the future state variables. In ref. [160], a double-closed-loop MPC approach based on vector control is proposed.