esa-sceva/satcom-chunk-collection huggingface dataset:
# A Hybrid Bald Eagle Search Algorithm for Time Difference of Arrival Localization
## 3 Proposed Localization Algorithm
### Hybrid Bald Eagle Search Optimization Algorithm
#### 3.2.3 Chaos Mapping  
The initial population of BES is generated randomly, and for the D-dimensional space, the initial population is:  
\[X_{i}=L_{b}+(U_{b}-L_{b})*rand \tag{21}\]  
where \(L_{b}\) and \(U_{b}\) represent the upper and lower bounds of the population search range, \(X_{i}\) represents the bald eagle \(i\) in the population, and rand is a random number of \((0,1)\). The initial population also has an impact on the algorithm's capabilities in finding the optimal solution. We need the initial population to be uniformly distributed, and the wide search range of the algorithm helps to find the globally optimal solution. Chaotic mappings have been applied to swarm intelligence algorithms such as the grey wolf algorithm and whale algorithm in the literature [33; 34]. Chaos mapping can increase the diversity of the initial population at the beginning of population intelligence and speed up the convergence of the algorithm. The bifurcation diagram for drawing the Tent map is shown in Figure 2. In this paper, the population expression of the tent-mapping initialized bald eagle algorithm was chosen as follows:  
\[x_{i+1}=\left\{\begin{array}{c}2x_{i},0\leq x_{i}\leq 0.5\\ 2(1-x_{i}),0.5\leq x_{i}\leq 1\end{array}\right. \tag{22}\]
## 3 Demand Response Models
### Price-Based Demand Response Model  
The price-based demand response model employs time-of-use pricing. In order to optimize the load curve, a portion of the power load is transferred to off-peak hours during times of high prices [28]. In the field of price elasticity theory, the demand price elasticity coefficient is a crucial parameter employed to quantify the responsiveness of electricity demand to variations in price. The demand price elasticity coefficient can be mathematically modeled using the following equation:  
\[e^{ij}=\frac{\Delta P_{\text{e}}^{i}/P_{\text{e}}^{i,0}}{\Delta\lambda^{j}/ \lambda^{j,0}} \tag{11}\]  
where e\({}^{ij}\) represents the demand price elasticity coefficient; \(P_{\text{e}}^{i,0}\) and \(\lambda^{j,0}\) represent the electricity load before participating in price-based demand response at time \(i\) and the electricity price at time \(j\); \(\Delta P_{\text{e}}^{i}\) and \(\Delta\lambda^{j}\) respectively represent the change in electricity load after price-based demand response at time \(i\), and the change in electricity price at time \(j\).  
In order to divide a schedule cycle into peak, off-peak, and valley phases, we utilize the demand price elasticity coefficient to create a matrix that depicts the correlation between user demand and energy prices. The formulation of the Price Elasticity Matrix is as follows:  
\[E=\begin{bmatrix}e^{pp}&e^{pf}&e^{pv}\\ e^{fp}&e^{ff}&e^{fv}\\ e^{vp}&e^{pf}&e^{vv}\end{bmatrix} \tag{12}\]  
where \(e^{pp}\), \(e^{ff}\) and \(e^{vv}\) represent the self-response demand price elasticity coefficients during peak, flat, and trough hours, respectively; \(e^{pf}\), \(e^{pv}\) and \(e^{fv}\) represent the price elasticity of demand for peak-flat, peak-trough, and flat-trough interactions, respectively. The meanings of other coefficients are similar.  
Utilizing the Price Elasticity Matrix, we establish the demand response model for electric load:  
\[\left[\begin{array}{c}P_{\text{e}}^{p}\\ P_{\text{e}}^{f}\\ P_{\text{e}}^{v}\end{array}\right]=diag(P_{\text{e}}^{p,0}\), \(P_{\text{e}}^{f,0}\), \(P_{\text{e}}^{v,0})E\left[\begin{array}{c}\frac{\Delta\lambda^{p}}{\lambda^ {p,0}}\\ \frac{\Delta\lambda^{p}}{\lambda^{j,0}}\end{array}\right]+\left[\begin{array}[ ]{c}P_{\text{e}}^{p,0}\\ P_{\text{e}}^{f,0}\\ P_{\text{e}}^{v,0}\end{array}\right] \tag{13}\]  
where \(P_{\text{e}}^{p,0}\), \(P_{\text{e}}^{f,0}\), \(P_{\text{e}}^{v,0}\) and \(P_{\text{e}}^{p}\), \(P_{\text{e}}^{f}\), \(P_{\text{e}}^{v}\) represent the power of the electricity load before and after participating in price-based response during peak, flat, and trough hours, respectively; \(\lambda^{p,0}\), \(\lambda^{f,0}\) and \(\lambda^{v,0}\) represent the electricity prices during system peak, flat, and trough hours before response; \(\Delta\lambda^{p}\), \(\Delta\lambda^{f}\) and \(\Delta\lambda^{v}\) represent the changes in electricity prices during peak, flat, and trough hours after response.
# Design of a Single-Stage Transformerless Buck-Boost Inverter for Electric Vehicle Chargers
## 5 Controller Design
### Closed-Loop Control
Finally, the map of the closed-loop poles can be drawn to validate whether the system poles are always inside the unit cycle. As it can be seen from Figure 6a, the system can be stable even when the duty cycle is as high as 0.8, which corresponds to an output voltage of 400 V. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Description** & **Symbol** & **Value** \\ \hline Dc-link voltage & \(V_{dc}\) & 100 V \\ \hline Filter inductor & \(L_{f}\) & 1 mH \\ \hline Filter capacitor & \(C_{f}\) & 50 \(\mu\)F \\ \hline Load resistor & \(R_{o}\) & 10 \(\Omega\) \\ \hline Output voltage & \(V_{C}\) & 50–150 V \\ \hline Current controller & \(G_{i}/C_{d}\) & 1/4 \\ \hline Voltage controller & \(G_{vp}/G_{vr}\) & 0.2/100 \\ \hline Fundamental frequency & \(f_{o}\) & 50 Hz \\ \hline Switching frequency & \(f_{s}\) & 10 kHz \\ \hline Sampling frequency & & \\ \hline Rated power & \(P_{o}\) & 1 kW \\ \hline \hline \end{tabular}
\end{table} 
Table 1: System and Control Parameter Values. Figure 5: Simplified control block diagram of the proposed buck–boost inverter.
## 3 Results  
Total of eleven patients underwent secondary reconstruction of mandible during the study period. There were ten males and one female with a mean age of 59.4 \(\pm\) 5.3 years. Five of them fell into the category I (defect not crossing the midline), three of them were category II (defect extending past the midline with availability of previous imaging data) and three of them were category III (defect extending past the midline but with unavailability of previous imaging data). The patients presented for secondary reconstruction after a mean duration of 2.7 years \(\pm\) 0.8 years. The mandibular defects in the patients as per HCL classification [13] (H: Hemimandibular defects i.e., mandibular defects including the condyle not crossing the midline, C: Central defects covering canine to canine region, L: Lateral defects i.e., mandibular defects sparing the condyle and not crossing the midline) included six lateral defects/L, three lateral defects combined with central defects/LC and two right and left laterals combined with central/LCL. The reasons for secondary reconstruction were osteoradionecrosis in five patients, reconstruction plate exposure in one and second stage reconstruction of post ablative defects in five of them (Table 1).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**S. No.**} & **Age** & **Defect** & **Anatomical Parts** & **Diagramatic** & **Diagnosis** & **Design Category** \\  & **(Years)** & **Gender** & **Type** & **Involved** & **Illustration** & **ORNA** & I \\ \hline \multirow{2}{*}{1} & \multirow{2}{*}{65.3} & \multirow{2}{*}{M} & \multirow{2}{*}{L} & Right body, angle, ramus, coronoid & \multirow{2}{*}{**ORNA**} & \multirow{2}{*}{I} \\ \cline{1-1} \cline{5-5} \cline{7-7} \multirow{2}{*}{2} & \multirow{2}{*}{57.7} & \multirow{2}{*}{M} & \multirow{2}{*}{L} & Right body, angle, ramus, coronoid & \multirow{2}{*}{**ORNA**} & \multirow{2}{*}{I} \\ \cline{1-1} \cline{5-  
The mean linear discrepancy between the planned and post-operative position was 1.43 \(\pm\) 1 mm in category I, 1.72 \(\pm\) 1.29 mm in category II and 4.92 \(\pm\) 3.8 mm in category III. The Kruskal-Wallis Test showed a significant difference among three categories. The Mann-Whitney U test was carried out to further compare mean discrepancy between any two categories and it showed no significant difference between group I and group II (\(p\) > 0.05) whereas comparison of groups I and II separately with group III showed a significant difference (\(p\) < 0.01). Guided by the observation, mean discrepancy values between L/LC defects (1.99 \(\pm\) 1.34 mm), and LCL defect (5.98 \(\pm\) 4 mm) was analyzed, which showed a significant difference (\(p\) < 0.05). Such an observation would imply that the proposed and described algorithm of template printing might be less accurate in huge bilateral mandibular defects. The ICC between two individuals was 0.89 which confirmed almost perfect agreement for inter-rater reliability of analysis.
# BGPcoin: Blockchain-Based Internet Number Resource Authority and BGP Security Solution
## 4 Design of BGPcoin: Roles, Components and Functions  
BGPcoin is a system hosted with the Ethereum blockchain to extend the BGP security architecture. The system is controlled by a set of smart contracts that allows entities to manage its Internet number resource (Internet address and autonomous system number) and give a reliable origin advertisement source for BGP security.  
BGPcoin has five types of participants:  
1. The Internet Assigned Numbers Authority (IANA) as the base authority who builds the BGPcoin_base contract;
2. Regional Internet registries as the allocator of two types of resources (IP addresses and ASNs)
3. National/local Internet registries as the allocate and leaser to assign the resources to autonomous systems
4. Internet Service Providers (ISPs) as the leasees of the resources who have the right to publish the ROA binding their IP address resource and ASN resource
5. All other entities, i.e, the border gateway routers as the client to retrieve the ROA records and any volunteer as the anomalous detector and checker. The contracts of BGPcoin contain several primary functions:
* **Resource trading**: The resource management for BGP security is implemented by operating eight BGPcoin resource trading functions, register, allocate, assign, update and revoke for IP address resource and register, allocate and update for the ASN resource.
* **Aggregated Internet address reposition and updating**: To store efficiently in a way that compresses the number of resource entries on the blockchain, BGPcoin aggregates the continuous IP-prefixes with the same owner, the same tenant (in the case of leasing them) and the same state.
* **Resource searching**: The client of BGPcoin accomplishes every IP validation by operating the resource search on the blockchain. The IP ownership record is displayed by the newest operation log in the latest transaction about the corresponding IP address resource.  
BGPcoin has four types of trading operations for the Internet Address resource and three types of trading operations for AS numbers resource, as shown in Table 1 and Figure 1. We note that an IP address resource assignment refers to an established lease with a valid period. The owners of certain resources are in charge of updating resource states after their expiration date. An IP revocation operated by its owner during its leasing period is only permitted if the resource leasee's consent is attached.
# Telepresence Social Robotics towards Co-Presence: A Review
## 3 From Telepresence to Co-Presence Design
### Co-Presence Design
#### 3.1.1 Sensing  
Robotic sensing technologies are becoming more efficient, lighter, and cheaper. Early human-robot interfaces used to integrate few sensors and relied mainly on video and audio data and low-resolution proximity sensors (e.g., sonars). Current robots can be equipped with 3D or 2D cameras (e.g., low-cost RGB-D cameras), pressure sensors, touch sensors, directional sound sensors (arrays), high-precision proximity sensors (e.g., range laser finder (lidar)), and robot pose and position sensors (e.g., gyroscopes, accelerometers, and GPS). The fusion of these sensors combined with high-accuracy robots, person localization algorithms (e.g., simultaneous localization and mapping (SLAM) or Open Pose), and deep learning approaches, have improved robot operations in an environment, enhancing HRI between operators and bystanders. Valuable information can be extract due to advances in sensor technologies and software, such as sound locations [95,124,125], speech segregation [126,127] and recognition [128,129], attention [79], gesture recognition [130,131], human action analysis [132,133], human intentions [134,135], object recognition [136], and scene understanding [137,138].  
Figure 4: Unmovable robotic telepresence (RP) systems: (**a**) Kubi, (**b**) TableTop TeleMe, (**c**) SelfieBot, (**d**) Meeting Owl Pro, and (**e**) Robovie mR2.  
Figure 3: Mobile robotic telepresence (MRP) systems: (**a**) PRoP, (**b**) Giraff, (**c**) Double 2, 3, (**d**) PadBot 2, (**e**) PadBot 3, (**f**) PadBot T1, (**g**) Beam Pro, (**h**) Ava 500, (**i**) Ohmni SuperCam, (**j**) VGo, (**k**) TeleMe, (**l**) RP-Vita, (**m**) Teleporter, (**n**) FURo-i, (**o**) MeBot, (**p**) Origitbot2, (**q**) Nao, (**r**) Pepper, and (**s**) GrowMeUp.
## 3 General System Design
### Module Introduction  
#### 3.2.1 Device Adjustment and Linkage Module  
Device adjustment is divided into two types: single adjustment and continuous adjustment. Single adjustment is used for fine-tuning, while continuous adjustment is employed for coarse adjustments. By measuring the physical linkage brought about by the movement of each component of the spectrometer, and utilizing Unreal Engine 5 to store all measured engineering parameters in variables, the adjustment functionality is implemented through blueprint scripting. The ultimate goal is to achieve dynamic effects close to the real world.  
Device Linkage:  
Device linkage primarily consists of five types: single device rotational motion, single device translational motion, single device combined rotational and translational motion, multi-device rotational linkage, and multi-device rotational and translational linkage.  
Since device motions are classified into rotation and translation, the system only needs to implement the functionality of these two motion types in the top-level parent class of the instrument. However, constant attention to the moving objects is necessary, as, in the case of multi-device linkage, it involves other object devices. Hence, their parent-child relationships are particularly important (the child in the physical space will adhere to the parent's movement).  
Below is the algorithmic content of this module. First, let us declare the meanings of the variables:  
1. Boolean variable 'Other_Has_Using' indicates whether other keys are invoking the macro. It is set to false only when the conditions \"(pressing Q key and releasing E key) or (pressing E key and releasing Q key)\" are satisfied.
2. Boolean variable 'Is_Runing' represents whether the macro is currently running. It is set to true only when the macro is running.
3. Floating-point variable 'Delay_Time' represents the delay time.  
As shown in Figure 4, when invoking the macro, it is necessary to first check whether other keys are invoking the macro. The execution continues only when \"Other_Has_Using == false\". When the key status is released, we set \"Is_Runing == false\" to indicate that the macro is not running. After exiting the macro, subsequent logic is not executed. When the key status is pressed, we set \"Is_Runing == true\" to indicate that the macro is running. At this point, the first delay time 'Delay_Time' is set to 1 s. Then, the initial judgment of 'Is_Runing' is executed. When \"Is_Runing == true\", we exit the macro concurrently, execute subsequent logic, and delay for 'Delay_Time' time. The first delay is 1 s, and, during this period, releasing the key results in a single adjustment. If the pressing time exceeds 1 s, it becomes a continuous adjustment. Under continuous adjustment, we set a new delay time 'Delay_Time' to 0.05 s. This allows the continuous checking of the 'Is_Runing' state and the execution of subsequent logic.
# Backscatter Communications: Inception of the Battery-Free Era--A Comprehensive Survey
## 5 BackCom: Wireless Communications
### Multiple Access Techniques for BackCom
#### 5.3.3 Orthogonal Frequency Division Multiple Access (OFDMA)  
The OFDMA system was found to have synchronous power and information transfer [104]. Moreover, these researchers focused on energy efficiency for the power dividing capabilities of receivers and considered two different scenarios.  
* The first arrangement considered the receivers, dividing the received power into a continuous set of power streams while having arbitrary power splitting ratios.
* Another scheme examined receivers that can divide the received power into a discrete set of power streams while having fixed power splitting ratios.  
Figure 27a depicts the block diagram of the transceiver model for wireless power and information transfer. Figure 27b shows the OFDMA downlink communication system with \(K\) mobile receivers. In the figure, \(I_{i,k}\) represents the received aggregate co-channel interference on sub-carrier \(i\) of receiver \(k\)  
Figure 26: CDMA compared with TDMA in BackCom.  
with zero mean and variance \(\sigma^{2}_{l_{i},k}\). \(I_{i,k}\) is emitted by unintended transmitters using the same frequency channel. The power splitting ratios \(\rho^{I}_{i,k}\) and \(\rho^{E}_{i,k}\) are used to decode the information in the signal and to harvest the energy, respectively. \(\sigma^{2}_{z_{k}}\) is the signal processing noise power at the receiver end [104].
## 3 Medium Access Control Layer
### Time-Based Schemes
#### 3.3.2 Random-Based Schemes
Instead of the sender, the node that initiates the handshaking process is the receiver, which informs its neighbors that it is available to receive. After that, the neighboring nodes inform the receiver about the size of their transmissions, and with that information and the previously known propagation delay, the receiver can calculate and broadcast a transmission order. Finally, senders follow this transmission order, and the data arrives at the receiver in a sequence of packets. Tone-Lohi (T-Lohi) [88] implements this technique. It automatically adapts the contention time to the number of contending nodes. The nodes send a short packet, called tone prior, to the actual data packet to count the number of terminals contending for the channel. If a node does not receive any other tones, it starts the transmission. If it receives more tones, it adapts its back-off time, depending on the number of tones received.
# Fine Recognition of MEO SAR Ship Targets Based on a Multi-Level Focusing-Classification Strategy
## 3 Coarse-Level Focusing and Classification of MEO SAR Ship Targets
### Ship Target Coarse Classification Based on Global Features
The mean pixel value and mean aspect ratio for the \(j\)-th category are as follows:  
\[\overline{PMV_{j}}=\frac{1}{Z}\sum_{i=1}^{Z}PMV_{j,i} \tag{21}\]  
\[\overline{AR_{j}}=\frac{1}{N}\sum_{i=1}^{N}AR_{j,i} \tag{22}\]  
Based on the known prior statistical information of the targets, the mean pixel values and mean aspect ratios of different target categories can be obtained. Let \(PMV_{test}\) and \(AR_{test}\) be the pixel mean value and aspect ratio of the test samples, respectively. To improve precision, the confidence of the prediction results can be adjusted, and the correction coefficient for the sample is given by the following equation:  
\[Cor_{mean}=[cor_{mean}^{1},cor_{mean}^{2},cor_{mean}^{3}] \tag{23}\]  
The mean value \(cor_{mean}^{j}\) for the \(j\)-th category of samples is given by  
\[cor_{mean}^{j}=\frac{1}{(\overline{PMV_{j}}-PMV_{test})(\overline{AR_{j}}-AR_{ test})} \tag{24}\] \begin{table}
\begin{tabular}{c c c} \hline \hline  & **ResNet101** & **Output Size** \\ \hline Conv1 & \(7\times 7\), 64, stride 2 & \(112\times 112\) \\  & \(3\times 3\), max pool, stride 2 & \\ Conv2\_x & \(\begin{bmatrix}1\times 164\\ 3\times 364\\ 1\times 1256\\ 1\times 1128\\ 3\times 3128\\ 1\times 1512\\ \end{bmatrix}\times 3\) & \(56\times 56\) \\ Conv3\_x & \(\begin{bmatrix}1\times 1128\\ 3\times 3128\\ 1\times 1512\\ \end{bmatrix}\times 4\) & \(28\times 28\) \\ Conv4\_x & \(\begin{bmatrix}1\times 1256\\ 3\times 3356\\ 1\times 11024\\ \end{bmatrix}\times 23\) & \(14\times 14\) \\ Conv5\_x & \(\begin{bmatrix}1\times 1512\\ 3\times 3512\\ 1\times 12048\\ \end{bmatrix}\times 3\) & \(7\times 7\) \\ \multicolumn{3}{c}{Average pool, 1000-d fc, softmax} & \(1\times 1\) \\ FLOPS & \(7.6\times 10^{9}\) & \(1\times 1\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Coarse classification network structure. The output probabilities of the neural network classifier are adjusted using the sample correction coefficient, thereby incorporating the domain-specific characteristics of the training samples into the learning process. The correction formula is given by  
\[Pt=P+Cor_{mean} \tag{25}\]  
in which \(P^{\prime}\) represents the corrected output probability of the classifier, while \(P\) denotes the original output probability before correction. The coarse classification network diagram is shown in Figure 8.
## 3 Study on Surface Integrity of Milled Workpiece Surface
### Microscopic Topography of Milled Workpiece Surface
#### 3.3.1 Effect of Milling Parameters on the Surface Hardness of the Milled Workpiece  
In this section, the variation of the microhardness of titanium alloy TC21 with milling parameters will be studied. The microhardness values of the machined surface are the result of the comprehensive effect of material hardening and softening of the workpiece surface. Titanium alloy TC21 consists of isometric, spherical \(\alpha\)-phase, and \(\beta\)-phase. The volume fraction of the \(\beta\)-phase, which is the hardening phase becomes larger during milling, which leads to the hardening of the workpiece surface [35]. The titanium element is chemically active at high temperatures, and the machining surface tends to form a hard and dense film of titanium oxide and titanium nitride, which also increases the degree of surface hardening. In addition, the poor thermal conductivity of titanium alloys, and the concentration of milling heat on the workpiece surface will cause thermal softening of the machined surface layer. The experimental results show that the substrate hardness of TC21 is in the range of 320-340 HV. During the milling of TC21, The hardening effect caused by the deformation and chemical reaction of the material tissue is stronger than the softening effect caused by the milling heat. This is manifested in the results as a surface hardening phenomenon, in which the surface microhardness is higher than that of the substrate.  
According to the experimental results, the variation of the surface microhardness with the machining parameters under dry milling condition can be obtained as shown in Figure 20. The microhardness of titanium alloy TC21 becomes larger with the increase of milling speed, radial cutting depth, and feed per tooth, while the effect of axial cutting depth on the microhardness of the machined surface is small. The milling force increases with the increase of axial cutting depth, but the increase of axial cutting depth also leads to the increase of milling area in the same proportion. The cutting force and heat per unit area vary little. Thus, the axial cutting depth has little effect on the hardness of the milled surface. With the increase in milling speed, radial cutting depth, and feed per tooth, the milling temperature will increase accordingly, which will lead to an increase in softening effect. However, the milling heat also causes an increase in the oxidation of titanium elements. Meanwhile, the elongation and fibrillation of the internal grains of the material will become more prevalent. This leads to a greater hardening effect than softening effect on the workpiece surface. As a result, the surface hardness becomes greater with increasing radial cutting depth, milling speed, and feed per tooth.