esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Scenarios
### Scenario 1: Homeowner and Technical Expert  
Agent \(a_{ho}\) is a homeowner who wants to renovate their home to improve its energy efficiency and reduce energy bills as well as the seismic response of the building to earthquakes. They contact agent \(a_{gc}\), a General Contractor, to manage all the aspects of the renovation works. Technical expert \(a_{te}\), qualified to provide advice on the requirements for the Superbonus 110, is then appointed by \(a_{gc}\). Then, \(a_{te}\) visits the home of \(a_{ho}\) and surveys to identify the work needed to make it more energy efficient and what anti-seismic improvements can be made. \(a_{te}\) then prepares a project outlining the necessary work and estimating the costs. \(a_{ho}\) accepts the project proposal, then General Contractor \(a_{gc}\) applies for financing and credit transfer from their bank and obtains approval. They then contract experienced Sub-contractor \(a_{sc}\) to carry out the renovation work. After \(a_{sc}\) completes the work, \(a_{te}\) conducts a final inspection and issues a certificate of compliance. After these passages, \(a_{gc}\) can invoice and start the discount process to redeem the tax credit accrued. All the sequence interactions among the agents are shown in Figure 4.  
Four main user classes are involved in this process: homeowner, technical expert, bank, and contractor (see Figure 5). Each class has its attributes and methods that relate to their role in the process:  
* Homeowner: The individual who owns the property that will undergo the energy efficiency and/or seismic renovation. The class has attributes such as name, email, phone, and methods such as applyForFinancing() and signContract().
* Technical Expert: The expert who conducts the energy efficiency survey and provides a project proposal for the renovation work. The technical expert class has attributes such as name, email, and phone, and methods such as conductSurvey(), prepareProposal(), conductFinall Inspection(), and issueCertificate().
* Bank: The financial institution is financing the energy efficiency renovation. This class has a single method called approveFinancing().  
* Contractor: The company or individual who completes the renovation work. The contractor class has attributes such as name, email, phone, and a single method called completeRenovationWork().  
Figure 4: Sequence diagram of Scenario 1. Interactions between the homeowner, technical expert, bank, and contractor in the first scenario are illustrated.  
Figure 5: Class diagram of Scenario 1. Shows the classes involved in the first scenario, including the homeowner, technical expert, bank, and contractor. The classes have attributes such as name, email, and phone, as well as methods such as applyForFinancing(), conductSurvey(), and completeRenovationWork(). The interactions between the classes are shown with arrows and labels.
# Privacy-Preserving Data Analytics in Internet of Medical Things
## 2 Background
### Multidimensional Sensitivity-Based Anonymization  
Multidimensional sensitivity-based anonymization is an improved kind of anonymization that can be used to outperform more conventional anonymization methods [11]. It identifies which attributes are sensitive in the datasets. It includes both quasi-identifiers (which can identify individuals when combined) and direct identifier attributes. It evaluates the sensitivity of each attribute. Some attributes may be more sensitive than others, and this sensitivity can be quantified. Anonymization strategies such as generalization, suppression, or noise addition are implemented to make sure the data cannot be traced back to individuals. The level of anonymization that is used can change depending on how sensitive each attribute is. Interactions between multiple attributes are considered. Even if individual attributes are anonymized, the aggregation of attributes prevents re-identification. This is essential for defending against inference attacks, in which the attacker reidentifies a target using multiple attributes. It provides enhanced privacy by considering the sensitivity of multiple attributes and their interactions. It allows for different levels of anonymization based on the sensitivity of each attribute and minimizes the risk of re-identification through the use of combinations of attributes. This technique is better suited for large scales an static data. Moreover, it is not applicable to streaming data.
## 3 Methodology
### Improvement Strategy in the Identification Module  
Text recognition of multimodal data such as contorted deformation and tilt with direction is inadequately supported by the majority of methods. For example, in the recognition task, the accuracy of the extracted text after reversal or rotation is low when the image contains text with a directional inclination. Even if it is possible to recognize the image region containing the text, it is difficult to extract that text from the image. Therefore, in the context of public opinion monitoring, it is very meaningful to predict the direction of text from multimodal data such as images or videos. In this paper, we introduce a sinusoidal loss function for rotation recognition proposed by [PERSON] [54], which can optimize the model for recognition of rotated fonts and enhance the performance of text detection for scenes with arbitrary rotation angles. The specific functional designs are as follows:  
For target rotated text area detection \(\mathbf{r}\in R^{N\times 5}\), a new direction-aware loss function \(L_{rbx}\) is proposed. The loss function sets five description parameters, two of which describe the coordinates of the center of the rotated region; two are used to describe the height and width of the detected region; and the last one is used to describe the angle of rotation. \(\theta\in R\), \(\theta\) indicates the angle toward the top. The loss function for the first m match detection is expressed as shown in Equation (1):  
\[L_{rbx}=\sum_{i=1}^{4}\alpha_{i}|\hat{r}_{i}-r_{i}|+\alpha_{\theta}\sin^{2}( \hat{\theta}-\theta) \tag{1}\]  
where the hat indicates a prediction. The values of both \(\alpha_{i}\) (\(i\) takes values in the range [1; 4]) and \(\alpha_{\theta}\) are chosen empirically. For \(n\in Z\), the sine square loss function has \(n\pi\)-periodicity, and this loss function takes advantage of the fact that the symmetry between the rectangular frame in the rotated region and m eliminates the preexisting ambiguity during model training and is able to make the same prediction for a vertical or inverted detection frame. At the same time, L1 parametric regularization is used for the first four parameters and a loss based on the sine function is designed for the angle, which satisfies reasonableness and derivability. After introducing this loss function on top of the original  
Figure 3: RSE-FPN framework structure diagram.  
model, it is verified on the test dataset that the performance of detection and recognition of arbitrary rotation text sensitivity has improved to some extent.  
The original CTC decoder has the ability to make rapid inferences, but its accuracy is poor. Thus, it introduces the GTC method. The attention module is used to guide the CTC training to integrate more features, while it is removed without increasing the inference time during the inference phase. Attention is more attuned to spatial data and can optimize STN (Spatial Transformer Network) networks with precision [55].
# AoI Analysis of Satellite-UAV Synergy Real-Time Remote Sensing System
###### Abstract  
With the rapid development of space-air-ground integrated networks (SAGIN), the synergy between the satellite and unmanned aerial vehicles (UAVs) in sensing environmental status information reveals substantial potential. In SAGIN, applications such as disaster response and military operations require fresh status information to respond effectively. The freshness of information, quantified by the age of information (AoI) metric, is crucial for an effective response. Therefore, it is urgent to investigate the AoI in real-time remote sensing systems leveraging satellite-UAV synergy. To this end, we first establish a comprehensive system model, corresponding to the satellite-UAV \"multiscale explanation\" synergy remote sensing system in SAGIN, in which we focus on the typical information transmission and fusion strategies of the system, the analysis framework of AoI, and the temporal evolution of AoI. Subsequently, the time-varying process of the system model is transformed into a corresponding finite-states continuous-time Markov chain, enabling a precise analysis of its stochastic behavior. By employing the stochastic hybrid system (SHS) approach, the moment generating functions (MGFs) and mean AoI, offering quantitative insights into the freshness of status information, are derived. Following this, a comparative analysis of AoI under different queuing disciplines, highlighting their respective performance characteristics, is conducted. Furthermore, considering transmit power and bandwidth constraints of the system, the AoI performances under full frequency reuse (FFR), and frequency division multiple access (FDMA) strategies are analyzed. The energy advantage and spectrum advantage associated with AoI are also examined to explore the superior AoI-related performance of the FFR strategy in SAGIN.  
AoI; SAGIN; satellite-UAV synergy; remote sensing; SHS; Markov chain 28 July 2024 31 August 2024 2 September 2024 5 September 2024 5 September 2024 5 October 2024 16 305 1
## 2 Methodology
### k-Means Clustering  
k-means clustering is a widely used technique in data mining and statistics for partitioning datasets into k clusters based on feature similarities [23; 24; 25]. Although there are various clustering techniques available--such as hierarchical clustering [26], DBSCAN [27], and Gaussian Mixture Models (GMMs) [28]--we focused on computational complexity and, therefore, selected k-means clustering to efficiently cluster large-scale daily power consumption data. k-means clustering has a relatively low computational complexity and employs a simple process of iterative mean calculation and assignment, resulting in fast computation.  
In this study, \(k\) was treated as a variable to allow optimization based on the building's operational characteristics (e.g., residential, commercial, or industrial). The optimal \(k\) value was determined using the elbow method, which evaluates the within-cluster sum of squares (WCSS) to identify the point at which additional clusters provide diminishing improvements in clustering performance [29; 30; 31]. WCSS is computed as follows:  
\[\text{WCSS}=\sum_{i=1}^{k}\sum_{x\in C_{i}}\left\|\,x-\mu_{i}\right\|^{2}, \tag{1}\]  
where \(C_{i}\) represents the set of data points in the \(i-\)th cluster, \(\mu_{i}\) is the centroid of cluster \(C_{i}\), and \(\left\|\,x-\mu_{i}\right\|^{2}\) denotes the squared Euclidean distance between a data point \(x\) and the cluster centroid. The optimal number of clusters was determined by identifying the elbow point in the WCSS curve.  
The clustering process began by randomly selecting \(k\) initial centroids. Each data point was then assigned to the nearest centroid based on the Euclidean distance. The centroids were iteratively updated by computing the mean of all data points within each cluster until convergence was achieved.  
By optimizing the number of clusters using the elbow method, the dataset effectively captured seasonal variations and operational conditions specific to different building types. This adaptive clustering approach ensured that each model was trained on data with similar underlying consumption patterns, significantly improving forecasting accuracy.
## 4 Experimental Section
### Metrics
#### 4.1.4 Tracking Accuracy  
Given the actual location of a Mobile Node, \(i\), at a given time, the accuracy of the location estimation, \(f^{acc}\), is measured by the Euclidean distance between the estimated location of the Mobile Node (\(x^{e}\), \(y^{e}\)) and its actual location (\(x^{a}\), \(y^{a}\)) at any point in time during the course of simulation, _i.e._,  
\[f^{acc}_{i,t}=\sqrt{\left(x^{a}_{i,t}-x^{e}_{i,t}\right)^{2}+\left(y^{a}_{i,t} -y^{e}_{i,t}\right)^{2}} \tag{4}\]
# Energy-Efficient Internet of Drones Path-Planning Study Using Meta-Heuristic Algorithms
## 4 Result Discussion and Analysis
### Results and Discussion
#### 4.2.3 Convergence Speed  
The convergence speed is a significant factor in assessing the effectiveness of an algorithm. As we can see in Figure 5, IPSO outperforms other competitors by demonstrating faster convergence at an early stage of process evolution. This proves IPSO's capability to generate more accurate and quicker safety paths. While GA shows faster convergence speed than ABC, it gets stuck in local optima, and the generated path tends to have higher energy consumption.  
It is worth mentioning that PSO is susceptible to getting stuck in local optima and experiencing premature convergence issues. However, IPSO mitigates these problems through adaptive mutation of solutions, which strikes a balance between local and global search. This adaptive mutation approach accelerates convergence. Furthermore, if a particle becomes inactive and fails to improve the solution, it is replaced by a new particle. This replacement process enhances the diversity of the search and guides the solution towards the global optimum.
## 2 Preliminaries
### Digital Signatures
\begin{tabular}{l} Experiment \(\mathbf{Imp}_{\mathtt{ACT-STAT},\Pi}(1^{\mathtt{T}})\) \\ \((i,j)\leftarrow\mathtt{ACT-STAT}_{1}(1^{\mathtt{T}},\ell)\) \\ \((pub,priv,s_{0})\leftarrow Gen(1^{\mathtt{T}},\ell)\) \\ \(hist_{i-1}\leftarrow\mathtt{Auth}_{i-1}(1^{\mathtt{T}},pub,priv,states_{i-1})\) \\ \((a_{j}^{\mathtt{T}},\ldots,a_{j}^{\mathtt{T}})\leftarrow\mathtt{ACT-STAT}_{2}( 1^{\mathtt{T}},pub,i,j,hist_{i-1})\) \\
**if**\(\forall h\in[j,i]\) \\ \(Ver(1^{\mathtt{T}},h,a_{h}^{\mathtt{T}},pub,hist_{h-1}^{\mathtt{T}})=1\) \\ \(where\hist_{h-1}^{\mathtt{T}}=(a_{1},\ldots,a_{j-1},a_{j^{\mathtt{T}}}^{\prime},\ldots,a_{h-1}^{\mathtt{T}})\) \\
**then return** 1 \\
**return** 0 \\ \end{tabular}   
_The advantage of \(\mathtt{ACT-STAT}\) is defined as_  
\[\mathbf{Adv}_{\mathtt{ACT-STAT},\Pi}^{\mathbf{Imp}}(1^{\mathtt{T}})=Pr[ \mathbf{Imp}_{\mathtt{ACT-STAT},\Pi}(1^{\mathtt{T}})=1].\]  
_The OM-UEA scheme for \(\ell\) session \(\Pi=(Gen,Auth,Ver)\) is secure in the sense of \(\ell\)-\(\mathtt{IMP}\)-\(\mathtt{A}\)-\(\mathtt{ST}\) if, for all probabilistic-polynomial time active static adversaries \(\mathtt{ACT-STAT}\), the advantage of \(\mathtt{ACT-STAT}\) in the impersonation experiment is negligible in the security parameter \(\tau\)._  
[PERSON] et al. [1] proposed three constructions for OM-UEA schemes and analyzed their security with respect to the different security notions proposed in their paper. More precisely, the first construction is based on one-way function families and does not require the parameter \(\ell\) to be fixed in advance. On the other hand, the second and third constructions are based on one-way permutation families and trapdoor one-way permutation families, respectively. In particular, the second construction requires the parameter \(\ell\) to be fixed in advance, whereas the third one supports an arbitrary polynomial number of sessions. While the first construction does not provide security against active static attacks but only against passive attacks, the other two constructions satisfy such a notion of security. However, the drawback of such solutions is that they are not practical for use in applications where the parties have limited computing power and limited bandwidth channel.
## 3 Optimal Dispatching Model of MGs and ESP  
### RESs Model  
Due to abundant resources, renewability and environmental friendliness [30], RESs are becoming increasingly important [31], such as PVs and wind turbines (MTs). In order to facilitate engineering application, the output power \(P_{PV}\) of the PV module can be described as the following model [32].  
\[P_{PV}=P_{STC}G_{AC}[1+k_{I}(T_{c}-T_{r})]/G_{STC} \tag{1}\]  
where \(G_{AC}\) is light intensity. \(P_{STC}\) is the maximum test power under the standard testing environment, in which solar incident intensity is 1000 W/m\({}^{2}\) and ambient temperature is 25 \({}^{\circ}\)C. \(G_{STC}\) denotes the light intensity under standard testing environment. \(k_{I}\) is the power temperature coefficient. \(T_{c}\) is temperature of the photovoltaic cell. \(T_{r}\) is the reference temperature.  
The relationship between the output power of WT and wind speed \(v\) can be represented as [33; 34]:  
Figure 1: An illustration of the studied system structure with coalitions of microgrids (MGs). ESP: energy sharing provider.  
\[P_{WT}=\left\{\begin{array}{ll}0,&v\leq v_{ci}\\ av^{3}+bv^{2}+cv+d,&v_{ci}<v<v_{r}\\ P_{r},&v_{r}\leq v\leq v_{co}\\ 0,&v\geq v_{co}\end{array}\right. \tag{2}\]  
where \(P_{WT}\) is the output power of WT; \(P_{r}\) is the rated power of WT; \(a\), \(b\), \(c\) and \(d\) are fit parameters; \(v_{ci}\) is the cut-in wind speed; \(v_{r}\) is the rated wind speed; \(v_{co}\) is the cut-out wind speed.  
The RESs should be made full use of for the reduction of the operation cost. The output power of RESs including PVs and WTs can be given as: [35].  
\[P_{uc}=P_{PV}+P_{WT} \tag{3}\]  
where \(P_{uc}\) denotes the output power of RESs; \(P_{PV}\) and \(P_{WT}\) are the forecasting output power of PV and WT, respectively.
## 4 Results and Discussions  
### Surface Roughness under Different Cutting Parameters  
Figure 7 illustrates surface roughness with varying spindle speeds and cutting depths under specific feed of 0.05 and 0.075 mm/tooth, respectively, in which the spindle speed ranges from 3000 to 8000 rpm and cutting depth ranges from 0.1 to 2.0 mm. The response surface graphs indicate that surface roughness is affected to different extents by the combined effect of spindle speed and cutting depth, and by changing the feed rate. Overall, the roughness scatters from 0.5 \(\upmu\)m under a lower cutting depth and spindle speed to a higher value of 0.9 \(\upmu\)m under a high spindle speed and larger cutting depth. Further, it is noted that cutting depth has an apparent positive influence on surface roughness: increasing the cutting depth increases the roughness of the machined surface. The influence trend is more significant with a correlation coefficient (r \(\cong\) 0.8-0.9) at feed of 0.075 mm/tooth as compared with feed of 0.05 mm/tooth (r \(\cong\) 0.5-0.8). There is no significant relationship between the surface roughness and spindle speed defined in the range from 3000 to 8000 rpm for machining under lower feed per tooth. However, at a higher feed per tooth, the roughness increases with an increase in spindle speed when the cutting depth is greater than 1.0 mm, and the roughness decreases with an increase in spindle speed when machined with a lower depth (<1.0 mm). At a lower feed rate, the roughness increases at a spindle speed from 4000 to 7000 rpm, and then, the roughness decreases at speeds below 4000 rpm or those higher than 8000 rpm.  
Figure 8 illustrates the variation of surface roughness under different feed rates and cutting depths. The linear feed rate is expressed in units of mm/min and ranges from 600 to 2400 mm/min, which corresponds to a corresponding spindle speed from 3000 to 8000 rpm and feed of 0.05 and 0.075 mm/tooth. As shown in Figure 8, the surface roughness generated in machining is affected by a change in the combination of the feed rate and cutting depth. Within the entire range of testing data, there is no significant correlation between the surface roughness and the feed rate. However, when machining conditions are confined within the testing conditions (Z \(>\) 1.0 mm, F = 1800-2000 mm/min, and S = 6000-8000 rpm), machining vibration apparently increases with an increase in the feed rate,  
spindle speed, and cutting depth, which shows a significant negative influence on surface equality with a correlation coefficient (r > 0.8). In particular, it is observed that surface roughness scatters in lower ranges when machining is performed using various cutting depths at some critical feed rates such as 900, 1200, and 1500 mm/min, corresponding to spindle speeds of 3000, 4000, and 5000 rpm and the feed of 0.075 mm/tooth.
# LSRM: A New Method for Turkish Text Classification
## 4 Application of the Model
### Thread and Validity  
Since 3-Gram and 5-Gram-like methods failed to provide sufficient efficiency in terms of classification performance in previously published Turkish language-based studies [2; 13], the current research focused on the word suffixes. Yet, the developed method concentrated on removing word suffixes, contrary to methods aiming to find the root of a word by taking the first three or five letters of the word, such as the 3-Gram and 5-Gram methods. Analysis of the test results revealed that the LSRM method generated 8 of the total 12 best results from the datasets, although it is not feasible to conclude with certainty that the LSRM method resulted in the highest class performance rates. However, considering the average class performance values, the LRSM method always generated higher class performance rates.  
* When analyzing the study results based on the algorithm and assessing them based on class performance rates, it appears that, although the SMO and RF algorithms generated the best ML algorithm results, they seemed to lag behind the performance of the TL and DL algorithms.
* Rather than using DL algorithms alone, combining DL with WEm algorithms failed to generate the desired class performance increase. However, using WEm + DL algorithms together with the LSRM method almost always increased the class performance rates.
* Among the TL algorithms, the BERTurk algorithm, specially trained for the Turkish language, observably increased the classification performance rate by an average of 12% over the BERT and RoBERTa algorithms.
## 2 FMCW LiDAR  
FMCW LiDAR represents an advanced ranging technology that excels in precisely measuring distances to targets. Esteemed for its broad measurement range and superior accuracy, FMCW LiDAR enables non-contact measurements, distinguishing itself as a versatile tool. A primary advantage of FMCW LiDAR lies in its capacity to ascertain velocity information. In FMCW operation, the LiDAR emits a frequency-modulated continuous laser, which is split into two beams. One beam is directed toward the target, while the  
\begin{table}
\begin{tabular}{l l l} \hline \hline  & **TOF** & **FMCW** \\ \hline Distance measurement & High precision & Extremely high precision \\ \hline Speed Measurement & Indirect measurement with & Direct measurement with low \\  & high latency and low accuracy & latency and high accuracy \\ \hline Interference Resistance & Weaker & Strong \\ \hline Signal-to-Noise Ratio & Lower & High \\ \hline Dynamic Range & Narrow & Wide \\ \hline Multi-echo detection & Weaker & Strong \\ \hline Adaptability to Harsh & & Strong \\ Environments & Weaker & Strong \\ \hline \multirow{3}{*}{Cost} & \multirow{3}{*}{Low} & Short-term costs are high, with \\  & & significant potential for cost \\ \cline{1-1}  & & reduction following solid-state \\ \cline{1-1}  & & integration. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Advantages and disadvantages of TOF and FMCW method.  
other serves as a reference signal or local oscillator, directed to the photodiode (PD) [4]. The sampled signal is compared with a reference signal in the frequency domain, and the distance and velocity of the target object are then calculated using the Doppler effect. Given its distinctive capabilities, FMCW LiDAR has emerged as an important area of interest within the field of LiDAR technology.