esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Empirical Test and Result Analysis
### Endogenous Treatment
#### 5.3.3. Instrumental Variable Method: Two-Stage Regression  
In order to alleviate the endogenous problems caused by measurement errors, this paper uses the mean value of digital transformation in the same industry and region as an instrumental variable for two-stage regression. The results are in Table 6. The Hausman test statistic was 137.90 (\(p\) = 0.000). The null hypothesis was rejected at the 1% level, and the endogenous explanatory variable _Ludigital_ was considered. The weak instrumental variable test statistic F value was 906.956 (\(p\) = 0.0000), rejecting the weak instrumental variable hypothesis. Finally, because there is only one instrumental variable, there is no over-identification problem. In the second stage, the coefficient of _Ludigital_ is 0.025, which is significant at the 1% level, indicating that this conclusion is still valid after alleviating endogenous problems.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **(1)** & **(2)** \\ \hline  & **First** & **Second** \\ \hline \multirow{2}{*}{_Ludigital_} & & 0.0250 *** \\ \cline{2-3}  & & (0.0080) \\ \hline \multirow{2}{*}{_Ludigital\_ind_} & 0.8010 *** & \\ \cline{2-3}  & (0.0270) & \\ \hline \multirow{2}{*}{_Lusize_} & 0.0160 & 0.0820 *** \\ \cline{2-3}  & (0.0120) & (0.0030) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Regression results of the instrumental variable method (* \(p\) < 0.1, ** \(p\) < 0.05, *** \(p\) < 0.01).  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **(1)** & **(2)** \\ \cline{2-3}  & & **ESG** & **ESG** \\ \hline \multirow{2}{*}{_Mb_} & 0.0087 & 0.0113 \\ \cline{2-3}  & (0.0139) & (0.0140) \\ \hline \multirow{2}{*}{_Fsh1_} & 0.0296 & 0.0350 \\ \cline{2-3}  & (0.0412) & (0.0414) \\ \hline \multirow{2}{*}{_Dlzb_} & 0.0576 & 0.0652 \\ \cline{2-3}  & (0.0937) & (0.0944) \\ \hline \multirow{2}{*}{_Big4_} & 0.1470 *** & 0.1470 *** \\ \cline{2-3}  & (0.0216) & (0.0216) \\ \hline \multirow{2}{*}{_Lange_} & 0.0896 *** & 0.0866 *** \\ \cline{2-3}  & (0.0199) & (0.0206) \\ \hline \multirow{2}{*}{_.cons} & 0.9570 *** & 0.9720 *** \\ \cline{2-3}  & (0.1630) & (0.1660) \\ \hline Year & control & control \\ \hline Industry & control & control \\ \hline \(N\) & 9686 & 9464 \\ \hline \(R^{2}\) & 0.302 & 0.299 \\ \hline adj. \(R^{2}\) & 0.297 & 0.294 \\ \hline \hline \end{tabular}
\end{table}
Table 5: _Cont._
# Research on Improved Bridge Surface Disease Detection Algorithm Based on YOLOv7-Tiny-DBB
## 4 Experimental Analysis and Validation
### Performance Evaluation Metrics  
The performance metrics of the experimental model include precision (P), recall (R), the harmonic mean of the precision and recall known as the F1 score, and mean Average Precision (mAP) [37; 38]. Additionally, the speed of the model in detecting bridge defects is assessed by measuring the inference time per image and the Frames Per Second (FPS) to determine whether it could meet the standards required for industrial applications.  
The formulas for calculating precision (P) and recall (R) are presented in Equations (7) and (8), respectively.  
\[\text{Precesion}= \frac{\text{TP}}{\text{TP}+\text{FP}} \tag{7}\] \[\text{Recall}= \frac{\text{TP}}{\text{TP}+\text{FN}} \tag{8}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & **Initialize** & \multirow{2}{*}{**Momentum**} & \multirow{2}{*}{**Weight Decay**} & **Refinement of** & \multirow{2}{*}{**Batch Size**} & \multirow{2}{*}{**Image Size**} \\  & **Learning Rate** & & & & \\ \hline Value & 0.01 & 0.937 & 0.0005 & 300 & 16 & 640*640 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Configuration of Experimental Hyperparameters.  
In the formula: TP denotes the number of correctly detected diseases; FP represents the number of falsely detected diseases; FN indicates the number of undetected diseases.  
The F1 score is the harmonic mean of precision and recall, calculated according to Equation (9).  
\[\text{F1}=\frac{2\text{PR}}{\text{P}+\text{R}} \tag{9}\]  
The mean Average Precision (mAP), as illustrated in Equation (10), represents the average of the Average Precision (AP) values across all categories. It reflects the trends in precision and recall rates.  
\[\text{mAP}=\frac{\sum_{i=0}^{1}\text{AP}(i)}{n}\frac{1}{n}\sum_{i=0}^{1}\int_ {0}^{1}P(R)dR \tag{10}\]  
In the equation, n denotes the number of target classes.  
The mean Average Precision (mAP) includes both [EMAIL_ADDRESS] and [EMAIL_ADDRESS]:0.95. Specifically, [EMAIL_ADDRESS] indicates the average mAP when the threshold exceeds 0.5, reflecting the trends in precision and recall rates. On the other hand, [EMAIL_ADDRESS]:0.95 represents the average mAP across various Intersection over Union (IoU) thresholds ranging from 0.5 to 0.95 with a step size of 0.05; a higher value of [EMAIL_ADDRESS]:0.95 signifies stronger boundary regression capabilities of the model and a greater degree of alignment between predicted bounding boxes and ground truth annotations.  
\"FPS\" refers to the number of images that a network model can detect per second, which indicates the detection speed of the network.  
\[\text{FPS}=\frac{\text{NumFigure}}{\text{TotalTime}} \tag{11}\]  
In the equation, NumFigure denotes the total number of images detected; TotalTime represents the overall inference time.
## 3 Materials and Methods
### User Clustering  
Social bots not only post tweets but also need to engage with other users to mimic regular users and build trust and influence. When they decide to follow, mention, or retweet other users, they need to choose a target user. A user's number of followers is seen as an indication of their reputation and influence. Users vary in terms of how difficult it is to gain their trust and the impact of interacting with them. In previous studies, the selection of users was carried out by a specialized agent with a discrete action space of \(N\), which is typically around 1000 for the environment used in the literature. Each user corresponds to an action. However, as \(N\) increases, this approach results in a high-dimensional discrete action space, meaning the agent needs to explore and learn the best actions from a larger set. This leads to higher training costs.  
To reduce the dimensionality of the action space for target user selection, user clustering is proposed to be performed before action space construction. Specifically, the user selection process is heuristically decomposed into two sequential steps: interest-based selection followed by reputation-based selection. Initially, tweet embeddings are utilized to capture user interests. Due to the high dimensionality of tweet embeddings, principal component analysis is applied to reduce data sparsity and computational overhead. The number of principal components is set to 15, where the explained variance ratio is just over 0.95. Subsequently, K-Means clustering is employed to group users with similar interests, where each cluster is treated as a distinct action. The number of clusters is fixed at 1000 to maintain consistency with established methodologies in prior research [10]. Subsequently, the bot detection model is utilized to predict bot scores for all users, with the user exhibiting the lowest bot score being identified as possessing the highest reputation. This user is then selected as the target user for the chosen cluster. However, because very few of them have followed any bots, it would be extremely challenging for the controlled bot to gain influence from them based on our assumption. To mitigate this limitation, an alternative selection criterion is implemented, wherein for 50% of clusters, the target user is chosen from among those who have followed at least one bot account while still maintaining the lowest bot score requirement.
## 3 Numerical Examples
### Influence of Node Deviations on the Reliability Performance of Optimized Structures
* Optimized structure (without nodal deviations): Expected displacement: \(-9.90\times 10^{-3}\) m; Variance: \(1.09\times 10^{-7}\) m\({}^{2}\). Initial structure (with nodal deviations): Expected displacement: \(-9.89\times 10^{-3}\) m; Variance: \(1.22\times 10^{-7}\) m\({}^{2}\); Relative differences: \(0.11\%\) in expectation and \(11.76\%\) in variance. The above data show that compared with the initial structure, the difference in the expected value of the response of the optimized structure in the two cases is smaller (from \(4.05\%\) to \(0.11\%\)), and the difference in variance is also reduced (from \(78.69\%\) to \(11.76\%\)). This shows that the nodal deviations have less impact on the optimized structure.
## 2 Methodology and Modeling
### Passive Solution Tests
#### 2.6.5 Window-to-Wall Ratio  
As [PERSON] (2019) emphasizes, the window-to-wall ratio (WTWR) is a key design parameter. Tests were conducted to evaluate the impact of reducing the glazed area using two glass types: the base single glazing and tinted glass.  
In Portugal, all window heights were uniformly reduced, while in Turkey and Brazil, only the larger windows were resized. The window heights for each alternative and country are summarized in Table 9.
# Renewable Energy-Based Energy-Efficient Off-Grid Base Stations for Heterogeneous Network
## 2 System Architecture and Mathematical Modeling
### Problem Formulation and Optimization  
The proposed hybrid energy generation model is considered as an optimization problem, where the target is to minimize NPC by ensuring maximum utilization of RESs. The number of solar PV, biomass generator, battery bank, and converter is optimized according to the operational constraints and load demands. Specifically, each watt of generated energy should be utilized. For an effective performance of the optimization process to formulate the optimal hybrid power system, a number of parameters for system components, such as operational lifecycle, component efficiency, and associated cost, are taken into account. At each hour, using the HOMER optimization software, we determine how to satisfy the electricity demand using HetNet infrastructure, including losses, as well as provide backup power. It also calculates the BS load demand to the input power supply in each iteration, and the optimization phase follows all simulations. Finally, we identify the least expensive component combination that meets the BS load while ensuring no power shortage. The following optimization process was maintained during the simulation process. For macro and micro BSs:  
minimize \[NPC\] (18a) subject to \[E_{PV}+E_{BG}>E_{BS},\] (18b) \[E_{PV}+E_{BG}+E_{batt}=E_{BS}+E_{l},\] (18c) \[E_{Share}=E_{Gen}+E_{batt}-E_{BS}-E_{l}-E_{Excess},\] (18d) \[E_{battmin}\leq E_{batt}\leq E_{battmax},\] (18e)  
For pico and femto BSs:  
minimize \[NPC\] (19a) subject to \[E_{PV}>E_{BS},\] (19b) \[E_{PV}+E_{batt}=E_{BS}+E_{l},\] (19c) \[E_{battmin}\leq E_{batt}\leq E_{battmax},\] (19d)  
where \(E_{l}\) is the sum of converter losses, battery bank losses, and surplus energy transfer losses in kWh/yr. For the macro and microcell configuration, the hybrid solar PV and BG can support the BS energy demand, as mentioned in (18b). In the case of pico and femtocell configuration, the surplus energy transfer losses is zero and the standalone solar PV system can fulfill the BS entire energy requirement, as mentioned in (19b). The constraint in (18c) and (19c) ensures that the annual energy obtained by the renewable energy sources carries the annual BS energy consumption with the associated losses. The amount of surplus electricity is preserved for future use during the abnormal condition or shared among the neighboring BS due to scarcity of energy, and is described by the constraint (18d). Constraint (18e) and (19d) indicates that the battery bank storage capacity should not exceed the maximum limit and does not reach below the threshold level.
# World-Models for Bitrate Streaming
## 2 Background  
### Bitrate Streaming  
Streaming video over variable-bandwidth networks requires a client to adapt to network conditions in order to create the optimal user experience [7]. Users like to have high video quality, minimal buffer pauses, and no noticeable differences in quality between video chunks; these preferences vary between users and situations. HTTP-based adaptive streaming, through standards, such as DASH [8], is the predominant form of network streaming today. In DASH, videos are stored on servers in multiple chunks, each holding a few seconds of video. A client-side application makes requests to download chunks, allowing for servers to have stateless backends. Each chunk is encoded at several discrete bitrates, with higher bitrates containing higher resolution and chunk sizes. Quality-of-Experience (QoE) is the primary metric for evaluating the performance of the client. A variety of QoE metrics exist, and many metrics are a function of three factors: maximal resolution, buffering time, and smoothness (rapid changing of bitrates across consecutive chunks) [1]. Selecting the optimal bitrate is challenging, due to the variability of network conditions [9] and the conflicting QoE requirements. Moreover, bitrate decisions can have a cascading effect on QoE over the course of the video; for example, a high bitrate may drain the playback buffer, which causes the video to rebuffer in the future [1]. The QoE metric is defined below for a video of \(N\) chunks:  
\[QoE=\sum_{n=1}^{N}q(R_{n})-\mu\sum_{n=1}^{N}T_{n}-\sum_{n=1}^{N-1}|q(R_{n+1})- q(R_{n})|\]  
Here, \(R_{n}\) is the chosen bitrate, \(q(R_{n})\) is a quality function on the chosen bitrate, \(\mu\) is a parameter constant, \(T_{n}\) is the time to download the chunk, and the final term is the penalty for consecutive changes in resolution. There are various heuristic-based algorithms for bitrate control. RobustMPC [10], for example, decides the next bitrate by maximizing QoE across the predicted future bitrates in a near horizon, computed through a harmonic mean; however, it can conservatively estimate throughput and requires tuning. Heuristics, such as MPC, may perform extremely poorly when their network assumptions are violated [1]. Pensieve [1] was the first formulation of bitrate streaming as a reinforcement learning problem and it achieved state-of-the-art results on the problem, outperforming human-engineered heuristics, including RobustMPC [10], BOLA [11], and buffer-based [12] algorithms.
# Analysis of Video Transmission Capabilities in a Simulated OFDM-Based Supplementary BPL-PLC System
## 3 Materials and Methods
### Methodology  
The resistance has been calculated taking into account the skin effect in the conductors, the screen, as well as the armor. The skin depth (\(\delta\)) is defined as a function of frequency (\(f\)) and can be calculated as:  
\[\delta=\sqrt{\frac{1}{\pi f\mu_{c}\sigma_{c}}}, \tag{1}\]  
where \(\sigma_{c}\) and \(\mu_{c}\) are the conductivity and permeability of the conductor, respectively.  
When analyzing the structure of the 3-phase cable, the resistance of one solid phase can be calculated as:  
\[R_{solid}=\frac{1}{\pi a\delta\sigma_{c}}(\Omega/m), \tag{2}\]  
where \(a\) is the radius of the conductor.  
However, taking into account the reduction in the resultant cross-section of the phase due to the wiring stranding, a correction factor \(X_{R}\) must be included:  
\[X_{R}=\frac{cos^{-1}\Big{(}\frac{r_{wire}-\delta}{r_{wire}}\Big{)}\times r_{ wire}^{2}-(r_{wire}-\delta)\sqrt[2]{r_{wire}^{2}-(r_{wire}-\delta)^{2}}}{2 \times r_{wire}\times\delta}, \tag{3}\]  
where \(r_{wire}\) is the radius of a single wire in the stranded conductor, and \(\delta\) is the skin depth. With this correction, the resistance for the single phase can be expressed as:  
\[R=X_{R}\cdot R_{solid}(\Omega/\text{m}). \tag{4}\]  
Therefore, the final resistance for the 3-phase cable is calculated according to:  
\[\delta R_{Cable}=\frac{R}{3}. \tag{5}\]  
When considering the effects of the screen \(R_{Screen}\) and the armor \(R_{Armor}\), their resistance values were calculated from Equation (2). In case of the screen, \(a\) represents its radius. However, for the armor, we must take into account both its different radius as well as the magnetic permeability of iron. The resultant resistance \(R_{t}\) of the 3-phase power cable constitutes the parallel connection of all components and therefore should be calculated as:  
\[\frac{1}{R_{T}}=\frac{1}{R_{Cable}}+\frac{1}{R_{Screen}}+\frac{1}{R_{Armor}}. \tag{6}\]
## 4 Discussion  
### The Effect of Single and Dual-Adaptive Reconfiguration Methods  
In this section, the effect of single and dual-adaptive reconfiguration methods on the efficiency of the PV array is examined. Figure 9 shows the array structure in which a PV array can be converted into single and dual-adaptive. A single-adaptive structure is obtained by separating the first column of the sequence, and a dual-adaptive structure is obtained by separating the first and last columns. In the simulation, the irradiance values of the 4 th Scenario given in the previous section and the P-V curves of the array for these three structured cases are given. Here, the GA optimization developed in this study was applied to single and dual-adaptive PV arrays. As can be seen from the P-V curve, a better efficiency is clearly obtained in the dual-adaptive reconfiguration method compared to the single-adaptive one. In addition, the complexity caused by the increase in the connection probability of the dual-adaptive structure was resolved by GA, enabling the method to have a cycle time that can be applied in real time.
# Feedback Approach for the Relay Channel with Noisy Feedback and Its Security Analysis
## Appendix B Proof of Theorem 2
### Dither Signal for the Feedback Channel Power Constraint  
In this proposed scheme, we further extend the dither signal in the real number (see in Section 3.2.2) to the complex dither signal \(V_{n}\) (\(1\leq n\leq N-1\)) to ensure the codewords encoded by Destination satisfy the power constraints at time \(n\). It is also perfectly known by Source and Destination, and it is i.i.d. generated and uniformly distributed over \(\Lambda\), where \(\Lambda=\Lambda_{R}+j\Lambda_{I}\) with \(\Lambda\in[\pm d/2,\pm jd/2)\). Due to the average power constraint of \(\mathbf{X}_{n}\) (\(1\leq n\leq N-1\)) on the feedback channel encoder, the value of \(d\) is \(\sqrt{6P_{f}}\). In addition, \(\gamma_{n}\) (\(1\leq n\leq N-1\)) is the real-valued number and is chosen to avoid modulo-aliasing errors occurring in Source.
## 4 Discussion
The synergistic effect observed in the present investigation led to obtaining a more enhanced anti-_Listeria_ activity of EOs at lower MIC values (that permits the use of EOs as preservative without affecting the sensory quality of foods) when combined with each other and/or with bacLP17, whereas the presence of EOs in the mixture ensures the bacteriocin a broader spectrum of activity (which also includes resistant strains). [PERSON] et al. also reported that the combination of bacteriocin (enterocin A) with EO (thyme essential oil) exhibited synergistic activities against _L. monocytogenes_ and _E. coli_ O157:H7 even at low concentration [55]. Several studies have shown that combined use of EOs with bacterios is advantageous in inhibiting the growth of _L. monocytogenes_ and other food pathogens in meat and minced meat [56; 57; 58]. Further studies will be necessary to improve the prospects of active edible coatings for future application in the food industry. More knowledge of the spatial distribution of both natural compounds and their interactions with other microbial species sharing the same habitat with _L. monocytogenes_ in different types of foods is also necessary. Both these natural antimicrobials, in fact, present limits such as the reduced sensitivity of Gram-negative bacteria to LAB bacterios and the strong smell of EOs. The synergism obtained with the combined use of EOs and bacteriocin may be a promising natural way to overcome both the narrow range of activity and the unpleasant sensory impact. Lastly, it is important to underline that the use of edible coatings obtained from food by-products is a great advantage for the environment because such coatings are biocompatible and eco-friendly material. Conceptualization, P.M.; methodology, R.I., C.Z. and I.M.C. ; validation, R.I. and S.C.; investigation, R.I., S.C. and M.C. ; resources, P.M. and C.Z. ; data curation, R.I. and I.M.C. ; writing--original draft preparation, R.I., S.C. and M.C. ; writing--review and editing, P.M., R.I. and C.Z. ; visualization, I.M.C. and M.C. ; supervision, P.M. and C.Z. ; project administration, P.M., R.I. and C.Z. ; funding acquisition, P.M. All authors have read and agreed to the published version of the manuscript. The study was supported by Departmental FAR 2019 grant to [PERSON] from University of Modena and Reggio Emilia: Prot. n. 37510 of 27/02/2017. Not applicable. The authors declare no conflict of interest.
## 4 Proposed Hybrid GASAVNS Algorithm
### Local Search  
In this paper, the heuristic method of SA is used as a local search algorithm. The SA can be successfully applied to the optimization scheduling problem [38]. SA will be applied to the output obtained from the cross-over in each iteration. SA is run through several iterations until the stopping requirement is satisfied.  
The procedure is described as follows:  
1. Set the initial parameters: In this proposed method, the initial values set for the parameters of SA are an initial temperature of 50.0, a cooling rate of 0.90, and a final temperature of 10.
2. Neighborhood structures: For neighborhood structures, we use the VNS. The idea behind VNS is to use two or more neighborhood structures and systematically mutate them in a neighborhood within a local search [39]. In each iteration of the local search, one neighborhood structure is chosen at random and applied to the cross-over output sequence (offspring).  
The following are the six types of neighborhood structures that are based on VNS:  
* Swap neighborhood: Two positions are randomly selected from the sequence, and their positions are swapped.
* Insert neighborhood: Two elements are chosen at random and the latter element is placed in front of the earlier element.
* Inverse neighborhood: The elements are rearranged between the two randomly chosen positions in reverse order.
* Four positions are randomly selected from the sequence and their positions shuffled (rearrangement).
* Adjacent neighborhood: A position is randomly selected and switched to its adjacent position.
* Inverse neighborhood: Two positions are chosen from the sequence randomly, and then, the element order between those two positions is reversed.
3. Acceptance criteria and update: first, we find the delta makespan, i.e., the difference between the neighborhood makespan and the current makespan. Acceptance probability functions are crucial for accepting and rejecting new solutions based on the makespan difference and current temperature. If the new solution is better, i.e., the makespan is less than the current makespan, it is accepted; otherwise, it is rejected.
4. Cooling schedule: This uses an exponential cooling scheme. The temperature steadily and gradually drops with the cooling rate (temperature\"cooling rate).
5. Final temperature: The final temperature is a stopping condition. When the current temperature is equal to the final temperature, the local search loop stops, and we obtain the new solution. After obtaining the new solution, we update the population and control go to the HA next iteration.