esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
#### State of the Art
[19] illustrated the Italian pilot action of the Envelope Approach to improve Sustainability and Energy Efficiency in existing multi-storey multi owner residential buildings (EASEE) project, focusing on the analysis of energy refurbishment trends of the Lombardy region residential building stock. The paper focuses on precast multilayer panel outer solutions that basically add insulation to the outdoor facades without decreasing the useful surface of the building. On the contrary that in the present paper, the authors did not consider the renovation of other elements of the envelope; thus, the main difference with this article is that the renovation solutions were not selected following a cost-benefit analysis. Finally, the renovation solution was installed in a pilot apartment building in Milan, thereby assessing its effects on energy saving. After analyzing the current state of the art, we can determine the main novelties proposed in this paper:  
* LCC approach showing the different alternatives for the renovation. * Renovation measures comprising of facades, windows, roofs, slabs, ventilation system, infiltration improvement, thermal bridges, and domestic hot water production. * The price of the renovation measures was taken from a national official database of construction which includes the installation costs. * Identification of the cost optimal combination of measures that minimize the LCC over a 30 year period for a pilot building in a given climate. * Definition of a package of optimal solutions that are remarkably close to the optimum set of renovation measures that could be used for similar buildings in the same climate region.
# CarrDet: Attention-Symmetry-Based Microscopic Carrier Detection for Wastewater Treatment
## 6 Industry Practice  
During the practical domestic WWT process, approximately 6000 wastewater microscopic images are automatically generated on a daily basis. Subsequently, manual carrier detection is conducted at Environmental Protection Technology Co., Ltd. to recognize and quantify carriers, which establishes the basis for subsequent steps in WWT.  
However, due to constraints in terms of manual work and time costs, it is impractical to have a sufficient number of experts for the accurate recognition task. This could potentially compromise the efficiency and effectiveness of the subsequent WWT process. Therefore, the necessity of an efficient and continuously integrated automated carrier detection method becomes crucial.  
To validate the applicability of our method in a practical scenario, we collected 621 wastewater microscopic images that were detected by inexperienced engineers from the wastewater treatment plant. After being examined by two experts over a week, the corrected results of CarrDet show that CarrDet successfully rectified a total of 231 images, 166 of which were accurate, while 65 images were found to have incorrect assignments. Specifically, out of the 166 accurately corrected images, 398 unrecognized carriers with shallow edges were correctly added by our method, and 273 false positives caused by defective carriers were rectified. On average, this resulted in 2.4 accurate additions and 1.6 correct rectifications per image. The results show that manual carrier detection in wastewater microscopic images has limitations, while CarrDet's real-world performance is excellent in assisting manual detection.
# Machine Learning Method for Prediction of Hearing Improvement After Stapedotomy
## 2 Materials and Methods
### Evaluation Criteria  
The mean absolute error (MAE) and standard deviation (SD) were used to assess the models' prediction accuracy on each target. As the models directly predict the postoperative hearing intensities, the computed MAEs and SDs are in dB. For visualizations, the predictions were first rounded to the nearest value, divisible by 5. This format matches the structure of the standard audiogram, which contains hearing intensity measurements accurate to the nearest multiple of 5. Rounding can reduce or increase the error. However, the values in close proximity to the class (\(|e|<2.5\)) are all correctly adjusted. Therefore, rounding is preferred for model inference.  
For further evaluation, benchmarks were generated using naive predictions. Three naive methods, x_average, y_average, and x, were added. The prediction of x_average was the average of all preoperative hearing intensity measurements of the same frequency as the postoperative hearing intensity that we were predicting. For example, if the target hearing level at AC frequency of 1000 Hz was predicted, this naive method generated predictions for each sample in the test set, by taking the average preoperative AC hearing level at 1000 Hz, computed from training data. Therefore, for each test sample, the same (average preoperative) value was predicted. Similarly, the y_average is the average of all postoperative hearing intensities at the same frequency. To illustrate, for predicting the hearing level at an AC frequency of 1000 Hz, the average postoperative AC hearing level at 1000 Hz computed from the training data would be used. The x naive method predicted the target hearing intensity value by taking the preoperative hearing intensity at the corresponding frequency of the same sample. For instance, for the hearing level prediction at an AC frequency of 1000 Hz, the preoperative AC hearing intensity at 1000 Hz would be extracted from the same sample in the test set. In other words, this method predicts that the surgery will not introduce any changes in hearing level. Unlike other two naive methods, the x naive method is the only method that utilizes the test set and generates different predictions for each sample. Averages of the_average methods were calculated on training data, as illustrated in the examples.
## 2 RF Communication Devices
### IoT Technology  
The IoT is a relatively new technology that uses the operating principles of radars. Its usability relies on the fact that it operates with low power [33; 34; 35; 36; 37]. Thus, objects with a small battery can transmit very small packets of information to long distances. IoT devices are used to transmit limited data. Some examples are smart homes, devices that do not have a power source, drones that transmit and receive information from other objects with low power consumption, and satellites that collect data from objects and devices in remote areas, but there are many other applications. IoT is limited by the fact that it cannot transmit continuously at a high data rate. Generally, IoT devices have the following four characteristics: long range up to 18 km (sometimes much more, such as in space applications), long battery lifetime up to 10\(-\)20 years, the ability to utilize free and public networks, and low costs for devices and infrastructure.
## 7 Modulation of the Transcriptional Supercoiling Response by NAPs  
It was observed that the changes in supercoiling induced in growing bacterial cells under the influence of drugs modulating the topoisomerase activities produce distinct, long-range transcription patterns [40; 65; 96; 97]. The supercoiling responses of genomic transcription were found to vary with the growth phase and be distinctly modulated by NAPs, such as FIS, H-NS, IHF, and HU [31; 57; 66; 96]. Although both FIS and H-NS constrain negative supercoils [43; 98], it was shown that the binding of FIS at helically phased sites in the UAS of stable RNA promoters stabilizes coherently bent DNA microloops buffering the promoter activity on deviations from optimal superhelicity [83; 88; 99]. Another highly abundant NAP, HU, appears involved both in the maintenance of the global supercoiling level [45; 46; 100] and in the topological buffering (insulation) of transcription units [101]. The cells lacking HU demonstrate a loss of higher-order structural features of the nucleoid such as transcription foci as well as produce an aberrant genomic transcription pattern [66]. In the plant pathogen _'Dickeya dadantiti'_, deletion of the abundant NAP IHF leads to the spatial expansion of the transcriptional supercoiling response from the OriC and Ter poles along both chromosomal arms [57]. In both _E. coli_ and _D. dadantiti_, such spatial patterns of the supercoiling response, induced by environmental stress or topoisomerase poisons/inhibitors and modulated by NAPs, have been variably described as gene proximity networks [102], extended functional domains [12], stress-response domains [96], or coherent domains of transcription, also known as CODOs [103].
## 3 Results
### Result Validation
The validation of SNA results using gradient boosting not only reinforces the findings but also highlights the practical applications of machine learning models in combination with network analysis. SNA offers a more interpretable and efficient approach to understanding network relationships, while machine learning adds a layer of robustness, verifying that the identified relationships are indeed significant [61]. In the context of transportation networks, particularly peer-to-peer car-sharing systems, the combined use of SNA and gradient boosting provides a comprehensive understanding of user preferences. This methodology can be applied to other mobility platforms, allowing operators to perform the following:  
Focus on the most important vehicle attributes (e.g., safety, fuel efficiency) to increase rental conversions. Use machine learning models like gradient boosting for the continuous validation of user preferences, ensuring that platform offerings stay aligned with evolving user demands. Optimize platform features by focusing on central nodes in the network, improving user experience. The gradient boosting analysis validated the results obtained from the social network analysis, confirming that betweenness centrality, closeness centrality, and degree centrality are the most critical factors in influencing vehicle selection on a peer-to-peer car-sharing platform. The similarity in findings between both methods demonstrates the reliability of SNA as an analytical tool in transport systems. This validation highlights that while SNA provides an intuitive and efficient method to analyze networks, combining it with machine learning models like gradient boosting offers a deeper and more robust understanding of user decision-making behavior. This approach can be applied to optimize the design and functionality of car-sharing platforms, helping to meet the needs of both vehicle owners and renters.
## 1 Introduction
The main goal of the present paper is to design an algorithm to real-time tune the control penalty factor, while simultaneously ensuring the system stability and good control performance (for a large variation range of the unmeasurable perturbations which can disturb the controlled process). For a control system having as its main objective the rejection of the disturbance effect, two situations can be considered, depending on the possibility of measuring the disturbance. For the case when the disturbance is directly measurable, a strategy for on-line tuning of the control penalty factor was described by the authors in [17]. For the case when the disturbance is not directly measurable, a tuning strategy can be designed by considering an indirect measurement of disturbance, more precisely, by measuring the disturbing effect produced by it (this being the subject of this paper). In order to validate the proposed control strategy, an induction generator connected to a power system was considered as a controlled plant. An induction generator is a highly nonlinear plant (described by a mathematical model of the seventh order) and its operating point is frequently changing under the action of internal or external disturbances [20; 21; 22; 23; 24]. In this case, an electrical load/unload is considered one of the main disturbances whose effect must be rejected by the system. Such a disturbance cannot be directly measured. However, its effect on the process output can be measured. For any type of disturbance, this effect translates into the error of the controlled output. Due to this reason, the proposed strategy must be tested for any disturbance (measurable or unmeasurable) that produce an output error. For the case of an induction generator chosen as a controlled process, the mechanical torque variation and the load/unload regimes are the considered disturbances. An extension of the value range of disturbances, whose effect can be rejected, is the major objective of the new proposed control algorithm, leading to an increase of the control system stability and robustness. Although the validation of this improved control strategy will be performed on this particular case (induction generator), its logic is applicable for any minimum variance control of other complex plant.
# RANDnet: Vehicle Re-Identification with Relation Attention and Nuance-Disparity Masks
## 2 Related work  
### Vehicle Re-Identification  
To achieve discriminative feature representation in vehicle re-identification, some previous methods utilize orientation information. As vehicles are shot by cameras from various angles, images of an identical car from distinct orientations would vary largely, and sometimes it is difficult to identify similar vehicles of the same orientation. [PERSON] et al. [11] present a novel framework composed of the orientation invariant feature embedding and the spatial-temporal regularization. In addition, to obtain vehicle key points, which can be used to distinguish similar cars via subtle differences, [PERSON] et al. also adopt a key point regressor. [PERSON] et al. [15] tackle the vehicle re-identification problem by learning viewpoint-aware deep metrics through a two-branch network. With adversarial training architecture, [PERSON] and [PERSON] [16] utilize a viewpoint-aware attention model to realize multi-view feature inference from single-view input. [PERSON] et al. [17] focus on the uncertainty of different vehicle orientations and utilize LSTM to model transformations across continuous orientation variations of the same vehicle. [PERSON] et al. [18] propose train re-identification models for vehicles, orientations, and cameras, respectively. Then, the final similarity between the testing images is penalized by the orientation and camera similarity.  
Besides orientations, some previous works address vehicle re-identification problems by exploiting local details and regions [19; 20; 21]. [PERSON] et al. [22] design a parser to segment four views of a vehicle and propose a parsing-based view-aware embedding network to generate fine-grained representations. To address the near-duplicate problem in vehicle re-identification, [PERSON] et al. [19] propose a part-regularized approach that can enhance local features. [PERSON] et al. [23] introduce a multi-branch model that learns global and regional features simultaneously. In [23], ratio weights of regional features are adaptively predicted for the fusion process. Furthermore, to optimize the distance within and across vehicle image groups, a Group-Group loss is also proposed. [PERSON] et al. [24] propose a two-stage framework which incorporates important visual-spatial-temporal path information for regularization. [PERSON] et al. [25] present self-supervised attention for vehicle re-identification to extract vehicle-specific discriminative features. However, these methods may only identify too small discriminable regions and thus reduce the generalization of the network. We visualized the feature map of IBNnet [14], a commonly used backbone network of these methods. As shown in the left column of Figure 2, the network pays too much attention to the position of the car lights. In this paper, we also utilize discriminative regions to improve the re-identification accuracy. Specifically, a region mask is generated to highlight vehicle details. Note that no additional manual annotation is involved in this process.
## 4 Results and Discussion
### Vertical Swelling Pressure
#### 4.3.2 Effect of Initial Dry Density  
For a certain water content, there is a power function relationship between the vertical pressure on compacted bentonite without experiencing deformation, and the initial dry density [46; 47]. Figure 15 shows the relationship between the initial dry density of MB specimens and the corresponding final swelling pressure under saline water conditions (Equation (14)). The correlation coefficient (R\({}^{2}\)) is 0.98.  
\[P_{final}=25.25\rho_{d}^{8.11} \tag{14}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Initial Dry Density (g/cm\({}^{3}\))** & \(a\) & \(b\) \\ \hline
1.25 & 0.0023 & 0.00025 \\
1.40 & 0.0024 & 0.00047 \\
1.55 & 0.0048 & 0.00093 \\
1.75 & 0.0077 & 0.00418 \\
1.85 & 0.0106 & 0.00509 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Relationship between the initial dry density and time/swelling pressure.  
Figure 14: Relationship between the ratio of time to swelling pressure and swelling time for MB specimens with different dry densities under saline water conditions.  
Figure 13: Relationship between swelling pressure and swelling time for MB specimens with the same water content and different initial dry densities under saline water conditions.  
where \(P_{final}\) is the swelling pressure (kPa), and \(\rho_{d}\) represents the initial dry density of the MB specimen (g/cm\({}^{3}\)).
## 3 Distributed Jamming System Model for LEO Constellation Downlink
### Multi-Objective Optimization Objective Functions and Constraints  
In the distributed jamming system of LEO constellations, considering a given jamming success rate, the multi-objective optimization problem involves maximizing the total jamming power, minimizing relay delay, and minimizing frequency compensation. This can be formulated as follows:  
\[\left\{\begin{matrix}max(P_{total})\\ min\left(\Delta\tau_{ijk}\right)\\ min\left(\Delta f_{ijk}\right)\\ s.t.:\eta\geq\eta_{0}\end{matrix}\right. \tag{10}\]  
where \(\eta_{0}\) is the minimum required jamming coverage rate.
# Multi-Layer Feature Fusion-Based Community Evolution Prediction
## 2 Related Work  
### Community Evolution  
In a dynamic network, a community will evolve with a change in the network. To study the changes in a community in a relevant snapshot, scholars have proposed relevant community evolution models. These models define community evolution events. For the specific definition of community evolution events, different models will be different, and the most commonly used model is group evolution discovery (GED). Since the algorithm proposed in this paper analyzes community evolution through GED, this section will briefly introduce community evolution events in GED.  
In GED, community evolution events are divided into seven categories:  
* Birth: a community exists in the current snapshot, but in the previous snapshot, no community could match that community.
* Survive: two communities in the adjacent snapshot are identical or only a small number of nodes are different.
* Dissolve: a community existed in the previous snapshot, and no community can match it in the current snapshot.
* Grow: in the current snapshot, some new nodes join the community, and make the size of community bigger than the previous snapshot.
* Shrink: shrinking is the opposite process growing; a community is reduced in size compared to the previous snapshot.
* Merge: a community in the current snapshot is formed by merging several communities in the previous snapshot.
* Split: a community divides into several small communities in the current snapshot.
## 3 Proposed Model
### Feature Extraction Using Restricted Boltzmann Machine  
The power spectral density E in RBM seems to have the parameterization using weighted vector W as in equation, using variables \(v\) and \(h\) indicating a pair of transparent and concealed variables correspondingly in (6).  
\[E(v,h)=-a^{T}v-b^{T}h-v^{T}Wh \tag{6}\]  
The biased factors for transparent and concealed units are indicated by \(a\) and \(b\), respectively. The probability density \(P\) is calculated using Equation (12) with \(v\) and \(h\) in parameters of \(E\):  
\[P(v,h)=\frac{1}{Z}e^{-E(v,h)} \tag{7}\]  
Equation (7) gives the normalizing variable \(Z\):  
\[Z=\sum_{v^{\prime},h^{\prime}}\ e^{-E(v^{\prime},h^{\prime})} \tag{8}\]  
Furthermore, the likelihood of \(v\) across hidden neurons is provided by Equation (8), which is the combination of the above-mentioned formulae:  
\[P(v)=\frac{1}{Z}\sum_{h}\ e^{-E(v,h)} \tag{9}\]Log-likelihood Equation (9) is used to evaluate the variance in training data on the basis of \(W\):  
\[\sum_{m=N}^{n=1}\frac{\partial logP(v^{n})}{\partial W_{ij}}=\left\langle v_{i}h _{j}\right\rangle_{data}\ -\left\langle v_{i}h_{j}\right\rangle_{model} \tag{10}\]  
Here \(\left\langle v_{i}h_{j}\right\rangle_{data}\)_and_\(\left\langle v_{i}h_{j}\right\rangle_{model}\) represent the values expected for the data and distribution model, respectively. The training data is used to calculate weights in the network for document training examples, as shown in Equation (10):  
\[\Delta W_{ij}=\epsilon\big{(}\left\langle v_{i}h_{j}\right\rangle_{data}\ -\left\langle v_{i}h_{j}\right\rangle_{model}\ \big{)} \tag{11}\]  
It is feasible to get independent information from that because neurons are not coupled at the concealed or transparent levels. Furthermore, for specified \(h\) and \(c\), the activating of hidden or visible units is uncorrelated. The conditional property of is provided in Equation (11) for a provided \(v\):  
\[P(h\mid v)=\prod_{j}\ P\big{(}h_{j}\mid v) \tag{12}\]  
where \(h_{j}\in\{\)0,1\(\}\) and the probability of \(h_{j}=1\) is given in Equation (12):  
\[P\big{(}h_{j}=1\mid v\big{)}=\sigma\Big{(}b_{j}+\sum_{i}\ v_{i}Wij\Big{)} \tag{13}\]  
Here the logistic function \(\sigma\) is specified as in Equation (13):  
\[\sigma(x)=\big{(}1+e^{-x}\big{)}^{-1} \tag{14}\]  
Likewise, when \(v_{i}=1\), the conditional property is estimated by Equation (15),  
\[P(v_{i}=1\mid v)=\sigma\Big{(}a_{i}+\sum_{j}\ W_{ij}h_{j}\Big{)} \tag{15}\]  
In practice, unbiased testing is difficult with \(>\), but it could be used to recreate the very first testing of \(v\) from \(h\), and thereafter Gibbs sampling will be used for several repetitions. Each component of the concealed and transparent layers is modified in tandem using Gibbs sampling. Finally, by combining the anticipated and modified quantities of h and v, the right selection is calculated with \(>\). Recurrent neural networks can be started with RBM parameters
## 3 Results
### Structural Characterisation
#### 3.1.2 Fourier Transform Infrared (FTIR) Spectroscopy  
The molecular structures of synthesised polypyrrole and nanocomposites with 10% and 20% silver (Ag) embedded into polymer matrix were analysed using FTIR spectroscopy (Figure 5). The allure of the obtained FTIR spectra confirms the structural configuration of the PPY indicated from the literature data: the peak at 780 cm\({}^{-1}\) attributed to C-H out of plane of the polypyrrole ring, the peak at 1040 cm\({}^{-1}\) represents N-H in plane deformation [40], the peak located at 1186 cm\({}^{-1}\) is assigned to C-N stretching vibration [41]. The peaks at 1288 cm\({}^{-1}\) and 1040 cm\({}^{-1}\) are assigned to C-N in plane vibration; the peak at 1557 cm\({}^{-1}\) is attributed to the polypyrrole ring's vibration [41; 42]. The broad peak at 3228 cm\({}^{-1}\) is attributed to the stretching of N-H and C-H bonds [43; 44]. The Ag NPs' introduction into the PPY structure leads to some PPY FTIR characteristic bands' displacements, indicating specific interactions between Ag NPs and the PPY functional groups, especially those in the N-H group region [43; 45].  
Figure 4: Raman spectra recorded on: (**a**) PPY; (**b**) 10%Ag/PPY; (**c**) 20%Ag/PPY.