esa-sceva/satcom-chunk-collection huggingface dataset:
# An Inter-Comparison Study of Multi- and DBS Lidar Measurements in Complex Terrain
## Materials and Methods
### Data Treatment, Quality Control and Coordinate Systems
The analysis in this paper is based on 10 min averaging intervals, which is the standard in wind-energy applications. Before the analysis of the wind-vector components, a rotation into the mean wind direction (\(\overline{v}=0\) m\(\cdot\)s\({}^{-1}\)) is performed. A second rotation forcing \(\overline{w}=0\) m\(\cdot\)s\({}^{-1}\) as done in many other micrometeorological studies (cf. [33]) is not carried out as the aim of the study is mainly a comparison of the different measurement techniques. For this same reason, corrections which are often applied to eddy-covariance measurements (cf. [34]), such as correcting for high- and low-frequency loss, are not applied to the sonic anemometer measurements. In a previous study, [PERSON] and co-authors reported deviations between a Gill HS and other sonic anemometers, which was attributed to the Gill HS [35]. However, in their study, the internal correction/calibration was turned off, which removes the sensor head correction and the instrument-dependent calibration. In the present study, the internal corrections were turned on and the problems reported in [35] are assumed to not to be present in the collected data.
## 4 Case Study  
In this paper, an IEEE 33-node distributed power network is used as an example to calculate distributed power system transmission loss and voltage fluctuation by employing MATLAB simulation. Figure 6 is the structure of the IEEE 33-node distributed power network. The Node 0 is selected as the reference node because it is directly connected to the main grid. For this system, 12.66 kV is selected as the reference voltage and the maximum active load (ignoring the electric vehicle charging load) of this network is 3.72 MW. Figure 7 is the daily load curves of the given distributed power network in different seasons.  
Figure 5: A flow diagram of distributed power system transmission loss optimization based on the electric vehicle 0-1 integer programming model. EV = electric vehicle.  
Figure 6: The structure of the IEEE 33-node distributed power network.  
Figure 7: The daily load curves of the given network in different seasons [26].  
In this network, there are about 950 families, and the average demand of each family is 4 kW at peak demand time. Suppose that the penetration rate of electric vehicles is 30% in this system, thus, the total number of electric vehicles is 285 in this network. In addition, it is assumed that all electric vehicles are distributed evenly in all nodes to charge their battery. In the next section, simulation results of the optimal control strategy proposed in Section 3, based on this given case, will be given to show the effectiveness of the proposed optimal strategy.
# Explainable Artificial Intelligence for State of Charge Estimation of Lithium-Ion Batteries
## 5 Discussion
### Future Work  
There are various areas within this work that could be expanded upon. These include the following:  
**Transfer learning**: We only focused on the e-Golf car model within this work. However, there are other available car models in the data repository of AVILOO, such as the Nissan Leaf and Tesla Models S. If we are to make our work applicable with other car models, transfer learning techniques will be required. Transfer learning allows NNs to effectively learn from new domains without forgetting the knowledge learned from previous domains. This enables a NN to not be restricted by the information of one particular domain, allowing the model to be more versatile when deployed in real-world applications. In our case, transfer learning will enable accurate SoC predictions of the other car models, such as Nissan Leaf and Tesla Models S, whilst maintaining the low SoC prediction errors obtained for the e-Golf car model.  
Preliminary work on transfer learning with SoC EV battery estimation with different EV battery models has been carried out in studies such as those performed by [PERSON] et al. [20], [PERSON] et al. [22], and [PERSON] et al. [57]. They have confirmed that the employment of transfer learning techniques with EV batteries are able to achieve higher accuracy in SoC estimation where prediction errors were minimized.  
**NN ensemble**: We provided the SoC estimation results of different NN architectures and compared their performances with each other. The different NNs can serve as necessary components of an ensemble [58], which can be used to improve the robustness and reliability of SoC estimation. An ensemble combines the outputs of different methods--in this case, the different NNs that have been developed--and produces a more reliable output. The basic approach of achieving this is the averaging the different methods' outputs.  
There also exist other methods that strengthen the reliability of the output. An example of such is the work by [PERSON] et al. [59], who performed quantile regression on lithium-ion batteries such that the regression result was not a value but a likelihood distribution of what the value would likely be.
## 1 Introduction  
The proportion of the global energy consumed by the existing building stock has risen from 20% to 40% since 2008 [1], partially influenced by global climate change [2]. For this reason, institutions such as The International Energy Agency have highlighted the need for significant increases in the number of existing buildings being renovated [3, 4].  
In this context, some studies already state that \"the highest potential for energy reduction through building renovation lies in specific residential typologies, apartment buildings and single-family homes\" [5], while others show that it is possible to reduce greenhouse gas emissions by approximate 40% by deploying the technology already available in the market [6]. Therefore, buildings are considered one of the strategic sectors for reducing greenhouse gas emission to the 2030 target of values 55% below those in 1990, for improving energy efficiency to 32.5% and reaching the 32% share of renewable energy as a part of the European Green Deal [7].  
In projects involving the in-depth renovation of buildings, especially those aiming to meet nearly zero energy (nZEBs) requirements, cost-benefit analysis should be accurately developed [8]. These analysis methods include the net present value (NPV), investment cost (IC), payback (PB) or life cycle cost (LCC) as key indexes. However, most of the stakeholders involved in building renovation projects do not have the skills or qualifications to do so correctly [9]. To this end, the HAPPEN project has developed a specific Mediterranean zero energy buildings (MediZEBs) approach [10], aimed at fostering the renovation of existing residential buildings. This holistic approach is based on the following project pillars:  
1. Engagement and training: targets final users (owners and inhabitants) through knowledge transfer and behavioural upgrades, as well as building professionals, entrepreneurs, workers, and policy makers.
2. Optimal solutions: an extensive optimization project based on LCC analysis of the renovated buildings has been performed to compose cost-optimal packages of solutions (POSs) to be applied to different residential typologies according to a one-stop-shop and a step-by-step intervention logic.
3. Finance and Regulations: regards the testing of available innovative financial solutions (e.g., guarantee and solidarity funds, credit transfer mechanisms, etc.) and the development of the HAPPEN financial solution, fully integrated with the POS.  
The study presented in this paper is based on the requirements already stated in the EPBD [11] from the EU where, applying cost optimality and LCC of the most optimized energy renovation measures becomes mandatory (articles 5, 7, 9). Moreover, in annex III of said directive, the methodology framework to identify the cost optimal level of energy performance is explained. The instructions described there are illustrated through the case of a pilot building located in Castellon de la Plana (Spain), the main results and conclusions of which are presented below.
## 3 Experimental Results and Discussions
### Comparative Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \multicolumn{2}{c}{**Weights**} & \multicolumn{1}{c}{**Coefficients**} & \multicolumn{1}{c}{**Standard**} & \multicolumn{1}{c}{**t Stat**} & \multicolumn{1}{c}{**\(p\)-Value**} & \multicolumn{1}{c}{**Lower**} & \multicolumn{1}{c}{**Upper**} & \multicolumn{1}{c}{**Lower**} & \multicolumn{1}{c}{**Upper**} \\ \multicolumn{2}{c}{} & \multicolumn{1}{c}{**Error**} & \multicolumn{1}{c}{**95\%**} & \multicolumn{1}{c}{**95\%**} & \multicolumn{1}{c}{**95\%**} & \multicolumn{1}{c}{**95\%**} & \multicolumn{1}{c}{**95\%**} \\ \hline Intercept (w\({}_{0}\)) & \(-499.735\) & \(188.3426\) & \(-2.65333\) & \(0.008213\) & \(-869.736\) & \(-129.734\) & \(-869.736\) & \(-129.734\) \\ w\({}_{1}\) & \(-0.02747\) & \(0.015863\) & \(-1.73172\) & \(0.083913\) & \(-0.05863\) & \(0.003693\) & \(-0.05863\) & \(0.003693\) \\ w\({}_{2}\) & \(0.920282\) & \(0.013497\) & \(68.18238\) & \(2.2\times 10^{-282}\) & \(0.893766\) & \(0.946797\) & \(0.893766\) & \(0.946797\) \\ w\({}_{3}\) & \(0.139906\) & \(0.018693\) & \(7.484493\) & \(3.07\times 10^{-13}\) & \(0.103184\) & \(0.176629\) & \(0.103184\) & \(0.176629\) \\ w\({}_{4}\) & \(-35.0648\) & \(6.56439\) & \(-5.34166\) & \(1.38\times 10^{-7}\) & \(-47.9606\) & \(-22.1689\) & \(-47.9606\) & \(-22.1689\) \\ \hline \end{tabular}
\end{table} 
Table 9: Regression equation table. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Hours**} & \multirow{2}{*}{**LOC**} & \multirow{2}{*}{**LOC**} & \multirow{2}{*}{**LOC**} & \multirow{2}{*}{**LOCAL**} & \multirow{2}{*}{**T\({}_{\text{Eff}}\)**} & \multicolumn{2}{c}{**Real**} & \multirow{2}{*}{**MLR**} & \multirow{2}{*}{**GA-Fuzzy**} & \multicolumn{2}{c}{**GA-Fuzzy**} & \multirow{2}{*}{**AC-Fuzzy**} & \multirow{2}{*}{**AC-Fuzzy**} \\  & & & & & & & & & & & & \\  & & & & & & & & & & & & \\
00–01 & 24,234  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Hours** & **LOC** & **L\({}_{\text{WC}}\)** & **L\({}_{\text{CAL}}\)** & **T\({}_{\text{Eff}}\)** & **Real** & **MLR** & **GA-Fuzzy** & **GA-Fuzzy** & **AC-Fuzzy** & **AC-Fuzzy** \\  & & & & & & **Consumption** & **APE \%** & **GA-Fuzzy** & **AFE \%** & **AC-Fuzzy** & **APE \%** \\ \hline
00–01 & 26.788 & 25.966 & 25.577 & 2.25 & 26. +++ ==WARNING: Truncated because of repetitions==
710 & 26.160 & 2.060 & 27.598 & 3.325 & 26.274 & 1.631 \\  
++  
Firstly, as seen in Tables 10 and 11, the proposed mature-inspired technique-based forecasting model has smaller MAPE values compared with the statistical-based model MLR model. As seen in Figure 24, artificial intelligence-based models have better adaptation and yield more promising results than the statistical-based model in a nonlinear consumption zone. As with many statistical models, the MLR model provides more successful results, as expected in the linear zone.
# Using Deep-Learned Vector Representations for Page Stream Segmentation by Agglomerative Clustering
## 3 Materials and Methods
### Metrics  
Since we view the task of PSS as a clustering task, we also report the performance of models using a clustering/segmentation metric from the computer vision domain, called  
Figure 1: Illustration of the PSS task, where the numbered rectangles indicate the pages. In this illustration, we have a stream of seven pages that are contained within three documents. The task is to identify pages one, four and six as pages that start a new document.  
_Panoptic Quality_ (PQ) [20], as well as also reporting the scores of models using the P1 metric on the page labels alone, as this has several drawbacks, which have been discussed at length in the literature [21; 22]  
In the computer vision domain, the Panoptic Quality metric is used to measure how well a model is able to recognize and segment parts of images. As an example, consider a task in which we have to detect animals in an image, and our model returns bounding boxes of pixels where it predicts an animal. The metric is most easily understood when broken down into two components, namely the _Recognition Quality_ and the _Segmentation Quality_. The former measures how many of the objects have been recognized, and the latter measures how well these detected objects are recognized. (In the case of the animal pictures, how close are the predicted bounding boxes to the actual boundaries?) For computation of the Recognition Quality, the Jaccard similarity between a document in the gold standard and all documents in the predicted stream is used to calculate true positives. If there exists a predicted cluster \(p\) for which the Jaccard similarity with a gold standard cluster \(t\) is higher than 0.5, this is counted as a true positive, which also forces each gold standard document to be matched to at most one predicted document. In the case of documents, this would give the ratio of how many pages were in the same document for predicted and gold standard.  
The recognition quality is then calculated as \(\frac{|TP|}{|TP|+\frac{1}{2}|FN|+\frac{1}{2}|FP|}\), where FP are the false positives and FN are the false negatives.  
For the segmentation metric, we want to know how well the true positives were matched, which we will calculate using the Jaccard similarity, and this yields the formula below:  
\[SQ=\sum_{\{p,t\}\subset TP}JaccardSimilarity(p,t) \tag{1}\]  
Combining both the Recognition Quality and the Segmentation Quality, the complete Panoptic Quality metric is defined as the multiplication of the two, where the Segmentation Quality in essence weights the Recognition Quality.  
For both clustering and classification we measure results using the F1 score for the positive class, i.e., pages that mark the start of a new document.  
We report the average scores over all streams in the test set and compare obtained scores against a non-learned baseline, in which every predicted document has the mean document length of that stream, which we will refer to as the _Mean Document Length Baseline_.
# An Evaluation of Image Slicing and YOLO Architectures for Object Detection in UAV Images
## 3 Materials and Methods  
Variations in object sizes, orientations, and shapes pose significant challenges for object detection in aerial imagery. Studies discussed in the Related Work section employed anchor boxes and multi-scale feature pyramids to handle this issue, enabling the detection of objects at various scales and orientations. This study will examine the ISA slicing algorithm, which is proposed to increase object detection accuracy in high-resolution images. The slicing size can be determined dynamically in the ISA slicing algorithm. A fixed slicing size has been examined in previous studies. Our study will examine the effect of different slicing sizes on object detection accuracy. Because the determined slicing size directly affects the overlap ratio. In this case, it determines the objects arriving at the edge points and the status of the object labels after slicing. The study's main objective is to determine the correct ISA slicing size. The general flow diagram of the study is shown in Figure 1.
## 3 Results
### Cell Proliferation: BrdU Incorporation Assay  
Since the MTT test demonstrated an increase in metabolic activity of both cell lines, a cell proliferation assay based on bromo-deoxyuridine incorporation in the DNA of proliferating cells was performed. A different response was observed between the HepG2 cell line and canine At-MSCs after the treatment.  
The analysis of the DNA synthesis in HepG2 in response to LLP treatment, revealed a more complex picture compared to that of the At-MSCs (Figure 3A). The treatment with 200 \(\upmu\)g/well of LLP digested with collagenase showed a moderate but statistically significant stimulatory effect (\(p<0.01\)) compared to the control, while the cells treated with the higher concentrations (500 \(\upmu\)g) showed a replicative activity similar to that of the control.  
The LLP significantly increased the At-MSCs' proliferative capacity at both tested concentrations (200 \(\upmu\)g/well and 500 \(\upmu\)g/well; \(p<0.001\); Figure 3B). The treatments induced a similar stimulating effect, thus indicating a probable saturation of the cellular response already at the lower concentration.  
Figure 3: Evaluation of BrdU incorporation in HepG2 and At-MSCs after LLP treatment **(A)** HepG2: treatment with a suspension prepared with 200 \(\upmu\)g of LLP increased DNA synthesis (\(p<0.01\)), while 500 \(\upmu\)g/mL showed no difference in comparison to the control. **(B)** At-MSCs: both 200 and 500 \(\upmu\)g of LLP induced an increase in BrdU incorporation (\(p<0.001\)). Different letters indicate statistically significant differences.  
Figure 2: MTT assay on HepG2 after LLP treatment (48 h) in serum-free medium. LLP treatments ranging from 200 \(\upmu\)g to 1000 \(\upmu\)g/well increased the MTT signal with respect to serum-free control cultures (\(p<0.001\)). A dose-dependent trend was observed; higher concentrations of LLP demonstrated an effect comparable to 10% FBS supplementation. Absence of a common letter means a statistically significant difference (\(p<0.001\)).
# Utilizing Energy Storage for Operational Adequacy of Wind-Integrated Bulk Power Systems
## 2 System Model
### Chronologically Integrated Generation and Load Model  
The evaluation period is divided into a number of UCPs, and the generating units to be committed during each UCP are decided based upon established reserve requirements, such as the N-1 criterion, the chronological load model, and the estimated wind power. The capacity commitment, \(WC_{k}\), from the wind resources in the \(k^{th}\) UCP is considered to be the expected value of the wind power distribution, as given in Equation (7).  
\[WC_{k}=Mean\left(W_{k,1},\ W_{k,2},\ldots W_{k,i},\ldots W_{k,int}\right) \tag{7}\]  
The ESS can either be committed as a separate reserve unit or in conjunction with wind power to satisfy its capacity commitment, \(WC_{k}\). The ESS capacity in the \(k^{th}\) UCP, corresponding to \(w^{th}\) state of wind power distribution is given by \(ES_{k,w}^{P}\). In these two modes of operation, \(ES_{k,w}^{P}\) is respectively given by Equations (8) and (9).  
\[ES_{k,w}^{P}=\min\left(\frac{SOC_{t-1}}{remaining\ duration\ of\ UCP},ES_{DP}\right) \tag{8}\]  
\[ES_{k,w}^{P}=\min\left(ES_{DP},\left(WC_{k}-W_{k,w}^{P}-Res_{k,i}\right)\right) \tag{9}\]  
A committed generation model is then developed for each UCP by integrating the generation models of the committed conventional units, wind power, and the capacity available from the ESS, \(ES_{k,w}^{P}\). The ESS is, however, integrated into the system load model during the hours when it is charged through the grid. This is done by sequentially adding the ESS load consumed based on its charging rate, \(ES_{CP}\), to the bulk load connected at the same bus. On the other hand, the ESS does not contribute to the system load when it is charged by wind power. Equation (10) gives the wind charging capacity, \(CR_{k,\ w}^{es}\), available from its \(w^{th}\) capacity state in the \(k^{th}\) UCP to charge the ESS.  
\[CR_{k,\ w}^{es}=\begin{array}{c}0,\ if\ W_{k,w}^{P}-WC_{k}\\ \min\left(\left(W_{k,w}^{P}-WC_{k}\right),ES_{CP}\right),\ if\ W_{k,w}^{P}-WC_{k} \end{array}\right\} \tag{10}\]  
It should be noted that the capacity available from the wind, \(AW_{k,w}^{P}\), and ESS, \(ES_{k,w}^{P}\), to the grid is different during the ESS charge and discharge modes. The integrated power probability table (IPPT) in Table 1 shows the capacity distributions in the charge and discharge modes. The different states of wind power and ESS capacity are obtained by applying [PERSON]'s rule on wind power and ESS capacity distribution [25].
# Clustering-Based Energy-Efficient Self-Healing Strategy for WSNs under Jamming Attacks
## 6 Simulation Results
### Fifth Scenario  
The jammer in the quadrant EF34 provokes 38% of the isolated nodes in the blue cluster. Now, node E4 changes its cluster and CH association to the yellow cluster. This can be appreciated in Figure 13.  
The clustering determines a power transmission of 19.05 dBm to surpass the jammer effect. This value is based on node E4, which requires this transmit power to reach the CH on  
Figure 12: Fourth scenario: where the network chooses the same previous CH to provide communication on the clusters. However, the number of isolated nodes decreased significantly owing to the new position of the jammer.  
position G7. Consequently, the amount of information transmitted for FP and PA strategies is 534,835 kb for FP and PA strategies. The residual energy is 14.92 J and 487.71 J, respectively.  
The cooperation strategies choose nodes E2, F1, F2, G3, G4, and H3 as eligible relays. However, only relays F1 and H3 exhaust their batteries. Similar to the previous scenarios, these relays have more associations. Consequently, the amount of information transmitted reaches 245,869 kb and 300,262 kb for CP and PAC strategies, respectively. These results are similar to the previous scenario and demonstrate the impact of cooperation in this scenario. The residual energy acquired is 72.98% \(\mathrm{J}\) for CP and 79.52% \(\mathrm{J}\) for PAC. For the proposed strategy, FCPA, the information transmitted is 914,288 kb, while the residual energy is 72.27%. Thus far, the results demonstrate the excellent tradeoff of FCPA strategy for the analyzed scenarios.