esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Application of Fiber Optic Cables in Geophysics Exploration
### Microsismic Monitoring of SMF  
The use of DAS fiber optic sensing technology has been demonstrated to be efficient in providing sufficient sensitivity to detect a significant subsurface event. In such scenarios, established reservoir surveillance techniques, including subsurface seismic imaging and microseismic analysis, are employed. Each of these techniques possesses its own set of advantages and drawbacks, collectively contributing to an enhanced comprehension of the treatment processes [65]. The analysis result of the DAS images shows that contemporary optical fiber technology offers sufficiently higher sensing sensitivity to measure a notable count of microseismic events. These events can then be integrated with data pertaining to the strain rate and temperature changes, as demonstrated by previous research [66]. The  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline  
researchers applied SMF cable to analyze microseismic events in a single horizontal DAS using guided waves [67]. The DAS data were acquired with an OTDR interrogator unit conducting on SMF and measuring in terms of strain rate. The acquisition parameters were1 m channel spacing and 10 m gauge length, with 2000 samples per second. The SMF cables were installed in a single horizontal well outside the casing in a deviated well that expanded into an unconventional reservoir formation. SMF cable was also deployed schematically with offset wells and vertical logging wells. With the aid of an ML learning approach, the guided waves and DAS measurement were able to automatically detect microseismic events with high accuracy. The microseismic events were detected based on a localization process grounded in guided wave dispersion. These properties were predicted in situ using known perforations as references. Directional measurement DAS with SMF resolved the uncertainty in certain event locations with guided waves. The result showed that SMF arrays have excellent potential for event detection due to their spatial resolution and affection for microseismic events. The research output indicated that the initial utilization of microseismic event localization using SMF could be achieved and considered inept without a guided wave.
## 3 Results and Discussions
### Data-Driven Prediction Model Development and Training  
#### 3.2.1 Data Processing  
In this study, all models were trained using the same simulated battery data obtained from the battery model established in Section 3.1. This dataset was generated from a battery model based on the second-order RC equivalent circuit, which was constructed and parameterized using the methods discussed in Section 2.1. After the parameters were identified, the model was used to simulate continuous charge and discharge cycles of the battery. The dataset includes recorded data of the battery current, voltage, and state of charge at 10 s intervals.
## 6 Application Scenarios of the Proposed Approach
### Applications in Assistive Listening Devices  
The proposed approach can be leveraged to assistive listening devices such as hearing aids in order to improve the overall audio experience of the listener. Due to the distributed nature of the multi-microphone array configuration used in this work, it possesses the advantages of spatial diversity, enhanced noise reduction performance, and flexibility of placement. Spatial diversity indicates the placement of microphones at different positions which can enable the speech enhancement system to distinguish between the speech emanating from the target source and interfering signals and noise arriving from different directions. This can, in turn, help the system to isolate and enhance the desired speech signal while suppressing undesired components. The enhanced noise reduction performance can be attributed to beamforming, which helps to focus on a speech signal arriving from a specific direction while suppressing other directions. Finally, owing to its distributed nature, the microphone array elements can be flexibly placed across the user's clothing,or they can be distributed across the indoor environment, so as to effectively capture the acoustic signals from different directions [59]. The proposed approach, if leveraged to a distributed microphone array-based hearing aid application, can localize the speech source-of-interest at each time frame and improve the intelligibility of speech by performing dynamic beamforming-based target speech enhancement.
## 2 Instrument operating characteristics
### Instrument performance: SNR and Height Error
The strategy for WSOA as a Jason-2 instrument was to retrieve the interferometric phase over 16*1 km pixels on-board, before data downloading. In this case, the possibility of applying the multi-looking process on ground offered a key degree of freedom. Another major objective is to get a quality of measurement as uniform as possible over the swath. However, degradations caused mainly by the range loss, the backscattering coefficient and the range resolution are far more important at the edge of the swath. The center of the antenna beam has then to  
Figure 6: (a) Number of looks associated with different incoherent average processing procedures in the range direction (b) Associated WSOA instrumental noise impact on the height error (the digitization step in the number of looks is related to the intrinsic range resolution, that results in discontinuity when considering 1 km pixels). be oriented toward the far range of the swath in order to maximize the gain there and compensate these losses. Figure 7 shows that a 3.5 degree antenna boresight produces an almost flat height standard deviation across the whole swath. This optimization results in the coherence previously shown in Figure 5 (a). The required instrumental error is so stringent that a large amount of multilooking is necessary. There is a modification that could be brought to the WSOA operating characteristics that could help get slightly better coherence, and allow us to reduce the number of looks and thus improve the intrinsic spatial resolution (see section 5). However, the WSOA operating characteristics cannot be completely assessed and discussed without determining the impacts of external factors on the error budget. This is the purpose of the next section.
## 5 Data Models
In addition, its current version cannot cover the protocols with interactions containing error messages and requests with more than one response type. In addition, it requires a medium-size data set for training the machine learning models which may not exist in commercial applications. In another attempt, [PERSON] et al. [15] to complement their recent work in generating categorical fields in service responses, proposed a method that identifies the request fields that can be used to drive the value of each categorical field based on those that acquire minimum conditional entropy and called them predictor set. Then, the method finds a value for the categorical response field from all the categorical response fields' values in the historical messages that have the highest joint probability distribution with the predictor values. The proposed method improves existing research by increasing accuracy and reducing complexity and works for both stateless and stateful protocols. It has some limitations such as it cannot incorporate more than one influential message type in generating the categorical fields. Different methods of inferring data model are summarised in Table 5. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Specification** & **Remarks** \\ \hline Predator [34] & Clusters pairs of requests and corresponding responses based on their request. It builds the request and response models for each medoid in the form of a Markov model. Responses are trained with their requests to find contextual dependency between data fields & Finding contextual dependency using a Needleman–Wunsch string alignment algorithm \\ \hline Opaque SV [12; 21; 23] & Similar to Predator excluding Markov models and using different clustering methods & - \\ \hline Roleplayer [19] & Defining fields inside the protocol byte stream by comparing two manually selected instances of the same dialogue and align the bytes & Works on fixed-length binary protocols uses heuristics to assign semantic values and interpretation to the various fields \\ \hline Daikon [55] & Extracting data relationships from execution traces & - \\ \hline Aalst [56] & Combining different perspectives to create complete process & - \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Different approaches of data model inference.
## 4 Results
### Design of the Base Isolation System (BIS)  
In addition to the seismic protection strategy based on the use of TMD, the strategy based on building seismic isolation is also considered. BIS is designed to increase the fundamental period \(T_{1}\) of the building up to the value \(T_{BIS}\) such that the corresponding spectral acceleration at the SLV, \(S_{A}(T_{BIS})\), is equal to or lower than the spectral acceleration, \(S_{A}(T_{1})\), divided by the maximum value of \(\frac{M_{Eq.}}{M_{Eq.}}\). For the OB, \(\frac{M_{Eq.}}{M_{Eq.}}\),\(max\) is equal to 7.38. The stiffness of the BIS, \(k_{BIS}\), is then determined from the total mass of the building, \(M\), which is equal to 1740 \(t\). For the considered case study, \(S_{A}(T_{BIS})=0.90\,\mathrm{m/s^{2}}\), which is attained for \(T_{BIS}=2.90\,\mathrm{s}\). Consequently, \(k_{BIS}=8175\,\mathrm{kN/m}\) is obtained. To provide the BIS with this stiffness, eight RIs with the properties shown in Table 7 are used. The RIs are placed at the base of the walls lying on the building perimeter. At the base of the remaining structural elements, FSSs are installed. The layout of RIs and FSSs in the building plan is shown in Figure 17.
## 2 Literature Review  
### Affective Computing and Virtual Reality  
The advent of affective computing marks a significant intersection of emotional study and technological advancements. This interdisciplinary field is dedicated to developing systems and devices capable of recognizing, interpreting, processing, and responding to human emotional states [16]. Central to enhancing the effectiveness of affective computing are the processes of emotion recognition and emotion induction [17]. Emotion recognition entails the accurate detection of emotional states using various data sources. These include facial expressions [18], gestures [19], speech [20], eye movements [21], heart rate [22], skin temperature (SKT), blood volume pulse (BVP) [23], wrist pulse signal (WPS) [24], electroencephalogram (EEG) [25], electrocardiogram (ECG) [26], electromyogram (EMG) [27], and galvanic skin response (GSR) [22]. These modalities provide a comprehensive array of physiological and behavioral cues that are instrumental in identifying and interpreting the complex landscape of human emotions.  
Emotion induction is a deliberate process designed to elicit specific emotional responses through a variety of techniques, including video clips [28], pictures [21], gameplay [29], music [30], virtual reality (VR) [27], and autobiographical recall [23]. These methods are commonly categorized as passive induction, where participants engage passively within a controlled setting. The passive induction approach offers several benefits, notably the capability to control the stimuli presented, standardize the measurement conditions, and reduce the influence of external variables on the experimental results [31].  
Recent studies have increasingly explored virtual reality (VR) across various domains. VR distinguishes itself from other emotion induction techniques through its ability to immerse individuals in highly realistic and meticulously controlled environments [32]. Leveraging advanced VR technologies, researchers can simulate a broad spectrum of scenarios that evoke authentic emotional responses and behaviors. Such profound immersion provides invaluable insights into human cognition and affective states, elucidating the genuine reactions of individuals across diverse emotional contexts. Building on this foundation, [PERSON] et al. [32], gathered data on facial expressions, heart activity, EDA, SKT, and ECG during 28 pre-labeled VR games, each tagged with a dominant emotion term. This data was analyzed using SVM, RF, XGBoost, and DT classifiers. The results demonstrated a correlation between the experienced emotions and the pre-labeled emotions based on their frequencies, indicating that VR environments effectively elicit the intended emotions. These findings underscore the vast potential of VR as a powerful tool for studying and understanding human emotions in a controlled yet dynamic setting.
###### Abstract  
This study investigates the design and sizing of the second life battery energy storage system applied to a residential building with an EV charging station. Lithium-ion batteries have an approximate remaining capacity of 75-80% when disposed from Electric Vehicles (EV). Given the increasing demand of EVs, aligned with global net zero targets, and their associated environmental impacts, the service life of these batteries, could be prolonged with their adoption in less demanding second life applications. In this study, a technical assessment of an electric storage system based on second life batteries from electric vehicles (EVs) is conducted for a residential building in the UK, including an EV charging station. The technical and energy performance of the system is evaluated, considering different scenarios and assuming that the EV charging load demand is added to the off-grid photovoltaic (PV) system equipped with energy storage. Furthermore, the Nissan Leaf second life batteries are used as the energy storage system in this study. The proposed off-grid solar driven energy system is modelled and simulated using MATLAB Simulink. The system is simulated on a mid-winter day with minimum solar irradiance and maximum energy demand, as the worst case scenario. A switch for the PV system has been introduced to control the overcharging of the second life battery pack. The results demonstrate that adding the EV charging load to the off-grid system increased the instability of the system. This, however, could be rectified by connecting additional battery packs (with a capacity of 5.850 kWh for each pack) to the system, assuming that increasing the PV installation area is not possible due to physical limitations on site.  
second life batteries; off-grid PV system; residential building; EV charging station
# Artificial Intelligence Evolution in Smart Buildings for Energy Efficiency
## 2 Application of AI to Smart and Energy-Efficient Buildings
TBR programs propose an optimal and automatic residential scheduling of energy consumption to minimize the electricity payment and minimize the operation waiting time of each appliance by taking into account the consumer's lifestyle as well as environment/social factors [46,47]. [PERSON] et al. investigated maximum-saving behavior as a response to the summer residential critical peak price (CPP) in an apartment complex in Japan [48]. However, the concept of an IBP requires consumers to contribute voluntarily to a pattern in which the system operator directly turns off some appliances to reduce consumer energy usage during peak time [49]. Figure 1: A cyber-physical system in a smart building (adapted from [37]). A direct load control (DLC) program aims to minimize energy demand by combining the flexibility from shared energy storage systems and the DLC-based demand response without decreasing customer satisfaction [50; 51]. The authors in [52] studied the effect of implementing interruptible/curtailable (I/C) service in different modes on operation cost, customer profit, load factor, and peak reduction on the micro-grid, taking into consideration various uncertainties. The impact of flexibility options, which are offered in the capacity market (CM) program in a hybrid electricity market, was addressed by [53]. A comparison between the different DRPs based on their time resolution, response type, and methods is given in Table 1. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type of DR** & **Definition** & **Time Resolution** & **Response Type** & **Algorithm** \\ \hline \multirow{6}{*}{\begin{tabular}{c} Real-time \\ pricing (RTP) \\ \end{tabular} } & The electricity tariffs are & & & \\  & changed and announced & & & Mixed integer linear \\  & on a day-ahead or & Hourly & Customer side & programming \\  & hour-ahead basis before & & & (MILP) \\  & each period. & & & \\ \hline \multirow{6}{*}{\
\begin{tabular}{c} **Time-Based** \\ **Rate Programs** \\ \end{tabular} } & A day can be divided into & & & \\  & three intervals under a & & & \\  & & TOU pricing scheme, & & \\  & time of use & including peak, mid-peak & & \\  & (TOU) & and off-peak intervals; & & \\  & consumers often try to & & & \\  & shift the shiftable loads to & & & \\  & the off-peak interval. & & & \\ \hline \multirow{6}{*}{
\begin{tabular}{c} Critical peak \\ price (CPP) \\ \end{tabular} } & CPP operates similarly to & & & Difference-in- \\  & the TOU program, except & & & difference (DID) \\ \cline{1-1}  & for the time in which the & & & method/Fixed-effects \\ \cline{1-1}  & power reliability & & & logistic regression \\ \cline{1-1}  & is endangered. & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison between different DPRs in smart buildings. Figure 2: Classification of demand response programs (adopted from [54]).
# Application of Fly Ash as an Adsorbent for Removal of Air and Water Pollutants
## 4 Application of Fly Ash for Treatment of Pollutants
### Removing Water Pollutants
#### 4.2.1 Removal of Heavy Metals
The mechanism of modified FA for treatment of heavy metal wastewater can be summarized as follows: most FA is alkaline with a high adsorption capacity and high surface area, and negative charge accumulates at the FA surface in alkaline solutions. FA can be expected to remove some metal ions from wastewater by electrostatic interactions or precipitation-adsorption. The ion exchange capacity, high surface area, and unique pore characteristics of modified FA play an important role for treatment of heavy metal wastewater [15; 139]. \begin{table}
\begin{tabular}{|p{85.4 pt}|p{85.4 pt}|p{85.4 pt}|p{85.4 pt}|} \hline
**Metal** & **Target Organs** & **Primary Sources** & **Clinical Effects** \\ \hline As & Pulmonary nervous system, skin & Industrial dusts, medicinal uses of polluted water & Perforation of nasal septum, respiratory cancer, peripheral neuropathy: dermatomes, skin cancer \\ \hline Cd & Renal, skeletal, pulmonary & Industrial dust and fumes and polluted water and food & Proteinuria, glucosuria, osteeomalacia, aminacoduria, emphysema \\ \hline Cr & Pulmonary & Industrial dust and fumes and polluted food & Ulcer, perforation of nasal septum, respiratory cancer \\ \hline Mn & Nervous system & Industrial dust and fumes & Central and peripheral neuropaths \\ \hline Pb & Nervous system, hematopoietic system, renal & Industrial dust and fumes and polluted food & Encephalopathy, peripheral neuropathy, disorders, anemia \\ \hline Ni & Pulmonary, skin & Industrial dust, aerosols & Cancer, dermatitis \\ \hline Sn & Nervous system, pulmonary & Medicinal uses, industrial dusts & Central nervous system disorders, visual defects and EEG changes, pneumoconiosis \\ \hline Hg & Nervous system, renal & Industrial dust and fumes and polluted water and food & Proteinuria \\ \hline \end{tabular}
\end{table} 
Table 5: Primary sources of common heavy metals and their effects on human organs (reproduced with permission from [143], Elsevier, 2017).
# Video Super Resolution Using a Selective Edge Aggregation Network
## 3 Method
### Loss Function  
The edge map of \(SR_{t}\) during Stage 1 training is incomplete compared to the edge map of GT frame \(SR_{GT}\). Therefore, our model learns to minimize the difference of gradient magnitude between \(SR_{t}\) and \(SR_{GT}\).  
We adopt a Sobel mask as an edge detector within magnitude loss \(Loss_{m}\). \(m_{o}\) and \(m_{t}\) are gradient magnitude of \(SR_{t}\) and \(SR_{GT}\), respectively, and \(Loss_{m}\) is defined as follows.  
\[Loss_{m}=\sum_{i}^{H}\sum_{j}^{W}\lvert m_{o}(i,j)-m_{t}(i,j)\rvert \tag{7}\]  
We also adopt L1 Loss \(Loss_{L1}\) in Stage 1, and the the Stage 1 Loss \(Loss_{stage1}\) is formed as follows. where \(\alpha\) is a coefficient to balance the two terms.  
\[Loss_{stage1}=Loss_{L1}+\alpha\times Loss_{m} \tag{8}\]  
In Stage 2, we propose content loss, style loss, and reconstruction loss additionally, except for the existing L1 loss and magnitude loss between refinement learning, and perform optimal learning by learning the parameters together in front of each added loss formula.  
First of all, \(L_{content}\), the content loss, refers to the two images we want to compare with \(p\) and \(x\), respectively, and calculates each feature map to determine the \(p\) feature representation as \(P_{l}\) and the feature representation of \(x\) as \(F_{l}\). It is the square of the Frobenius norm for the difference.  
\[L_{content}(p,x,l)=\frac{1}{2}\sum(F_{l}-P_{l})^{2} \tag{9}\]  
In this case, content reconstruction can be solved by finding \(x\) that minimizes the loss using the feature map of the deep layer where only the in-depth information remains. \(L_{style}\), the style loss, is the sum of multiplying the weights after finding the Gram matrix for the feature map and squaring the Frobenius norm. The reason why the Gram matrix is used here is that style defines the correlation between feature maps. Here too, if the two images to be compared are \(a\) and \(x\), respectively, the values obtained by the Gram matrix for each feature map can be called \(A_{l}\) and \(G_{l}\), and the effect \(E_{l}\) of the layer on the total loss is defined as Equation (10).  
\[E_{l}=\frac{1}{4{N_{l}^{2}}{M_{l}^{2}}}\sum(G_{l}-A_{l})^{2} \tag{10}\]  
\[L_{style}(a,x)=\sum(w_{l}-E_{l}) \tag{11}\]  
Therefore, the final \(L_{style}\) using \(E_{l}\) is the same as Equation (11), and style reconstruction can be solved by finding \(x\) that minimizes loss. The reconstruction loss, \(L_{rec}\) calculates the difference between the actual value and the predicted value without taking the softmax in the cross-entropy loss.  
\[L_{Stage2}=\beta\times L_{content}+\gamma\times L_{style}+\delta\times L_{rec} \tag{12}\]  
Finally, the Stage 2 loss to be minimized by using all three losses we propose is the same as Equation (12), and each given beta, gamma, and delta is a factor that adjusts the reflection weight of each loss function. In this case, these arguments are self-learned.