esa-sceva/satcom-chunk-collection huggingface dataset:
# The Design Value for Recycling End-of-Life Photovoltaic Panels
## 1 Introduction
The biggest challenge is recovering materials from decommissioned devices. Therefore, we should try to design and manufacture devices that do not break and that can carry out their function efficiently for as long as possible, which is a central aspect. To date, PV panels have a lifespan of 25-30 years. In recent years, we have been working to efficiently recover the materials of solar panels at the end of their life cycle to ensure convenient investment in recycling plants. The producers, in fact, think in terms of profitability and not recyclability. It is, therefore, necessary to make recycling economically advantageous or, at least, to encourage it with government contributions. To date, silicon-based panels are the most common but only for reasons of producer profitability. In Europe, the process to achieve complete recycling of EoL products has already started. With Directive 2008/98/EC on waste, the EU has entrusted the producer itself with responsibility for its EoL panels, allowing the producer to include in the price initial costs for the EoL treatment. In 2012, Directive 2012/19/EU introduced the first regulation on disposal and recycling, opening the doors to different financing models for the separate collection of PV panels. In this research, the authors analyze data on the durability, recyclability rates, different possible design layouts and materials used in the design and manufacture of PV panels. The analyzed data were provided by the _Laboratory of Renewable Energy, Embedded System and Information Processing_, National School of Applied Sciences, Mohammed First University, Oujda 60000, Morocco. Through analytical evaluations and numerical simulations (Dfr and DfD approach), the study identifies the optimal materials, the best geometries and geometric proportions and the most convenient geometric and dimensional tolerances in the couplings between the layers and the components that comprise the panels with the aim of achieving the most current, efficient and effective solutions for recycling EoL PV panels and for longer durability. Finally, a logical scheme is proposed that allows the total recycling of materials at the end of their life (EoL) for panels designed and built in the optimized way. The paper is organized as follows. Section 2 describes the opportunities and challenges for PV panel recycling. In Section 3, after a preliminary description of the structure and materials that constitute PV commercial panels, an effective design method for recycling and incrementing durability is presented, and a logical scheme that allows the total recycling of materials at the EoL is proposed. In Section 4, a numerical analysis of the proposed method is carried out, and the obtained results are discussed. Finally, in Section 5, the conclusions are shown. Figure 3 shows all the main steps of the present research. Figure 2: Growth of waste materials from renewable resources.
## 5 Conclusions  
Spatial distribution of SOM showed a moderate variability in Kenli County, which is located in the Yellow River Delta, and both the spatial-prediction accuracy and spatial-distribution characteristics of SOM were influenced by the number of sampling points. The mean value of the SOM content in the study area was 10.96 g/kg, with a lower-middle fertility level. In terms of spatial variability, soils with SOM content of 9 g/kg-12 g/kg occupied most of the study area; soils with SOM content <9 g/kg were distributed in the southern part of Kenli County with the shape of spots, and soils with SOM content of 12 g/kg-14 g/kg were mainly distributed in the middle and northern parts of the study area. With the decrease in sampling-point numbers, the spatial variability characteristics of SOM showed the phenomenon of gradually weakened detail characterization, information loss and wrong description. From the independent verification results of the predicted values of SOM under different sampling numbers, the RMSE value did not have obvious regularity with the changes in the sampling numbers. Both the ME and ASE showed an obvious inflection point when the number of samples was 150, and remained at about 0 and 4, respectively, as the sampling numbers increased.  
Combined with the independent verification index and spatial-distribution characteristics, when predicting the spatial distribution of SOM in Kenli County, the rational sample points to ensure the spatial prediction accuracy of SOM numbered 150. It was equivalent to 107 sample points per 1000 km\({}^{2}\).  
Conceptualization, X.Z.; methodology, X.Z. and W.W.; software, M.D. and W.W.; validation, M.D.; formal analysis, X.Z., W.W. and M.D.; investigation, X.Z., X.S., X.L. and D.C.; resources, X.Z.; data curation, X.Z., W.W. and M.D.; writing--original draft preparation, W.W.; writing--review and editing, X.Z., M.D., W.W. and X.S.; visualization, M.D.; supervision, X.Z. and X.L.; project administration, X.Z.; funding acquisition, X.Z., X.S., X.L. and D.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China [grant number 2021 YFD1900900]; the Key Research and Development Program of Shandong Province, China [grant number 2021 CXGC010801, 2021 CXGC010804]; Shandong Province Modern Agricultural Industry Technology System Cotton Post Innovation Team [grant number SDAIT-03-06]; the Talent Fund of Qingdao Agricultural University, China [grant number 1114344].  
Not applicable.  
The authors acknowledge all the funds that provided funding during the writing process of the paper.  
The authors declare no conflict of interest.
# Energy Trading Strategy for Heat and Electricity-Coupled Microgrid Based on Cooperative Game
## 3 Model of Energy Consumer  
### Model of Load Demand  
The model of electric load demand is described as follows:  
\[e_{i}^{h}=f_{i}^{lh}+s_{i}^{lh},i\in[1,2,\ldots,N],h\in[1,2,\ldots,H] \tag{1}\]  
\[\left\{\begin{array}{cc}s_{i}^{\text{mmin}}{\leq}s_{i}^{lh}\leq s_{i}^{ \text{max}},&h\in[\tau_{i},\sigma_{i}]\\ s_{i}^{lh}=0,&h\
otin[\tau_{i},\sigma_{i}]\end{array}\right. \tag{2}\]  
\[\sum_{h=1}^{H}e_{i}^{lh}=eQ_{i} \tag{3}\]  
Figure 1: System Framework.  
where \(f^{lh}_{i}\), \(el^{lh}_{i}\) and \(sl^{lh}_{i}\) are total electric load, fixed electric load, and shiftable electric load and of user \(i\) in time slot, respectively. \(N\) is the number of users, \(H\) is the length of operation period. \(sl^{\text{min}}_{i}\) and \(sl^{\text{max}}_{i}\) are the lower limit and upper limit of shiftable electric load. \([\tau_{i},\sigma_{i}]\) is the feasible operation periods of shiftable electric load. \(eQ_{i}\) is the sum of the electric load demand of user \(i\) in the whole operation period.  
The model of thermal demand is defined as follows:  
\[tl^{h}_{i}=hl^{h}_{i}-\Delta hl^{h}_{i} \tag{4}\]  
\[0\leq\Delta hl^{h}_{i}\leq\Delta hl^{\text{max}}_{i} \tag{5}\]  
where \(tl^{h}_{i}\), \(hl^{h}_{i}\) and \(\Delta hl^{h}_{i}\) are the optimized thermal load, original thermal load, and cuttable thermal load of user \(i\) in time slot \(h\). \(\Delta hl^{\text{max}}_{i}\) is the upper limit of \(\Delta hl^{h}_{i}\).
# Flooding Message Mitigation of Wireless Content Centric Networking for Last-Mile Smart-Grid +
## 3 Proposed Scheme
### Expected Hop Based Flooding Control of the CCN Packet  
Generally, CCN packets are flooded to the entire network using FIB and PIT information for reliable content requests and deliveries. However, the unlimited packet flooding induces high network congestion, which is critical in wireless SG environments. To solve this problem, hop distance between content provider and consumer is utilized. Expected hop distance between the packet sender and the destination such as content provider and consumer is measured by multi-hop CA messages from the PMPR. When a device receives a multi-hop CA message from its neighbor(s), the receiver device updates the content prefix, outgoing face, and the expected HOP count information in the FIB table. Thus, the expected hop attribute of the interest and content packets can be filled using expected hop information in the FIB. If the device receives an interest or content packet from its neighbor, then it compares the expected hop count value between FIB and received packet. If the device has a same or lower expected hop count, packet can be flooded into the network. When the expected hop count of the packet is lower than its own hop count, the packet is discarded to limit flooding.
# Accurate Sybil Attack Detection Based on Fine-Grained Physical Channel Information
## 7 Experimental Evaluation  
Our experimental environment is a 6.35 m \(\times\) 8.5 m meeting room. Receivers and transmitters are mini-PCs equipped with Intel 5300 WiFi NICs. They all contain three antennas. We use one antenna at the transmitter and three antennas at the receiver. We only employed one receiver. We installed CSI tools [46] on these mini-PCs, which can receive the CSI of 30 subcarriers. In order to eliminate the interference in the environment, while further reducing the impact of phase offsets, we use the monitor mode and 5.32-GHz band. Our system can not only work in the 5-GHz band, but also in the 2.4-GHz band. We only need the centre AP equipment with WiFi NICs, which can receive CSI signals. Regardless of whether sending any type of WiFi device and any type of packet, we can receive the CSI when it accesses the wireless network and sends it to the transmitter. We use the same experimental environment for both dynamic and static detection algorithms.  
We conducted a total of five sets of experiments on Sybil attack detection from static and dynamic scenarios, respectively. Three of them are static attack detection experiments, which are respectively the AoA accuracy comparison of different algorithms, the AoA error estimation of different packets and access points and the detection accuracy of different types of Sybil nodes in a static scenario. Dynamic attack detection experiments include two sets, that is the accuracy of the DBSCAN clustering algorithm and the detection accuracy of Sybil nodes.  
In detail, we test an AP and mobile phone, which support the 802.11a/c protocol, as the AoA of the transmitter and use the mini-PC with the Intel 5300 network card as the receiver. In the static detection algorithm, the AoA value is set according to the experimental threshold, and the RSSI and amplitude detection threshold are combined to verify the efficiency of our Sybil node detection algorithm. We then use MATLAB simulation to increase the number of Sybil attacks compared to the RSSI attack detection methods. When applying dynamic detection algorithms, we let the client implement a variety of motions and verify the accuracy of the DBSCAN algorithm separately. Then, we increase the number of movement attack nodes and verify that the algorithm is still able to effectively detect when there are many kinds of movement patterns in the environment.
## 3 Proposed Algorithm
### Feature Fusion and Anomaly Detection  
At this stage, the features extracted from hand patches by the VAE and the body pose landmarks are represented as feature vectors \(\vec{v}\) and \(\vec{l}\), respectively. To integrate these feature vectors, we employed an early fusion [49] mechanism that creates a unified representation, combining the information extracted from both the hand patches and HPE. This mechanism achieves fusion by concatenating the feature vectors into a single unified feature vector. Subsequently, an isolation forest classifier (IFC) [50] model is trained using normal feature vectors derived from frames without a gun and involving normal actions to capture the correlations and interactions between features from both sources. For the implementation of IFC, the default settings were used, with the contamination parameter set to \"auto\" and the number of base estimators in the ensemble fixed at 40 across all experiments. Given the feature vectors \(\vec{v}\) and \(\vec{l}\), which contain two types of features, the final OCC model's prediction, denoted as \(h\), can be expressed as follows:  
\[p=h\Big{(}[\vec{v},\vec{l}]\Big{)}=\Big{(}[v_{1},v_{2}\ldots v_{K},l_{1},l_{2} \ldots l_{N}]\Big{)} \tag{8}\]  
where \(K\) and \(N\) represents the number of features extracted by the VAE and HPE, respectively. Regarding HPE, we consider ten body landmarks related to the positioning of the left and right arms including hands. These include _left_wrist_, _left_pinky_, _left_index_, _right_thumb_, _left_elbow_, _right_wrist_, _right_pinky_, _right_index_, _right_thumb_, and _right_elbow_, which are all crucial for accurate dangerous pose prediction. Similarly, eight deep features are extracted from the VAE after dimensionality reduction [51], corresponding to the hands, with or without a gun in either hand. Therefore, a total of twenty eight features are used to train the IFC model for classification for firearm prediction. Based on the anomaly score, samples containing a _hundgun_ and a dangerous pose are assigned a score of \"1\", indicating an anomaly, while samples without a gun and in a normal pose receive a score of \"0\", signifying normal behavior. The ROC curve of the proposed DeepGun method implemented using IFC, support-vector machine [52], Gaussian mixture models (GMM) [53], and minimum covariance determinant estimator (MCDE) [54] is shown in Figure 9. For fair comparison, all OCC implementations use the default parameter settings of IFC, SVM, MCDE, and GMM as provided by the scikit-learn library. In Figure 9, the DeepGun implementation  
Figure 8: Training and validation loss of VAE trained on the normal ‘_no gun_’ dataset for visual gun feature extraction.  
using IFC exhibits the largest area under ROC, indicating the best average performance compared to other implementations, including SVM, GMM, and MCDE.
## 4 Discussion
The results of research regarding rainfall estimates in Indonesia found several problems, including the uneven distribution of rain gauges even though they function as actual rain gauges and are often used as verification tools, limited coverage, and suitability of the use of radar types for different regional conditions, corrections to satellite data and inaccuracies in statistical methods in dealing with variations in rain patterns. The development of machine learning in the world has been very rapid, providing more accurate rainfall estimates, but it has yet to be widely applied in Indonesia. Optimizing data integration methods from various sources has also been widely carried out globally, but it is only sometimes optimal when applied to rain patterns in Indonesia. The current challenge for estimating rainfall in Indonesia is how to optimize the integration of data from various existing sources, namely rain gauges, weather radar, and satellites, so that they can complement one another, taking into account the differences in rainfall patterns in Indonesia, namely, seasonal, equatorial, and local patterns. From all the reviews, we can provide several recommendations regarding rainfall estimates. Place rain gauges more evenly in an area. The use of mobile technology can be a solution to fill gaps in distribution. Next, consider using a weather radar that suits the area's characteristics, correcting and calibrating satellite data with ground-truthing data. With limited data sources, combining rain gauge, radar, and weather satellite data must be used for better rainfall estimation results. Finally, develop and apply more adaptive statistical methods or, at the same time, adopt machine learning technology, which has proven to be more successful. Table 3 describes the summary of the review results of estimating rainfall in Indonesia.
## 3 Proposed Intelligent Weighting Factors Design Approach
### Proposed Weighting Factor Tuning Scheme Based on DRL
#### 3.2.5 Learning Scheme
For each episode, the system, i.e., the SPMSM controlled in a rotation speed closed loop of Figure 3, starts with the same given initial conditions and parameters from Table 3. At each time step \(k\leq N_{\text{steps}}\) of the episode, the agent receives the current value of the state of the system \(s(k)\) and applies the action \(a(k)=\pi_{\phi}(k)\) to the environment using the current value of the parameter vector \(\phi\). This action is used as the weighting factors of the FCS-MPCC decision function (11) so that the correct gating pulses can be selected to be applied to the VSI. Using them, the system evolves to the next time step. The resulting reward \(\rho(s(k),a(k))\) and next state \(s(k+1)\) are stored in the replay buffer as the 4-tuple \((s(k),a(k),\rho(s(k),a(k)),s(k+1))\). This process is run as long as the number of 4-tuples in the replay buffer \(\mathcal{D}\) is below a given threshold \(\text{Card}(\mathcal{B})\). Whenever this threshold is reached, an update of the parameter vectors \(\phi\), \(\chi_{1}\), \(\chi_{2}\), \(\tilde{\chi}_{1}\), and \(\tilde{\chi}_{2}\) is performed according to the procedure described in Section 3.2.2. The only difference that can be seen in Algorithm 1 is that the Bellmanregression target Y and the actor loss \(J_{\pi}(\phi)\) are computed using the minimum of both target critics \(Q_{\xi_{1}}\) and \(Q_{\xi_{2}}\). As explained before, this stabilizes the learning process. At the same time, the losses \(J_{\pi}(\phi)\), \(J_{Q}(\chi_{i})\), with \(i\in\{1,2\}\), and Y are not directly calculated from the current values of the state and action as was presented in Section 3.2.2, but as an average of their values on past experiences to improve the learning process. This is accomplished by sampling tuples from the replay buffer \(\mathcal{D}\) into a so-called mini-batch \(\mathcal{B}\). The gradients used to update \(\phi\), \(\chi_{1}\), and \(\chi_{2}\) are calculated using deep deterministic policy gradient [37]. To summarize key elements of Section 3, Table 5 provides a qualitative comparison of the DRL method we propose with the MOGA-NN one on some crucial aspects for real-life implementation of the methods.
# Green Approach in Water-Based Drilling Mud Design to Increase Wellbore Stability
## 5 Conclusions  
On the basis of laboratory tests, the following conclusions can be drawn:  
* MPP added to water-based mud reduces API filtration, PPT filtration, spurt loss, and pellet swelling regardless of particle size and concentration;
* the best results were obtained by adding MPP with particles from 0.10 to 0.16 mm at a concentration of 2% by volume of water;
* a 44% reduction in API filtration was achieved with mud B4 containing MPP particles from 0.10 to 0.16 mm at a concentration of 2% by volume of water;
* a 61.54% reduction in PPT filtration through a 0.75 \(\upmu\)m\({}^{2}\) (750 mD) ceramic disk was achieved with mud B4 containing MPP particles from 0.10 to 0.16 mm at a concentration of 2% by volume of water;
* a 53.85% reduction in PPT filtration through a 0.4 \(\upmu\)m\({}^{2}\) (400 mD) ceramic disk was achieved with mud B4 containing MPP particles from 0.10 to 0.16 mm at a concentration of 2% by volume of water;
* a 45% reduction in pellet swelling after 24 h was measured with mud B4 containing MPP particles from 0.10 to 0.16 mm at a concentration of 2% by volume of water at room temperature;
* a 48.6% reduction in pellet swelling after 24 h was obtained with mud B4 containing MPP particles from 0.10 to 0.16 mm added at a concentration of 2% by volume of water at 90 \({}^{\circ}\)C;
* satisfactory results were obtained up to a MPP concentration of 1% by volume of water;
* the swelling reduction results correlated very well with the API filtration reduction results.  
In general, it can be concluded that the addition of MPP to the drilling mud can increase wellbore stability. However, further measurements of the properties of the drilling mud are necessary to determine the optimal concentration that ensures adequate mud properties for the safe drilling of the well.  
**Author Contributions:** [PERSON], created the idea for the paper, provided laboratory testing, wrote the draft version of paper, and the discussion and conclusion sections; [PERSON]-M., wrote the results section and reviewed the whole paper; [PERSON], prepared the introduction section and tables; [PERSON], provided laboratory testing, and prepared the introduction section and figures. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The dissemination process is financially supported by the University of Zagreb within the project \"Circular economy in petroleum engineering\" (In Croatian: Kruzna ekonomija u nafntnom inzenjerstvu--KENI).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data available in a publicly accessible repository.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Evaluating and Ranking SCPMS Enablers Using ISM and SWARA
## 5 Discussion
SCPMS enablers may affect each other or may be influenced by another guiding SCPMS enabler, thus control depending on SCPMS enablers needs attention. Controlling SCPMS enablers is only possible by understanding their influence. As a result, resource allocation and optimization can become a simple process. The digraph revealed that'management readiness', 'digital readiness', and 'employee readiness' play a significant role in SCPMS. The results are consistent with earlier studies asserting the importance of each in supply chain 4.0 for I4.0 [6]. In the case of intelligent SCPMS, the process integration capability is enhanced through integrating information flow, financial flow, and physical flow of goods to enhance the firm performance. Such intelligent SCPMS can enhance revenue, customer relationship, and operational excellence by understanding firm size and consumer demand predictability [69; 70]. The IoT, cloud computing, and big data analytics provide speed, agility, and flexibility in intelligent SCPMS for dynamic decision making [6]. SCPMS enablers were further divided into four clusters of dependent, independent, autonomous, and linked categories using MICMAC analysis. It was possible to look at each SCPMS enabler using MICMAC analysis. On reviewing the MICMAC analysis of the SCPMS enablers, it was found that there is no enabler classified in the autonomous group. The results of this investigation showed the stability of the SCPMS enablers. There was no SCPMS enabler in the autonomous group. Offering preventative measures before the implementation of an SCPMS can further assist practicing managers. Analyzing the independent SCPMS enablers, managers may take proactive control actions through knowledge creation, infrastructure planning, and technology development to accommodate new Wi-Fi-enabled technologies such as IoT, RFID, cloud computing, and resource allocation, which may be planned. According to the SWARA results, 'top management support (E9)', 'SC 4.0 performance measurement awareness (E7)','managerial readiness (E6)', 'employee readiness (E12)', 'technology for 14.0 (E13), 'digital readiness (E11)', and 'continuous improvement through innovation (E14)' had a higher weight and constituted the top half of the ranking. On the other hand, 'intelligent SCPMS for industry 4.0 (E3)', 'information transparency (E4)', 'customization for sustainability (E5)', 'green sourcing (E10)', 'visibility (E12)', 'leagility (E1)', and'supply chain admonition (E8)' had a comparatively lower weight.