esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
The only model that is considered correct is Wang-Petit, as it is the only one that considers calendar aging, which has great importance in standalone systems, with very low charge/discharge rates. The results were not verified, as extensive time and resources would have been needed to conduct Li-ion aging tests and to verify the aging models. In this work, we wanted to estimate the lifetime of the battery using several available models. To obtain the tuning parameters of the different models, Li-ion batteries should be tested under similar conditions as the working conditions, during several years, as working conditions in stand-alone PV systems are low current (usually lower than C/20) and take many hours during the day at floating stage. This was not possible if we wanted to make the prediction now, so the available models were used. Usually, researchers and engineers use the equivalent full cycles model, but the results show that in many cases (most of the typical stand-alone PV systems) it leads to overestimation of the battery lifetime.
# Systematic Review of Anomaly Detection in Hyperspectral Remote Sensing Applications
## 2 Part A: Bibliometric Analysis
### Authors Analysis
The respective frequency (for the specific year) is displayed on a vertical axis in a logarithmic scale, and as can be seen, no trivial keywords were omitted, nor was any manual filtering performed. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Document** & **Reference** & **Local Citations** \\ \hline [PERSON]; [PERSON] Adaptive Multiple-Band CFAR Detection of an & & \\ Optical Pattern with Unknown Spectral Distribution. IEEE Trans. & & \\ Acoust. Speech Sign. Proces. 1990, 38, 1760-1770, & [34] & 90 \\ doi:10.1109/29.60107. & & \\ \hline [PERSON] A cluster-based approach for detecting man-made & & \\ objects and changes in imagery. IEEE Trans Geosci Remote Sens 2005, & [35] & 32 \\
43, 374-387, doi:10.1109/TGRS.2004.841481 & & \\ \hline [PERSON]; [PERSON] Detection algorithms for hyperspectral & & \\ imaging applications. IEEE Signal Process Mag 2002, 19, 29–43, & [9] & 32 \\ doi:10.1109/79.974724. & & \\ \hline [PERSON] Hyperspectral target detection: An overview of & & \\ current and future challenges. IEEE Signal Process Mag 2014, 31, & [18] & 21 \\
34–44, doi:10.1109/MSP.2013.2278992 & & \\ \hline [PERSON]; [PERSON] Hyperspectral Image Classification and & & \\ Dimensionality Reduction: An Orthogonal Subspace Projection & & \\ Approach. IEEE Trans Geosci Remote Sens 1994, 32, 779–785, & [36] & 19 \\ doi:10.1109/36.298007 & & \\ \hline [PERSON] Receiver operating characteristic curve confidence & & \\ intervals and regions. IEEE Geoscience and Remote Sensing Letters & [37] & 17 \\
2008, 5, 251–255, doi:10.1109/lgrs.2008.915928. & & \\ \hline [PERSON]; [PERSON]; [PERSON] Hyperspectral Image & & \\ Processing for Automatic Target Detection Applications. Lincoln & [38] & 16 \\ laboratory journal 2003, 14, 79–116 & & \\ \hline Nasrabadi, N.M. Regularization for spectral matched filter and RX & & \\ anomaly detector. In Proceedings of Proc SPIE Int Soc Opt Eng, 2008 & [39] & 16 \\ \hline [PERSON]; [PERSON]; [PERSON] Sparse Representation for & & \\ Target Detection in Hyperspectral Imagery. _IEEE J. Sel. Top. Signal_ & [40] & 16 \\ _Process._ 2011, 5, 629–640, doi:10.1109/jstsp.2011.2113170 & & \\ \hline [PERSON]; [PERSON]; [PERSON], [PERSON] Hyperspectral Image & & \\ Classification Using Dictionary-Based Sparse Representation. IEEE & & \\ Trans Geosci Remote Sens 2011, 49, 3973–3985, & [41] & 16 \\ doi:10.1109/tgrs.2011.2129595. & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The top 10 documents with the highest number of local citations that were not selected within the applied search strategy (Figure 1).
## 5 Conclusions  
This paper reports a comparison between two word-embedding methods in an Arabic sentiment analysis dataset (ASAD). In general, fastText provides a better vector for representing and recognizing sentiment in Arabic sentences. The accuracy achieved on sentences pre-processed with fastText was better than on those pre-processed with GloVe. Secondly, stacking the LSTM layers led to better accuracy, but, at the same time, decreased the speed of training and testing. In our experiment on the ASAD dataset, the best achievement was recorded using fastText with triple-layer LSTM, with accuracies of 96.7% and 90.9% for training and testing, respectively. Training and testing speed is significantly decreased when using the triple-layer LSTM architecture. The average testing time for a single sentence in a consumer PC is about 0.75 miliseconds. Sentiment analysis is still an interesting topic, since it varies between languages. This research provides an analysis of how the thicker LSTM layers improve the recognition rate and maintain relatively high implementation speeds, even in consumer PCs. Future research questions relate to the impact of thicker LSTM networks on accuracy and computational time. Experimenting with different bidirectional LSTM architectures is also interesting in order to see its impact on performance, since bi-LSTM has the ability to consider the impact of a word on previous words in a sentence as well as future words. With respect to the word embedding methods, comparing state-of-the-art embedding methods such as Word2 vec and BERT will lead to a better understanding of acceptable word embedding techniques for the Arabic language. Observing the cross-language impact of word embedding methods and their impact on the performance of various classifiers is an interesting topic for determining feasible frameworks under various implementation conditions.  
Conceptualization, A.S. and F.A.; methodology A.S. and A.L.; software, M.H., A.L., I.O. and L.K.; investigation, A.S. and K.; writing--original draft preparation A.S., A.A. and N.A.; data curation, M.A. and F.A.; visualization, A.L.; validation, K.; supervision, K.; project administration, M.H.; funding acquisition, F.A.; resources, F.A. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University, grant number RG-21-51-01 and The APC was funded by Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University.  
Not applicable.  
The authors used the publicly available dataset called Arabic Sentiment Analyis Dataset (ASAD). We provide the research results data and the code available for public.  
The authors are grateful to LPPM Universitas Amikom Yogyakarta for their administrative support. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan V GPU used for this research.  
The authors declare no conflict of interest.
## 2 Macro- and Micro-Encapsulated PCMs  
The encapsulated PCMs consist of the core material, i.e., the PCM, and an organic or inorganic shell material. Depending on their size, PCMs can be characterized as nano-encapsulated when their size is less than 1 \(\upmu\)m, micro-encapsulated when their size is between 1 and 1000 \(\upmu\)m, and macro-encapsulated when their size is bigger than 1000 \(\upmu\)m. Four articles have been published in this Special Issue related to the synthesis and characterization of macro- and micro-encapsulated PCMs. The micro-encapsulation of sodium nitrate (NaNO\({}_{3}\)) in zinc oxide shells as PCM for high temperature latent heat storage was investigated by [PERSON] et al. [1]. The aim of their work was to evaluate the potential of NaNO\({}_{3}\)-ZnO microcapsules with respect to thermal energy storage applications together with cost reduction related to metal corrosion in installations and storage tanks. Charge/self-discharge experiments revealed that the NaNO\({}_{3}\) content of the microcapsules influences the amount of thermal energy stored in the corresponding interval. Moreover, it was proved that prototype-scale thermal energy storage in microcapsules can be predicted from laboratory measurements on small material samples.  
In the study of [PERSON] et al. [2] micro-encapsulated PCMs were fabricated based on a core of paraffin Rubitherm(r) RT27 and a shell that consists of ethyl-vinyl acetate and low density polyethylene. The goal of this work was the production of polyurethane elastomeric materials demonstrating thermoregulating properties due to the incorporation of the aforementioned micro-encapsulated PCMs. The obtained results revealed that the mechanical properties and the density of the synthesized elastomeric materials together with the thermal synergy effects were related to the wt% content of the micro-encapsulated PCMs in order improved structural stability and enhanced latent heat to be demonstrated.  
In the work of [PERSON] et al. [3] the storage capacities of PCMs emulsions based on even- and odd-numbered paraffins with different purities are investigated, taking into account the corresponding supercooling effect and phase transition behavior. The obtained results revealed that the nucleation agent and the emulsified PCM influence both the storage and supercooling factors of the PCM emulsions. It was observed that the PCM's hydrocarbon chain and the purity of the materials affected the degree of supercooling.
## 3 Role of Small RNA in Stress Response
### MsrI and Mcr11  
MrsI (ncRv11846) is a 100-nt sRNA identified in _Mycobacterium_ sp. [144]. MrsI is regulated by the iron-dependent transcription factor IdeR, in both _M. tuberculosis_ and _M. smgmatis,_ driving its expression under iron-limiting conditions. High-throughput sequencing and differential expression studies identified MrsI as significantly upregulated during iron deprivation and membrane stress. Additionally, the CRISPR-mediated knockdown of MrsI further confirmed its regulatory role in iron homeostasis, with 118 genes upregulated including bacteriitin, hydrogenase maturation factor (HypF), and ferredoxin reductase (FprA). Functionally, MrsI represses _bfrA_ mRNA, encoding bacteriocinrin, an essential iron storage protein [144]. This repression is mediated by a conserved six-nucleotide seed sequence and a seven-nucleotide apical loop. Collectively, these findings highlight MrsI as a critical regulator of iron metabolism, essential for Mtb adaptation to iron-limiting environments, akin to RyhB in _Escherichia coli_.  
Another sRNA, Mcr11, in Mtb, plays a crucial role in stress responses during slow growth and chronic infection. It is regulated by the transcription factor AbmR [145] by a mechanism that involves slowing down the transcribing RNA polymerase by binding to both RNAP and the DNA, similar to that observed for Rv1222 [146]. Bioinformatic predictions and qRT-PCR analyses identified _Rv3282_, _fadA3_, and _lipB_ as direct targets of Mcr11, with base pairing sites located immediately upstream of these genes. Notably, _lipB_expression was significantly de-repressed in Mcr11-deficient Mtb, confirming its regulatory role. Furthermore, Mcr11-mediated gene regulation was influenced by the presence of fatty acids, linking it to central metabolism. These findings highlight the importance of Mcr11 in Mtb's adaptation and underscore the need to further explore sRNA stability and function in tuberculosis pathogenesis.
## 3 Results and Discussion
### GC-IMS Analysis of Volatile Profile of Dried Citrus Peel as Effected by Drying Methods
#### 3.3.3 Esters, Ketones, Acids, and Others  
Esters, usually of fruity or floral smell [44], are the main components contributing to citrus head scent; and ethyl acetate and ethyl butyrate were often considered as quality indexes of citrus aroma [46]. For esters detected in GC-IMS analysis, although different esters were detected as compared with GC-MS analysis, FD was always the best way to preserve esters.  
Ketones and acids were undetected in GC-MS analysis, while 11 ketones and 3 acids were detected in GC-IMS analysis (Figure 5d,f). Ketones impart the odor of butter, cream, and ether. It can be found from Figure 5d that AD was unfavorable to preserve most ketones, including 2,3-butanedione, 2-hexanone, acetophenone, cyclohexanone, and 6-methyl-5-hepten-2-one, which were higher in dried Chachi peel than in dried Ponkan peel. While hydroxyacetone were higher in AD peel since hydroxyacetone was formed through pyrolysis of carbohydrate at high temperature during the AD process [47]. Among the three detected acids (Figure 5f), 2-methylbutanoic acid (cheese, sweat [44]) was higher in GSD/GFD than other dried peels. Overall, SD could preserve ketones (2,3-butanedione, 2-hexanone, acetophenone, cyclohexanone and 6-methyl-5-hepten-2-one) and 2-methylbutanoic acid as FD, and dried Chachi peel possessed higher concentration of these ketones and acid than dried Ponkan peel. Thus, ketones (2,3-butanedione, 2-hexanone, acetophenone, cyclohexanone and 6-methyl-5-hepten-2-one) and 2-methylbutanoic acid could also be considered as the characteristic compounds of dried Chachi peel and the contribution of SD to the quality of dried Chachi peel might be partly from the preservation of these ketones and acid.  
For other detected compounds in GC-IMS analysis, including \(S\)-containing compounds, 2-acetylfuran, 2-ethyl-5-methylpyrazine, styrene, and two phenols, it could be found that 2-acetylfuran was a characteristic compound in GFD; while two phenols were characteristic for dried Chachi peel with much higher concentration in GFD/GSD than GAD, suggesting again that FD was favorable to preserve phenols and SD could preserve these two detected phenols in GC-IMS analysis as FD.
# Recent Advances on Surface Modification of Halloysite Nanotubes for Multifunctional Applications
## 2 Surface Modification of HNTs and the Relevant Properties
### Coupling Agent Modification  
Grafted silane coupling agent onto the surface is the most common chemical modification method for HNTs. The silane coupling agent can react with the HNTs through physical or chemical  
Figure 1: Illustration of the preparation of halloysite nanotubes (HNTs)/chloroxidine gluconate (CG) composite, drug release from the lumen of HNTs, and the application on cotton fabric. SEM: scanning electron microscope. Adapted with permission from [4], Royal Society of Chemistry, 2017.  
bonding. Modifications of HNTs have a superior hydrophobic property, so that they can be better dispersed in the polymer to enhance the interface interaction. [PERSON] et al. (2016) synthesized a high strength nanocomposite (polyamide 6/halloysite) by combining HNTs with 3-(trimethoxy silyl) propyl methacrylate. The results showed that the nanocomposites significantly improved its mechanical and thermal properties. Meanwhile, [PERSON] et al. prepared high-performance nanocomposite combined with 3-aminophenoxyay-phthalonitrile and poly (arylene ether nitrile) (PEN) based on HNTs ([PERSON] et al., 2017). It has been found that functionalized HNTs exhibit superior tensile strength and modulus because of their better dispersion and strong capacitance.
## 2 Data and Methods
### Auxiliary Data Sets
#### 2.5.2 Weather Forecast Data  
Weather forecast data based on models and observations are provided by the DWD. DWD employs numerous numerical weather prediction (NWP) models for regional and global domain at fine resolution. Since 2015, the operational global NWP model is based on the ICOsahedral Nonhydrostatic (ICON) model [45; 46]. In the ICON model operational ensemble data assimilation techniques are included, which utilise several observations (radiosonde, aircraft, wind profiler, surface level, satellite data) to compute a consistent state of the atmosphere and surface variables that are then used as initial state for the forecast. For the Sea Surface Temperature (SST) products, the DWD system uses the OSTIA [47] SST-analysis and observations from ships and buoys.  
The ICON model can provide high resolution output at a local domain through nesting modelling approach. The used DWD products cover Europe with a spatial resolution of \(\sim 7\) km. Hourly air-sea thermal components (SST, air temperature) and surface wind fields are provided by DWD. The air temperature from DWD is given at 2 m height, while horizontal wind components are delivered at 10 m height. Previous studies found a good agreement between DWD products and FINO-1 [48] mast measurements [17; 49]. In this study, the stability of the boundary layer is estimated through the thermal stability using the air temperature and the SST. The thermal stability is computed as the vertical gradient of potential temperature near the surface (see [PERSON] et al. [17], [PERSON] et al. [50]) and defined as:  
\[\frac{\delta\theta}{\delta z}=\frac{T_{2}-T_{1}}{\Delta z}-\gamma, \tag{21}\]  
where \(T_{2}\) is the air temperature at 2 m, \(T_{1}\) is the SST, \(\Delta z\) is the height difference (here \(\Delta z=2\) m), and \(\gamma\) is the dry adiabatic lapse rate (\(\gamma\sim\)-0.01 K/m). The atmosphere is defined as thermally neutral and stable for \(\frac{\delta\theta}{\delta z}\geq 0\), whereas unstable stratification is characterised by \(\frac{\delta\theta}{\delta z}\leq 0\).  
As previously mentioned, the study focuses on the use of SAR data for the analysis of horizontal wind speed gradients. The strict validation of SAR-derived wind fields with auxiliary data sets is out of the study scope. The LIDAR and the DWD wind fields are used as supportive material to check and particularly explain the process of unusual wind speed gradient patterns.
# An FDM-Based Dynamic Zoning Method for Disturbed Rock Masses above a Longwall Mining Panel
## 3 Case Study  
### Background of the Study Site  
#### 3.1.1 Geological Setting  
The Taixi coal mine is located in Jining City, Shandong Province, China. The territory of the area is flat, and the ground elevation is 38-41 m, with a slight fluctuation difference of less than 3 m (see Figure 8). It has been revealed through drilling that the strata from the newest to oldest in this area are Quaternary (Pingyuan formation), Neogene (Minghuazhen formation), Permian (Shihezi and Shanxi formations), and Carboniferous (Taiyuan formation); see Figure 9. More details are as follows.  
(1) The Quaternary (Q\({}_{\text{p}}\)) stratum is mainly composed of yellow clay loam intercalated with multilayer sand. The stratum has an average thickness of approximately 215.55 m and is in uncontormable contact with the Neogene.  
(2) The Neogene (N\({}_{\text{2m}}\)) stratum is mainly composed of red, yellow-brown mudstone and fine-coarse sandstone. The stratum has an average thickness of approximately 152.85 m and is in uncontormable contact with the Permian.  
(3) The Permian (P) is divided into the following two formations, from top to bottom.  
The Shangshihezi formation (P\({}_{1+2\text{s}}\)) is mainly composed of grey, purple, and other variegated mudstones and sandstones, and the bottom is carbonaceous mudstone. The stratum is in continuous deposition with the underlying Shanxi formation, with an average thickness of approximately 167.8 m.  
The Shanxi formation (P\({}_{1\text{x}}\)) is composed of black mudstone and greyish white sandstone. The average thickness of the Shanxi formation is approximately 52.94 m. The #3 coal seam is located in the lower part of the Shanxi formation, with an average thickness of approximately 3.5 m.  
(4) The Carboniferous (C\({}_{2\text{t}}\)) stratum is mainly composed of green-grey siltstone, mudstone, grey-white medium sandstone, fine sandstone, and limestone. The average thickness of this formation is approximately 174.69 m.  
Figure 8: The contour map of the ground surface in the study area.  
Figure 9: Stratigraphic column of the study site.
# Sustainable and Reliable Information and Communication Technology for Resilient Smart Cities
## 2 Related Work
### Data Models and Communication Protocols  
In addition to the aspects of reference architectures, a large number of data models and communication protocols are of particular relevance for the ICT of a smart city. First of all, the standard protocol suites of the Internet are of particular relevance--these suites include protocols such as Internet Protocol Version 4/Version 6 (IPv4/v6), Open Shortest Path First (OSPF), Routing Information Protocol next generation (RIP-ng), Border Gateway Protocol (BGP), Differentiated Services (DiffServ), Integrated Services (IntServ), Address Resolution Protocol (ARP), Neighbor Discovery Protocol (ND), Dynamic Host Configuration Protocol (DHCP), Domain Network System (DNS), Network Time Protocol (NTP), Hypertext Transfer Protocol (HTTP), Simple Object Access Protocol (SOAP) and many others, which ensure fundamental network functions of forwarding, routing, QoS (Quality of Service) and name resolution to name some (see, e.g., [36]). In the IoT access network segment, relevant protocols are given by ZigBee [36], low-rate wireless personal area networks (IEEE 802.15.4), IPv6 over Low power Wireless Personal Area Network (6 LowPAN) or low power Long Range Wide Area Network (LoRaWAN), whilst the over spanning IoT architectures utilize means such as Constrained Application Protocol (CoAP) or Message Queuing Telemetry Transport (MQTT) (see, e.g., [37]). All these protocols are based on well-defined non-proprietary standards enabling open communication network exchange within a smart city. The communication networks carry data using data and information models such Next Generation Sensor Initiative - Linked Data (NGSI-LD), Sensor Model Language (SensorML), Data Catalog Vocabulary - Application Profile (DCAT-AP) as well as different linked data-based formats describing the data and information within a smart city (see, e.g., [38, 39]).
## 3 1-vs.-1 WVR Air Combat Engagement Modelling
### State Space of 1-vs.-1 WVR Air Combat
#### 3.3.2 Target Position Prediction  
Before predicting the location of a target, it is necessary to assume that the target maneuvering is rational, and that the future control signal of the target only adds a small perturbation \(\widehat{\mathbf{e}}\) based on the current control signal. The specific calculation of the perturbation \(\widehat{\mathbf{e}}\) will be explained in Section 3.3. After calculating the perturbation, the predicted target position can be calculated while using Algorithm 1. Because the value of the perturbation is not strong and the prediction interval is small, the explicit single-step method (Euler's method) should be used to solve the UCAV motion differential equations.  
In Algorithm 1, \(T_{p}\) is the prediction interval, \(x,y,v,\phi,\psi\) are the current motion states of the target UCAV, and \(\delta t\) is the step time of solving differential equations, \(x_{p},y_{p}\) is the predicted position of the target UCAV. The components of the state space are included in Equation (14):  
\[\mathcal{S}_{predict}=\left[T_{p},x_{p},y_{p}\right]^{T} \tag{14}\]  
In summary, the three state space components of the current motion states, relative to the position and angle states and future motion states, are combined in order to obtain the overall state space:  
\[\mathcal{S}=\mathcal{S}_{base}\oplus\mathcal{S}_{relative}\oplus\mathcal{S}_{ predict} \tag{15}\]  
where, \(\oplus\) is the operator for linking two column vectors.  
```
Input:\(\widehat{\mathbf{e}}\), \(T_{p},x,y,v,\phi,\phi,\delta t\) Output:\(x_{p},y_{p}\)
1\(t:=0\);
2\(x_{p}:=x\), \(y_{p}:=y\), \(v_{p}:=v\), \(\phi_{p}:=\phi\), \(\psi_{p}:=\psi\);
3 while\(t<=T_{p}\)do
4\(v_{p}=v_{p}+\frac{\widehat{x}_{p}}{m}\delta t\);
5\(\varphi_{p}=\varphi_{p}+\widehat{c}_{u\phi}\delta t\);
6\(\psi_{p}=\psi_{p}+\frac{\sqrt{u}\,\varphi_{p}}{v_{p}}\delta t\);
7\(x_{p}=x_{p}+v_{p}\cos\psi_{p}\delta t\);
8\(y_{p}=y_{p}+v_{p}\sin\psi_{p}\delta t\);
9
10 end while
```  
**Algorithm 1**Target position prediction