esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Then the new tools statistic based on off-origin Hessians of the second characteristic function will be discussed and analyzed. We consider a synchronous DS-CDMA systems model. Simulations have been carried out to observe Interference to Signal Ratio (ISR) [24] as a function of sample number of observations and variation in bit error rate (BER) as a function of SNR. A performance comparison of DS-CDMA systems is executed using two assessment criteria to verify the advantages of the proposed method. The rest of this paper is organized as follows: Section 2 gives a description of the DS-CDMA signal model and describes the relationship between the DS-CDMA signal model and basic blind separation one. The charrelation matrix is illustrated and derived, and the blind user separation and blind spreading/chip sequence estimation are discussed in Section 3. The simulation results and discussions and concluding remarks are given in Sections 4 and 5, respectively.
## 6 Analyzing the Graph Database-Based and IFC-Based Construction Management Information Model
### Data Update  
The ability to update the construction information in real-time is the most significant advantage of twinning the IFC information model into the graph database. The process of data updating is simple. Users can query the corresponding tasks in the graph database of the construction information they are interested in through a simple search. Benefiting from the graph database's intuitive relationship, users can also get other information directly related to the construction information. For example, a user searches for a construction task and returns an IfcTask node, as well as the IfcResouce, IfcCostItem, and IfcProduct nodes that are directly associated with the IfcTask. After the user obtains the node, the node's attributes can be updated according to the actual construction data to complete the update of the graph database-based and IFC-based information model.
###### Abstract  
As controlling temperature and humidity is crucial for maintaining comfort and preventing microbial growth, operating rooms (ORs) are the most energy-intensive areas in hospitals. We aimed to evaluate the energy consumption of three dehumidification air conditioning systems used in ORs and their corresponding air quality for ORs at rest. This study selected three ORs using a conventional heating, ventilation, and air conditioning (HVAC) system; a liquid desiccant air conditioning (LDAC) system; and a rotary desiccant air conditioning (RDAC) system, respectively. The indoor thermal-hygrometric conditions, air quality, and energy consumption of the ORs were monitored in this study. The median levels of relative humidity (RH) were 66.7% in the OR using the conventional HVAC system, 60.8% in the OR using the LDAC system, and 60.5% in the OR using the RDAC system. The median daily total energy consumption of the RDAC system (10.1 kWh/m\({}^{2}\)) and LDAC system (11.8 kWh/m\({}^{2}\)) were 28.12% and 16.54% lower, respectively, than that of the conventional HVAC system (14.1 kWh/m\({}^{2}\)). The PM\({}_{\geq 0.5}\) levels and airborne bacterial concentrations in the ORs met the ISO 14644-1 Class 7 standard and China's GB50333-2013 standard, respectively. The RDAC system was clearly superior to the LDAC and conventional HVAC systems in terms of energy consumption.  
dehumidification air conditioning system; humidity; energy consumption; air quality; operating room; ventilation +
Footnote †: journal: Applied Soft 2020, 10, 3721; doi:10.3390/app10113721
## Evaluating Remotely Sensed Phenological Metrics in a Dynamic Ecosystem Model  
**[PERSON] 1,*, [PERSON] 1,* and [PERSON] 2,3**  
* [1] Department of Soil, Water and Climate, University of Minnesota, Saint Paul, MN 55108, USA
* [2] Department of Geological Sciences, Brown University, Providence, RI 02912, USA; E-Mail: [EMAIL_ADDRESS]
* [3] The Ecosystem Center, Marine Biological Laboratory, Woods Hole, MA 02543, USA
* [4] Authors to whom correspondence should be addressed; E-Mails: [EMAIL_ADDRESS] (H.X.); [EMAIL_ADDRESS] (T.E.T.); Tel.: +1-612-625-7278 (T.E.T).  
_Received: 28 March 2014; in revised form: 16 May 2014 / Accepted: 19 May 2014 / Published: 26 May 2014_  
**Abstract:** Vegetation phenology plays an important role in regulating processes of terrestrial ecosystems. Dynamic ecosystem models (DEMs) require representation of phenology to simulate the exchange of matter and energy between the land and atmosphere. Location-specific parameterization with phenological observations can potentially improve the performance of phenological models embedded in DEMs. As ground-based phenological observations are limited, phenology derived from remote sensing can be used as an alternative to parameterize phenological models. It is important to evaluate to what extent remotely sensed phenological metrics are capturing the phenology observed on the ground. We evaluated six methods based on two vegetation indices (VIs) (_i.e._, Normalized Difference Vegetation Index and Enhanced Vegetation Index) for retrieving the phenology of temperate forest in the Agro-IBIS model. First, we compared the remotely sensed phenological metrics with observations at Harvard Forest and found that most of the methods have large biases regardless of the VI used. Only two methods for the leaf onset and one method for the leaf offset showed a moderate performance. When remotely sensed phenological metrics were used to parameterize phenological models, the bias is maintained, and errors propagate to predictions of gross primary productivity and net ecosystem production. Our results show that Agro-IBIS has different sensitivities to leaf onset and offset in terms of carbon assimilation, suggesting it might be better to examine the respective impact of leaf onset and offset rather than the overall impact of the growing season length.  
keywords: phenology; remote sensing; dynamic ecosystem model; Agro-IBIS; MODIS +
Footnote †: journal: Journal of Scientific Computing
## 6 Results
### Evaluation of Privacy Protection
Figure 7: Reconstruction quality (SSIM, MS-SSIM) vs. noise variance (\(\sigma^{2}\)) for a victim MobileNetV3 backbone model on the CelebA dataset. In Figure 7, the reconstruction quality decreases as the noise variance (\(\sigma^{2}\)) increases, and the reconstruction quality plateaus for values \(\sigma^{2}\geq 1.3\). We choose \(\sigma^{2}=0.4\) because it is the smallest perturbation that offers the best privacy before the values plateau. We observe that the annulment factor has a greater influence on the quantitative analysis of reconstruction quality when compared to the noise variance. The previous quantitative measures are useful for observing trends for hyperparameter configurations; next, we also present a qualitative analysis of our system because the existing metrics are not designed with privacy in mind. Specifically, we consider some examples of reconstructed images from the PennFudan and CelebA dataset using hyperparameter configurations based on the trends presented earlier. We verify that the observed trends are reflected qualitatively and study observations that are not represented using standard metrics such as SSIM and MS-SSIM. In Figure 8, we consider the worst-case scenario in which the reconstruction from unprotected feature maps results in images that bear a significant resemblance to the original images. In the third column, we see that our proposed scheme can minimize the exposure of any information in the images to the cloudlet. Now, we discuss how the method of feature map annulment using the annulment value (\(k\)) affects the reconstruction quality. As seen in Figure 9, for the same variance threshold (\(\sigma^{2}\)) and annulment factor (\(\lambda\)), feature map annulment using a Gaussian distribution (\(k\sim\mathcal{N}(0,1)\)) is the most effective at preserving privacy. As far as smaller details are concerned, feature annulment with \(k=1\) offers the best performance and privacy. Figure 8: Comparison of reconstruction quality with and without our proposed scheme. The first column shows original images taken from the CelebA dataset, the second column shows the images reconstructed using the unprotected feature maps without using our scheme, and the third column shows the images reconstructed using an annulment factor of \(\lambda=0.3\), noise variance of \(\sigma^{2}=0.4\), and mean of \(\mu=0.1\). The annulment value used is based on a Gaussian distribution (i.e., \(k\sim\mathcal{N}(0,1)\)).
## 2 Materials and Methods
### Source Inversion  
An observed STF at \(i\)-th station, \(H_{i}(t)\), can be represented as the summation of all the weighted STFs at subfault \(B_{j}(t)\), with the time delay due to wave propagation and the propagation of the rupture front with respect to the hypocenter taken into account.  
\[H_{i}(t)=\sum\limits_{j}^{m}B_{j}(t-\tau_{ij})w_{j} \tag{5}\]  
where \(\tau_{ij}\) is the time delay due to the wave and rupture propagation, \(i\) is a station index, \(j\) is a subfault index, and \(w_{j}\) is the slip weight of each subfault. Equation (5) can be written in matrix form:  
\[S=KD\]where \(S\) is a data matrix consisting of observed STFs, \(D\) is an unknown vector composed of the slip weight \(w_{j}\) of each subfault, and \(K\) is the matrix determined by each STF of subfault \(B_{j}(t)\) associated with the aforementioned time delay. If we solve (6) to obtain \(w\), then the slip amplitudes at each subfault, \(u_{j}\), can be obtained as follows:  
\[\mu_{j}=\frac{M_{0}w_{j}}{Am} \tag{7}\]  
where \(A\) is the subfault area, \(M_{0}\) is the seismic moment, and \(\mu\) is the shear modulus. A constraint condition, \(w_{j}\geq 0\), must be imposed on (6) to solve \(w_{j}\). Finally, we can estimate \(w\) by applying the least square and conjugate gradient methods [21] to obtain the slip source model.
# Design and Numerical Study of Induction-Heating Graphitization Furnace Based on Graphene Coils
## 4 Conclusions  
An induction-heating graphitization furnace based on graphene coils is proposed in this paper. We conduct a detailed analysis of its energy efficiency, electromagnetic, and temperature-field distributions by comparing an enhanced furnace model with a traditional one through multi-physical-field coupled simulations. The paper also covers how variations in the coil parameters influence the coil losses and temperatures. The following are the key findings:  
Figure 12: (**a**) Temperature distributions of induction furnace for different coil positions. (**b**) Temperature distributions along r-axis. (**c**) Effect of coil position on energy efficiency.  
1. The proposed furnace model significantly improves the energy utilization of the induction furnace and reduces the energy consumption by 33.34%. Because the graphene coils of the new furnace have no cooling water inside, the coil losses are used to maintain the heater temperature. Positioned closer to the heater, these coils achieve superior electrical efficiency;
2. Both the coil conductor's conductivity and the coil's radius are crucial factors affecting the coil losses;
3. The coil's distance to the heater significantly impacts the furnace's performance. Optimal positioning of the coil is vital for balancing the furnace's energy efficiency and coil temperature. Our work underscores the promising application potential of macroscopic graphene materials in induction-heating technologies, particularly for high-temperature processes, like those found in graphitization and polysilicon furnaces. Future directions include the experimental validation of the proposed furnace design.  
Conceptualization, H.D. and R.L.; methodology, R.L.; validation, R.L. and Y.Z.; formal analysis, J.L. and R.L.; investigation, R.L.; resources, R.L.; data curation, R.L. and Y.Z.; writing--original draft preparation, R.L.; writing--review and editing, L.G. and H.D.; visualization, H.D.; supervision, H.D.; project administration, B.L.; funding acquisition, X.C. and H.D. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Science and Technology Bureau of Ulanqab City (Grant No. 2022 JB005) and the Science and Technology Innovation Bureau of Shenzhen (Grant No. KCXFZ2020122 1173010027).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflicts of interest.
## 5 Experiments
### Data Acquisition  
According to the configuration of the imaging setup, an imaging algorithm needs to be used to reconstruct an image from measurements of the reflected or transmitted fields. The configuration of the imaging system can either be monostatic or multistatic, where one or more antennas are used as transmitters and receivers. In monostatic configuration, a single antenna serves as both transmitter and receiver. In multistatic configuration, each antenna transmits the microwave signal, and the scattered signals are captured by all other antennas. This procedure is repeated for all antennas until all signal samples are obtained. Although, the multistatic approach is more complex compared to the monostatic approach, it provides detailed information about the imaged area/object and hence improves the accuracy.  
In this work, an array of six monopole directional antennas were placed around the phantom at equal distances. To achieve good penetration, the antennas were separated from the absorber using a 5 mm thick foam. The dielectric value of the foam is similar to that for air to ensure the integrity of antennas. The phantoms were penetrated in a way where one antenna transmitted the signal and where reflected and transmitted signals were captured by all other antennas. The experiments were repeated for each antenna to capture both the reflected signals (S\({}_{11}\)) and transmitted signals (S\({}_{21}\)). However, only the reflected signals are used for image reconstruction. The total number of 1001 frequency points were stored for each antenna. These points were organised as a matrix for each case.  
Similarly, an empty skull was penetrated with the same configuration and the reflected data were stored. These data were used as a reference signal to remove strong reflections from the skin and skull.The matrix of signals was then converted to a time domain using Fourier transform. The resulting signals were used as input to the imaging algorithm for image reconstruction.
## 4 Conclusions  
In summary, we developed a high beam quality and high compact hundred picoseconds SLM laser system based on SBS phase conjugate compensation and pulse compression technologies.  
Figure 6: Intensity distribution of SBS pulse width compressed output (**a**) 2D distribution of near-field; (**b**) 3D distribution of near-field; (**c**) 2D distribution of far-field; (**d**) 3D distribution of far-field.  
Figure 7: Measured beam characteristics of \(M^{2}\) with \(M_{x}^{2}=1.80\) and \(M_{y}^{2}=1.75\).  
Compact single-cell structure is adopted in our experiment. By building a standing-wave field with the forward transmission pump light and backward transmission Stokes light, high stability pulse compression is obtained. Owing to the excellent properties of FC-770, these pulses were delivered with a near-diffraction limited spatial intensity profile. Also, different output energy and compressed pulse width were compared experimentally. In the operation of the whole system, maximum energy 220 mJ and compressed pulse around 400 ps was achieved with no breakdown of any optical device or media. The overall energy jitter of the system is less than 3%. The excellent energy, stability and transverse beam profile performances make this system a promising candidate for scientific and industrial applications.  
This work is supported by the National Natural Science Foundation of China (grant Nos. 61378007, 61138005 and 61378016). All the authors have made significant contributions to this study and have approved this submission. [PERSON] built the whole system and drafted the main manuscript; [PERSON] and [PERSON] supervised the research and provided the experimental platform; [PERSON] and [PERSON] designed the pulse compressor; [PERSON], [PERSON] and [PERSON] contributed to the data analysis and results discussion; [PERSON] revised the article and responses the reviewer's comment; [PERSON] and [PERSON] recorded the experimental data.  
The authors declare no conflict of interest.
# Explainable AI Frameworks: Navigating the Present Challenges and Unveiling Innovative Applications
## 4 Discussion
### XAI Frameworks
#### 4.1.17 Kernel SHAPs  
The XAI framework, Kernel SHAPs, focuses on explaining the individual predictions for various models with numerical outputs. It utilizes local explanations to delve into how each feature value contributes to a specific prediction, offering insights into the model's decision-making process for a particular instance. Unlike SHAPs, which rely on linear models, Kernel SHAPs employ kernels to handle non-linear feature relationships, making them well-suited for explaining complex models. Notably, they can be applied to diverse model types, including deep learning architectures, and leverage kernels and Shapley values to provide interpretable explanations [48].
## 3 Proposed Scheme  
### System Model  
In the sensor deployed systems considered, the cellular channel is divided into cellular and D2D links. A sensor can be connected to an aggregator via a cellular link or connected to another sensor via a D2D link. Because of these links, there are four interference cases as shown in Figure 2. The first two cases are the interference between cellular links and between D2D links. This interference can occur in both intra-cell interference and inter-cell interference in the case that the same frequency is allocated to the same type of link. Another case is the interference between cellular and D2D link. The process for sensors to control the complex interference situation is a challenging issue. The proposed solution in this paper focuses on minimizing interference while maximizing throughput by allocating the given radio frequency intelligently to each link.
## 3 Results and Discussion
### Cut Resistance  
The goal of the cut resistance tests was to determine the load required for cutting through knitted fabric with the edge of the 20 mm length blade. The test results are presented in Figure 3.  
Figure 2: Tensile characteristics: (**a**) breaking force and (**b**) elongation at break.  
It was found that the higher loop length determines higher cut resistance. The increase of the loop length of the plain structure WP by 11.4% leads to the increase of the cutting through force by 33.3%. However, the similar increase of the loop length (10.4%) in the WPE structure with the elastomeric yarn caused approx. three times lower increases of the cut resistance--therefore, the cut resistance increased by only 10.7%. The obtained results demonstrate that the lower stitch density (caused by the higher loop length) leads to the higher coefficient of slippage due to the deflection of the textile material under the influence of the blade pressure. Therefore, the loose-knitted structure resists the mechanical action of the cutting force of the blade more effectively.
## 3 Results and Discussion
### Mechanical Properties of Mortars
The values of water absorption by total immersion referred to the weight and to the volume were higher for the specimens of the M25 mixture, respectively 5.5% by weight and 11.8% by volume, while the lowest ones were those measured on the reference mixture, respectively 4.6% by weight and 10% by volume. Figure 9 shows the uniaxial compressive strengths vs. the water absorption by total immersion referred to weight of the mortar specimens MS, M5, M10, M15, M20, and M25 at 28 days of curing. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & **Apparent Density** & **Capillarity Absorption** & **Water Absorption** & **Water Absorption** \\  & **(g/cm\({}^{3}\))** & **Coefficient (g/m\({}^{2}\) s\({}^{0.5}\))** & **(wt.\%)** & **(vol.\%)** \\ \hline \multicolumn{5}{c}{Mean SD = 0.02 g/cm\({}^{3}\)} & Mean SD = 1.2 g/m\({}^{2}\) s\({}^{0.5}\) & Mean SD = 0.2\% & Mean SD = 0.5\% \\ \hline MS & 2.18 & 14.2 & 4.6 & 10.0 \\ M5 & 2.19 & 13.2 & 4.8 & 10.4 \\ M10 & 2.18 & 13.5 & 4.8 & 10.4 \\ M15 & 2.16 & 15.2 & 5.0 & 10.9 \\ M20 & 2.15 & 15.0 & 5.3 & 11.4 \\ M25 & 2.13 & 17.7 & 5.5 & 11.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Apparent density, water absorption coefficient by capillarity, water absorption by total immersion referred to the weight and to the volume, measured in the MS, M5, M10, M15, M20, and M25 mortar mixtures at 28 days of curing. Figure 9: Uniaxial compressive strength vs. water absorption by total immersion referred to weight of the MS, M5, M10, M15, M20, and M25 mortar mixtures at 28 days of curing. From the graph, it is possible to observe that the water absorption by total immersion referred to the weight decreases as the uniaxial compressive strength of the specimens increases (it increases with the increase in the percentage of cement replacement with marble powder). The best equation describing the relationship between uniaxial compressive strength and water absorption by total immersion referred to the weight is as follows:  
\[\text{Water absorption (wt.\%)}=-0.0628\cdot\sigma_{\text{c}}\text{ (MPa)}+8.208,\,R^{2}=0.94.\]
# Enhanced Solar Photovoltaic System Management and Integration: The Digital Twin Concept
## 4 Challenges of PV-DT
### Economic Implications  
Another major challenge to PV-DT is the cost-benefit considerations for medium-sized installations. Currently, a full DT setup may only become commercially viable for very large installations in order of tens of MWp and above [136]. This is largely because of the initial capital for DT setup and additional running costs from cloud data and computing services [135; 137]. As noted by [117], DT implementation scalability is critical to its adoption in the PV area. As this technology matures, it is expected to become more scalable for smaller or distributed installation sizes. This is a classic case of smaller PV installations in various remote/rural areas being jointly monitored from a single control system [57].