esa-sceva/satcom-chunk-collection huggingface dataset:
# How the Implementation of BREEAM in Hotels Could Help to Achieve the SDGs
## 1 Introduction
Its main objective is to guide the construction sector towards sustainability. 8. SDG 15. Life of Terrestrial ecosystems. It seeks to minimize the damage caused to the environment and its biodiversity when building. Although the UNWTO has established that tourism contributes to the achievement of the SDGs [2], the hotel industry faces the challenge of determining which of the 17 SDGs and their associated targets are its priority [12]. Hotel guests have radically changed their attitude towards adopting environmentally friendly practices, and, as a result, hotels cannot ignore their environmental and social responsibilities [13, 14, 15]. Research on efficient energy-use procedures and interventions based on clients energy-related behavior is crucial for hotelers to understand what the guests' needs are and how these can be incorporated in the hotel [16]. A sustainable hotel must operate according to the principles of green engineering by implementing waste management systems, recycling and saving water and energy, among other procedures [17]. These approaches must be incorporated from the design stage of the building. A recent study has shown that the application of environmental procedures and their consequent certifications can optimize the image of the firm and its operational performance [18]. The EU Energy Efficiency Building Directive (EPBD) and the UK Climate Change Act 2008 included sustainable buildings on their policy agenda. Since then, a wide variety of tools have been developed to assess and assist construction projects, with the Building Research Establishment Environmental Assessment Method (BREEAM) being among the most successful of them [19]. BREEAM leads the list and, although it was initially designed to focus primarily on environmental aspects [20; 21], in the last decade it has also highlighted social and economic aspects. Despite its global implications, there is not much research on how improving hotel sustainability can help successfully achieve the SDGs of the 2030 Agenda. This paper studies the application of the BREEAM energy certificate in hotel design. It shows how the implementation of a sustainable certificate can help to redefine infrastructure and create sustainable hospitality buildings according to climate change needs and the SDGs of the 2030 Agenda.
# A Robust Hybrid Iterative Linear Detector for Massive MIMO Uplink Systems
## 3 Methods
### Proposed Method  
Initialization of detection methods impacts the performance-complexity profile as well as the convergence rate. Proposed method exploits the benefits of low complexity JA and GS methods. In addition, a stair matrix (**S**) is also utilized to guarantee a fast convergence rate. In the literature, most methods are using the diagonal matrix where there is no guarantee of achieving a satisfactory convergence rate. Figure 1 illustrates the block diagram of the proposed detector, and Figure 2 presents the flowchart of the two stages proposed detector, the initialization, and the final estimation. Unlike the work in [16], where a detector is proposed based on individual GS, individual SOR, individual JA, individual NI, and individual RI methods, this paper proposes a hybrid JA-GS detector which initialized using a stair matrix. Proposed detector obtained a satisfactory balance between the performance and the complexity even when the BUAR is small (BUAR \(<5\) ).  
The initial solution is first computed based on the stair matrix as  
\[\hat{\mathbf{x}}_{(0)}=\mathbf{S}^{-1}\mathbf{y}_{MF}. \tag{9}\]  
Then, the first iteration of the JA method is conducted as  
\[\hat{\mathbf{x}}_{(1)}=\mathbf{D}^{-1}\left(\hat{\mathbf{y}}_{MF}+\left(\mathbf{ D}-\mathbf{A}\right)\hat{\mathbf{x}}_{(0)}\right). \tag{10}\]  
In the next step, the estimation is conducted and iteratively refined based on the GS method as shown in (7), where \(n=2\). Algorithm 1 illustrates the proposed detection algorithm using the JA, GS methods, and the stair matrix.  
Figure 1: Block diagram of the proposed detector based on the JA and GS methods and a stair matrix.  
Figure 2: Flowchart of the proposed detector based on the JA and GS methods and a stair matrix.
## 4 Model Development and System Architecture
### Training Process  
Figure 7 displays the training process phases for DoH tunneling attack detection. This process begins with the data preprocessing phase, removing the data point with infinity and nulls and replacing it with the mean value of that feature in the dataset. This step guarantees that only meaningful data points can be fed into our model. Then, data are provided into two-layer classification, both using Transformer architecture. In the previous works, Refs. [15; 16; 17], the ratio of DoH traffic to regular HTTPS traffic is 8:2, and the ratio of normal DoH traffic to malicious traffic is 10:1. We kept the same percentage in our work to make a fair comparison. The first layer objective is to isolate DoH traffic from non-DoH traffic. The dataset for the first layer included 233,427 instances, which is only 20% of instances compared with the previous work, and the ratio of DoH traffic to regular HTTPS is 8:2. After the training process, the model was used to classify the rest of the dataset to check the accuracy of the model. The second layer categorizes instances as malicious DoH or normal DoH. From the DoH traffic classified from the first layer, we randomly chose  
\begin{table}
\begin{tabular}{c c} \hline
**No.** & **Feature Name** \\ \hline
1 & Duration \\
2 & Flow bytes sent \\
3 & Flow sent rate \\
4 & Flow bytes received \\
5 & Flow received rate \\
6 & Packet length variance \\
7 & Packet length standard deviation \\
8 & Packet length mean \\
9 & Packet length median \\
10 & Packet length Mode \\
11 & Packet length skew from median \\
12 & Packet length skew from mode \\
13 & Packet length coefficient of variation \\
14 & Packet time variance \\
15 & Packet time standard deviation \\
16 & Packet time mean \\
17 & Packet time median \\
18 & Packet time mode \\
19 & Packet time skew from median \\
20 & Packet time skew from mode \\
21 & Packet time coefficient of variation \\
22 & Response time variance \\
23 & Response time standard deviation \\
24 & Response time mean \\
25 & Response time median \\
26 & Response time mode \\
27 & Response time skew from median \\
28 & Response time skew from mode \\
29 & Response time coefficient of variation \\ \hline \end{tabular}
\end{table}
Table 2: List of the 34 statistical features extracted from captured traffic using CICFlowmeter.  
Figure 6: Raw DoH traffic capture.  
53,928 instances, which are only 20% of instances compared with the previous work that had a ratio between regular DoH traffic and malicious DoH of 10:1. In both layers, we used the same Transformer architecture with similar parameters. The models were trained on a local CPU (Intel Core i7 8 th 3.20 GHz) and GPU NVIDIA GeForce GTX-3060 8 GB (32 GB memory). The model parameters were constructed as in Table 3.
# Salicylic Acid Modulates Volatile Organic Compound Profiles During CEVd Infection in Tomato Plants
## 2 Materials and Methods
### Normalization of Ion (m/z) Profiles Across Genotypes  
To compare the volatilome of _NahG_ vs. _RNAi_S5H_ genotypes in CEVd-infected conditions, ion levels were normalized across genotypes using a global baseline average from mock samples, which was adjusted by a baseline factor specific to each genotype. This process involved the following steps: (I) Calculation of a Reference Baseline: The average ion level across all genotypes under mock (control) conditions was computed to serve as a common baseline reference (global mock average). This average was derived as follows:  
\[\textbf{{Global\ Mock\ Average}}=\frac{\sum_{i=1}^{n}\textbf{{Mock\ level\ of\ Genotype}}}{n}\]  
where n represents the number of samples.  
(II) Baseline Adjustment for Each Genotype: For each genotype, the relationship between its individual mock level and the global mock average was determined. This ratio, referred to as the \"baseline adjustment factor\", was calculated as follows:  
\[\textbf{{Baseline\ Adjustment\ Factor}}=\frac{\textbf{{Mock\ level\ of\ Genotype}}_{i}}{\textbf{{Global\ Mock\ Average}}}\](III) Normalization of Treated Samples: Ion levels of treated (CEVd-infected) samples were normalized by dividing their raw values by the corresponding baseline adjustment factor of their genotype. The normalized volatile level for treated samples was expressed as follows:  
\[\textbf{Normalized Treated Level}=\frac{\textbf{Raw Treated Level}}{\textbf{Baseline Adjustment Factor}}\]  
This normalization ensured that differences in volatile levels across genotypes under mock conditions did not confound the evaluation of treatment effects.
# Performance of Ore Sand as Aggregate for Interlocking Blocks
###### Abstract  
The search for alternatives that can replace conventional materials extracted from nature is crucial for environmental sustainability. This is especially the case for construction and geotechnical engineering, as this sector is a major consumer of the planet's natural resources. This paper explores the use of ore sand (OS) tailings with fine aggregate characteristics, which are generated during exploration of iron ore and usually stored in stacks, in terms of their potential for replacing conventional aggregates (gravel, natural sand, and limestone sand) used in interlocking paving blocks for light-traffic pavement. A comparative life cycle assessment (LCA, cradle-to-gate) approach was applied for aggregates in interlocking blocks produced using OS compared to blocks produced with conventional aggregates. The OS was able to replace 24 wt% of conventional aggregates (100% limestone sand and 13.3% natural sand), while maintaining compressive-strength performance similar to that of the conventional block (35 MPa). For all eleven environmental-impact categories evaluated in the LCA, the block with OS has improved environmental performance compared to the conventional block. Through the creation and use of a scoring indicator (SI), it was possible to determine that the transport distance of the OS is a limiting factor for the environmental efficiency of the alternative block. Nonetheless, the incorporation of OS mitigates the impacts of block production on eutrophication, acidification, fresh water, human toxicity, and abiotic depletion. The use of tailings reduces the demand for natural resources, decreasing the environmental impact of production and promoting sustainable construction practices.  
ore sand; aggregates; interlocked block; tailings; life cycle assessment +
Footnote †: journal:
# Optical Design of a Miniaturized Airborne Push-Broom Spectrometer
## 2 System Specification
### Optical System Parameters
#### 2.2.3 Relative Aperture  
The relative aperture mainly affects the SNR of the spectrometer. The larger the relative aperture is, the higher the SNR of the image is, which is beneficial to obtain high-quality spectral data. However, the aberration is difficult to correct, and the volume and weight are also increased. A small relative aperture will result in a decreased energy in the image and a small SNR. Combining the existing spectrometer, the relative aperture of the front objective lens is determined to be 1/4.5 according to the SNR estimation and the optical design analysis.
# Decoding Temporal Bone Carcinoma: The Role of CT and MRI in Precision Staging
###### Abstract  
Primary squamous cell carcinoma (SCC) of the temporal bone is an extremely rare malignancy, comprising only 0.2% of head and neck cancers. There is currently no universally accepted staging system for these tumors. This study evaluates the utility of the Pittsburgh staging system for temporal bone SCC, examines prognostic factors, and reports clinical outcomes. A retrospective review was conducted on a small cohort of 15 patients diagnosed with temporal bone SCC between 2020 and 2023. Imaging included high-resolution CT and MRI (T1W, T2W, and DWI). Staging accuracy was assessed, and [PERSON]'s Kappa coefficient was used to compare radiological and pathological staging. Results showed a 73% accuracy in preoperative radiological staging, with higher agreement for T3/T4 tumors (86%) compared to T1/T2 (63%). MRI provided additional information, revealing dural enhancement in two patients and parotid involvement in one. Survival analysis indicated 100% survival in T1/T2 patients, 25% in T3, and 0% in T4, supporting the prognostic value of the Pittsburgh system. While limited by the small sample size, the study confirms that the Pittsburgh staging system offers a reliable approach to stratifying patients with temporal bone SCC and underscores the importance of combined CT and MRI for accurate staging and treatment planning.  
squamous cell carcinoma; temporal bone; diagnostic imaging; head and neck cancer +
Footnote †: journal: Applied sciences  
article
## 2 Materials and Methods
### Design of ADRC  
ADRC is a contemporary control methodology whose fundamental principle revolves around estimating and compensating for both internal and external system perturbations in real time to enhance the control performance [23]. The self-immobilizing controller comprises three integral components: the tracking differentiator (TD), the extended-state observer (ESO), and the nonlinear state error feedback mechanism. The TD facilitates a smooth transition process, accurately tracking the reference input signal \(T^{*}\) and mitigating abrupt changes in \(T^{*}\). The ESO offers estimations of the system's state variables, \(z_{1}\) and \(z_{2}\), as well as a real-time assessment of system perturbations. The nonlinear state error feedback converts the error between the transition process and the object's state variables into a nonlinear combination, providing the necessary control input for the system [24]. These three components are interconnected, yet they can be designed independently. Figure 4 illustrates the basic structural configuration of this control architecture.
## 4 Experimental Validation
### The Efficacy of the LSTM-RNN-Oriented RLU Prediction
As can be seen in Table 3, the proposed method's prediction error at cycle 350 is 32 cycles, which is lower than the error of 36 and 81 cycles predicted by the gated recurrent unit (GRU) RNN [31] and the simple RNN, respectively. This indicates that our method is more effective at capturing long-term dependencies than those other two approaches. The prediction error at cycle 450 is 21, 32, and 61 cycles, respectively. It is clear that, in both cases, the simple RNN presents the largest error. This is ample evidence showing its inability to effectively store and learn long-term dependencies without special gates. The proposed method's results on specimen B are shown in Figure 15. The prediction error of our proposed LSTM-RNN, the GRU-RNN, and the simple RNN at cycle 350 were 41, 43, and 95 cycles, respectively. At cycle 450, they were 34, 37, and 89 cycles. Figure 14: LSTM-RNN-based RLU prediction results of concrete beam A: (**a**) at cycle 350; (**b**) at cycle 450. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Fault-to-Failure** & **Method** & **Prediction Error Cycles** & **Prediction Error Cycles** \\
**Signals** & & **(at Cycle 350)** & **(at Cycle 450)** \\ \hline \hline \multirow{2}{*}{Concrete beam} & LSTM-RNN & 32 \(\pm\) 3 & 21 \(\pm\) 4 \\  & GRU-RNN * & 36 \(\pm\) 4 & 32 \(\pm\) 5 \\ \multirow{2}{*}{A} & Simple RNN & 81 \(\pm\) 6 & 61 \(\pm\) 8 \\ \multirow{2}{*}{Concrete beam} & LSTM-RNN & 41 \(\pm\) 7 & 34 \(\pm\) 5 \\  & GRU-RNN * & 43 \(\pm\) 6 & 37 \(\pm\) 7 \\ \multirow{2}{*}{B} & Simple RNN & 95 \(\pm\) 11 & 89 \(\pm\) 8 \\ \multirow{2}{*}{Concrete beam} & LSTM-RNN & 36 \(\pm\) 3 & 24\(\pm\) 3 \\ \multirow{2}{*}{C} & GRU-RNN * & 39 \(\pm\) 7 & 32 \(\pm\) 3 \\  & Simple RNN & 88 \(\pm\) 4 & 68 \(\pm\) 7 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: RUL prediction results. In Figure 16, specimen C's prediction results from the proposed method are plotted; its values can be again checked in Table 3. The first prediction at cycle 350 shows the prediction error for our proposed method and the GRU-RNN as 36 and 39 cycles, respectively; the prediction error at cycle 450 are 24 cycles with LSTM-RNN and 32 cycles with GRU-RNN. Concerning the simple RNN, the 88 and 68 divergent cycles in the two predictions clearly demonstrate its failure of long-term dependencies learning.
## 3 P-MAC Design Integrated with Routing  
The conventional duty cycle MAC protocols were designed independently neglecting the impact of the network layer. P-MAC considers cross-layer optimization with the goal of minimizing the communication overhead and maintaining the superiority of duty-cycling schemes. It divides all sensor nodes into different grades according to their logical hop distances to the sink. The lower a node's grade, the fewer hops it needs to send packets to the sink.
# Two Decades of Groundwater Variability in Peru Using Satellite Gravimetry Data
## 4 Discussion
### Methodological Transferability and Broader Applicability  
While this study focuses on Peru, its methodology has clear potential for generalization to other data-scarce or hydrogeologically diverse regions. The combined use of GRACE-DA-DM satellite products, statistical normalization through percentiles, regression modeling of climatic and anthropogenic drivers, and validation with limited in situ observations forms a modular framework that can be adapted to similar contexts in South America, Sub-Saharan Africa, Central Asia, or arid zones in the Mediterranean. Furthermore, the application of percentile-based hydrological anomaly detection provides a scalable tool for monitoring groundwater conditions where absolute storage volumes are unavailable. This study thus offers not only context-specific insights, but also a replicable approach to understanding groundwater trends in regions facing climatic pressures, land-use change, and overextraction.  
GRACE's coarse resolution (-300 km) limits the detection of localized GWS variations--especially in the Andes, where abrupt topography leads to complex recharge patterns--potentially over-smoothing key processes such as saltwater intrusion [19]; combined with the absence of well observations (-5% uncertainty), this hampers the accuracy of GWS estimates. Future research should incorporate high-resolution models (e.g., MODFLOW [27; 38]) and expand in situ monitoring to better capture localized dynamics and account for land-use changes such as urbanization and deforestation.
# Secure Communication for Uplink Cellular Networks Assisted with Full-Duplex Device-to-Device User
## 1 Introduction
From the perspective of the physical layer, physical layer security technology uses channel characteristics to protect information transmission, which can greatly enhance the security of the communication system [23; 24; 25]. Thus, physical layer security technology in this paper is adopted to increase the security of cellular networks based on D2D in FD mode. The main contributions of our proposed scheme can be summarized as follows:  
1. A novel secure cooperative communication model is proposed to simultaneously solve the problems of long-distance cellular uplink transmission and reliability of auxiliary transmission. In the established system model, the D2D user can play the role of relay to assist in the information transmission from CU to BS. As the low spectrum efficiency of HD, the relay in this paper is operated in FD mode to shorten the transmission time slot, which is not involved in [9; 10; 11; 12]. Unlike the existing works, we consider the reliability of D2D user-assisted transmission, which means that the relay is untrusted at the data level. Alternatively, the relay may decode confidential information from the received signal. In order to ensure secure communication between the CU and the BS, the CU transmits a signal that combines a confidential signal and an interference signal to reduce the signal to noise ratio (SNR) of the untrusted relay. 2. Based on physical layer security, the secrecy rate is defined as the rate difference between the legal link and wiretapping link. Therefore, we first derive the expression of the secrecy rate of the cellular uplink for the untrusted relay. If the secrecy rate is greater than a positive threshold, it indicates that the D2D user cannot successfully decode the confidential information. Considering the interference of the CU to the D2D user, the instantaneous rate expression of D2D is obtained. Subsequently, an optimization problem to maximize the D2D link rate is formulated. 3. For the optimization problem formulated, we jointly optimized the power allocation factors of the CU and the D2D user. Specifically, the optimal power allocation factor at the D2D user is obtained based on the monotonicity of the objective function. Then we prove that the power allocation factor at CU is optimal. The remainder of this paper is organized as follows. Section 2 describes the considered system model of cellular networks based on the relay played by D2D in FD mode. In Section 3, we formulate the D2D link rate maximization problem with the constraint of cellular secrecy rate. Moreover, the power allocation factors at CU and the D2D user is optimized. In Section 4, simulation results verify the performance of our proposed scheme compared with the conventional scheme. Finally, Section 5 concludes this paper.
# Sample Reduction-Based Pairwise Linear Regression Classification for IoT Monitoring Systems
## 3 SRbPLRC Framework for Large-Size Video Face Recognition
### APSRbPLRC Algorithm  
From the above subsection, we can see that the DSRbPLRC algorithm increases the number of videos, which may reduce the computing speed, and it does not consider the influence of any noise or outliers that may exist in the videos. Thus, the APSRbPLRC algorithm is introduced in this subsection, which first uses the clustering method to divide each video into several sub-videos, and then uses the centroid and mean values of each sub-video as anchor points.  
Anchor points are the most representative points in videos or image sets, and the subsets formed by anchor points can approximately replace the original videos or sets. In order to decrease the size of image sets or videos and improve efficiency, subsets of anchor points are constructed by extracting the common characters from each image and singling out the most representative points. An illustration of this process is shown in Figure 3.  
In this study, the hierarchical divisive clustering (HDC) algorithm [31] is used as the clustering method to extract the clusters, i.e., to divide each video into several sub-videos. Specifically, the HDC algorithm regards each image set or video as a manifold and measures the non-linearity degrees of different clusters according to the deviations between the Euclidean distances and geodesic distances. This ensures that there is a similar number of images in the extracted clusters, and that the samples in each cluster have similar visual characteristics. Then, the mean image of each cluster is computed as the anchor point, which can in turn represent the whole subset.  
After obtaining the anchor points, the size of the videos is greatly reduced, and due to the necessary information being preserved by the anchor points, recognition rates can also be retained. We should point out that other anchor points extraction strategies can also be used, such as the K-means clustering approach, spectral clustering algorithms, and one-class SVMs.  
In this study, for \(C\) gallery videos \(\{X_{1},\cdots,X_{C}\}\) and a probe video \(Y\), the HDC algorithm is first used to extract the anchor points of each video, so we could obtain the following anchor point gallery and probe videos: \(\{X_{1}^{ap},\cdots,X_{C}^{ap}\}\) and \(Y^{ap}\) (where \(X_{i}^{ap}\) contains \(N_{i}^{ap}\) representative images, \(Y^{ap}\) contains \(n^{ap}\) representative images, and \(N_{i}^{ap}\ll N_{i}\), \(n^{ap}\ll n\) is satisfied). As with the DSRbPLRC algorithm, we could then directly obtain the label of \(Y\) using Equations (5) and (6).