esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Concrete Made with Recycled Products
#### 4.4.1 Compressive Strength
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Mix**} & \multirow{2}{*}{**Curing Time**} & \multicolumn{2}{c}{**Control Mixture**} & \multicolumn{2}{c}{**50\% Recycled Mixture**} & \multicolumn{2}{c}{**Cement Sludge Mix**} \\ \cline{3-8}  & & **Density** & **Compr.** & **Density** & **Compr.** & **Density** & **Compr.** \\  & **(kg/m3)** & **Strength (MPa)** & **(kg/m3)** & **Strength (MPa)** & **(kg/m3)** & **Strength (MPa)** \\ \hline \multirow{4}{*}{**C25/30**} & 3 & 2355 & 21.88 & 2316 & 21.22 & 2288 & 19.31 \\  & 7 & 2334 & 26.45 & 2343 & 25.54 & 2290 & 23.54 \\  & 14 & 2311 & 29.58 & 2355 & 29.62 & 2291 & 26.35 \\  & 21 & 2310 & 32.09 & 2317 & 31.50 & 2295 & 29.17 \\  & 28 & 2353 & 34.17 & 2356 & 33.62 (\(-1.6\%\)) & 2302 & 31.78 (\(-7\%\)) \\ \hline \multirow{4}{*}{**C30/37**} & 3 & 2322 & 40.46 & 2276 & 39.65 & 2206 & 38.03 \\  & 7 & 2341 & 45.37 & 2368 & 44.46 & 2224 & 42.64 \\ \cline{1-1}  & 14 & 2304 & 48.08 & 2273 & 47.12 & 2189 & 45.19 \\ \cline{1-1}  & 21 & 2328 & 49.44 & 2326 & 48.46 & 2211 & 45.97 \\ \cline{1-1}  & 28 & 2315 & 50.61 & 2283 & 49.59 (\(-2\%\)) & 2199 & 46.56 (\(-7.9\%\)) \\ \hline \hline \end{tabular}
\end{table} 
Table 8: _Cont._C30/37 concrete grade. As stated by [PERSON] et al. [21], this is mainly due to the higher presence of fine particles, which have higher water absorption values. From these data, it is possible to state that all the products obtained from the treatment of the returned concrete (aggregates, washing water and cement sludge) can be used for concrete production. In particular, the combined use of recycled aggregates and microfiltered water guarantees mechanical performance similar to that of a traditional concrete. Similarly, the use of cement sludge as filler (partial substitute of the natural aggregate fine fraction) allows to obtain concrete mixtures with good mechanical performance. This is demonstrated by the fact that at 28 curing days, all the mixtures guaranteed mechanical performance higher than the desired design strength.
###### Abstract  
The Internet of Things (IoT) has revolutionized modern communication systems by enabling seamless connectivity among low-power devices. However, the increasing demand for high-performance wireless networks necessitates advanced frameworks that optimize both energy efficiency (EE) and security. Cell-free massive multiple-input multiple-output (CF m-MIMO) has emerged as a promising solution for IoT networks, offering enhanced spectral efficiency, low-latency communication, and robust connectivity. Nevertheless, balancing EE and security in such systems remains a significant challenge due to the stringent power and computational constraints of IoT devices. This study employs secrecy energy efficiency (SEE) as a key performance metric to evaluate the trade-off between power consumption and secure communication efficiency. By jointly considering energy consumption and secrecy rate, our analysis provides a comprehensive assessment of security-aware energy efficiency in CF m-MIMO-based IoT networks. To enhance SEE, we introduce a hybrid deep-learning (DL) framework that integrates convolutional neural networks (CNN) and long short-term memory (LSTM) networks for joint EE and security optimization. The CNN extracts spatial features, while the LSTM captures temporal dependencies, enabling a more robust and adaptive modeling of dynamic IoT communication patterns. Additionally, a multi-objective improved biogeography-based optimization (MOIBBO) algorithm is utilized to optimize hyperparameters, ensuring an improved balance between convergence speed and model performance. Extensive simulation results demonstrate that the proposed MOIBBO-CNN-LSTM framework achieves superior SEE performance compared to benchmark schemes. Specifically, MOIBBO-CNN-LSTM attains an SEE gain of up to 38% compared to LSTM and 22% over CNN while converging significantly faster at early training epochs. Furthermore, our results reveal that SEE improves with increasing AP transmit power up to a saturation point (approximately 9.5 Mb/J at \(p_{\text{AP}}^{\text{max}}=500\) mW), beyond which excessive power consumption limits efficiency gains. Additionally, SEE decreases as the number of APs increases, underscoring the need for adaptive AP selection strategies to mitigate static power consumption in backhaul links. These findings confirm that MOIBBO-CNN-LSTM offers an effective solution for optimizing SEE in CF m-MIMO-based IoT networks, paving the way for more energy-efficient and secure IoT communications.  
Footnote †: journal: Future Internet  
_Article_
## 3 Overview of Hyperspectral Imaging Spectroscopy (HSI)
### Multivariate Image Analysis  
One hypercube may contain over 200,000 spectra, necessitating multivariate image analysis (MIA) techniques [73]. MIA can be used on raw or preprocessed images; it is applied to the hypercube in a specific sequence and repeated several times with changes until the best classification model is found. Typically, the image analysis sequence begins with image cleaning, which includes removing unwanted backgrounds and correcting shade effects. The image is preprocessed to reduce noise, increase the signal-to-noise ratio, and remove unnecessary information before using MIA. Errors in an image can be caused by optical-related errors, sample presentation errors, and dead pixels in the detectors and background shade. Removing these image errors before processing is critical to obtaining accurate information from the dataset and making good predictions. The image cleaning is followed by exploratory analysis using principal component analysis (PCA). PCA is a linear transformation tool that reduces the dimensionality of an image dataset without losing much of the data's critical information variance [70]. PCA reduces the number of dimensions while retaining most of the data's informative variance. The new variables are known as principal components (PCs), and they correspond to the covariance matrix's largest eigenvalues, accounting for the most significant possible variance in the data. The development of a regression model follows PCA. Regression techniques used in NIR spectroscopy, such as MLR, PCR, and PLS for regression models, are also used on hyperspectral images [70].  
Generally, a successful MIA relies on how effective the image preprocessing and cleaning steps are implemented. MIA is a powerful tool for understanding and evaluating physical and chemical compositions in a sample matrix and their spatial distribution.
## 6 General Overview
Risk coefficients (RQ) value based on the ratio of the Predicted/Measured Environmental Concentration (PEC/MEC) and Predicted No-Effect Concentration (PNEC) was performed in three papers for endocrine disruptors [61,68,69] and in one for pharmaceuticals, pesticides, and other CECs [26]; the fate of pesticides in sediments and risk assessment according to their physico-chemical properties was discussed in one paper [44];
* Spatial distribution of the contaminants was highlighted in several publications [45, 49,53,61,100];
* Pollution emission sources were investigated for PAHs [96], OPCs [94], pharmaceuticals [51,53], and heavy metals [106,126,128,156]. Untreated and inadequate treated waste water was demonstrated as being the main source of organic pollution in the low Danube basin. The metals pollution is associated with industrial and municipal sources;
* Seasonal variations were reported for all contaminants classes, probably due to the temperature related processes of biotransformation and absorption. Similar phenomena were reported for pharmaceuticals in Swedish aquatic environment [157] and for herbicide and insecticide in surface waters in Spain [158];
* The Dniester River is one of the less-studied rivers in Europe;No report was identified concerning pharmaceuticals residues in seawater or sediments for the North-West Black Sea coast;
* Studies were heterogeneous and, generally, did not allow comparisons;
* The pollution level in the Lower Danube basin was in agreement with other European rivers such as the Rivers Elbe (Germany) [159], Lis (Portugal) [160], or Po (Italy) [161];
* Future research should be conducted in the investigation on the effect of emerging pollutants mixtures to different biological systems, on the development of bacterial resistance, and the fate of CECs in the environment (transport, bioaccumulation, degradation). Effective wastewater treatment and reliable fate and toxicity assessment are needed.
# Special Issue on Algorithms for Scheduling Problems
## 2 Special Issue
They concentrate on a complex multi-stage, multi-product, multi-machine, and batch production environment considering completion time and energy consumption optimization criteria. The proposed bi-objective algorithm is based on the Non-Dominated Sorting Genetic Algorithm II (NSGA-II). To tune it up, the authors apply a statistical analysis of multi-factorial variance. A branch-and-bound algorithm is used to evaluate the heuristic algorithm. To demonstrate the practical relevance of the results, the authors examined their solution on real data. The paper [10] deals with an image processing work-flow scheduling problem on a multi-core digital signal processor cluster. It presents an experimental study of scheduling strategies including task labeling, prioritization, and resource selection. The authors apply the above strategies as executing the Ligo and Montage application. A joint analysis of three conflicting goals based on the performance degradation provides an effective guideline for choosing a better strategy. A case study is discussed. The experimental results demonstrate that a pessimistic scheduling approach works better than an optimistic one and leads to the best optimization criteria trade-offs. The pessimistic heterogeneous earliest finish time (PHEFT) scheduling algorithm performs well in different scenarios with a variety of workloads and cluster configurations. The developed PHEFT strategy also has a lower time complexity in comparison with earlier versions known as HEFT (heterogeneous earliest finish time first). The last paper accepted for this special issue is [11], which addresses a scheduling problem, where jobs with given release times and due dates have to be processed on a single machine. The primary criterion of minimizing the maximum lateness of the given jobs makes this problem strongly NP-hard. The author proposes a general algorithmic scheme to minimize the maximum lateness of the given jobs with the secondary criterion of minimizing the maximum completion time of the given jobs. The problem of finding a Pareto optimal set of solutions with the above two criteria is strongly NP-hard as well. The author states properties of the dominance relation along with conditions when a Pareto optimal set of solutions can be found in polynomial time. The proven properties of the dominance relation and the proposed general algorithmic scheme provide a theoretical background for constructing an implicit enumeration algorithm that needs an exponential running time and a polynomial approximation algorithm. The latter allows for the generation of a Pareto sub-optimal frontier with a fair balance between the above two criteria. The guest editors would like to thank the authors for submitting their high-quality work to this special issue, the reviewers for their timely constructive comments, and the editorial staff of the MDPI journal _Algorithms_ for their assistance in managing the review process in a prompt manner and for their continuous support whenever needed. The authors declare no conflict of interest.
## 3 Building Energy Consumption Forecasting
### Accuracy Metrics  
A variety of accuracy metrics are used to validate the forecasting models, the most used being (see Figure 4) coefficient of variation of root mean square error (CVRMSE), mean absolute percentage error (MAPE), mean absolute error (MAE), root mean squared error (RMSE) and determination coefficient (R\({}^{2}\)). Each of these metrics has a particular function. CVRMSE standardizes the expectation error and can give a helpful unitless metric. MAPE shows rate accuracy and decreases the impact of absolute errors caused by singular exceptions [118]. MAE depends on the absolute error and can show the normal separation between the anticipated value and the real value. RMSE is generally proposed as an expectation quality estimation and it is frequently used to analyze the precision of various anticipating standards [119]. The measurement punishes huge errors because it mathematically enhances mistakes [120]. R\({}^{2}\) assesses how a model approximates the real information, giving a metric of the consistency of the model [121].  
Other accuracy metrics used by several researchers are normalized mean absolute error, expect error percentage, mean Absolute, relative error, mean square error, percentage of bias, weight absolute percentage error, and Pearson correlation coefficient.  
Figure 3: Building energy consumption forecasting horizon.
## 3 Results
### Rainfall Correlations (Influent and Effluent)
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Parameter** & **Rainfall** & **Rainfall** & **Rainfall** & **Temperature** & **Temperature** & **Temperature** \\
**Correlation (r)** & \(p\)**-Value** & **Strength** & **Correlation (r)** & **Strength** & \(p\)**-Value** \\ \hline Avg. pH In & \(-\)0.619 & 0.003 & Moderate & \(-\)0.642 & Moderate & 0.002 \\  & & & Negative & \(-\)0.619 & Moderate & 0.003 \\ Avg. pH Out & \(-\)0.476 & 0.029 & Moderate & \(-\)0.142 & Weak Negative & 0.509 \\ Avg. P Tot (mg/L) In & \(-\)0.714 & 0.000 & Strong & Negative & \(-\)0.023 & Very Weak & 0.876 \\ Avg. P Tot (mg/L) Out & \(-\)0.476 & 0.029 & Moderate & \(-\)0.166 & Weak Negative & 0.423 \\ Avg. COD (mg/L) In & \(-\)0.619 & 0.003 & Moderate & \(-\)0.738 & Strong & 0.000 \\ Avg. COD (mg/L) Out & \(-\)0.523 & 0.011 & Negative & \(-\)0.357 & Moderate & 0.138 \\ Avg. BOD\({}_{5}\) (mg/L) Out & 0.238 & 0.292 & Weak Positive & \(-\)0.357 & Moderate & 0.138 \\ Avg. TSSs (mg/L) In & \(-\)0.214 & 0.406 & Weak Negative & 0.38 & Moderate & 0.231 \\ Avg. TSSs (mg/L) Out & 0.095 & 0.693 & Very Weak & 0 & Very Weak & 1.000 \\ Avg. N-Total (mg/L) In & \(-\)0.809 & 0.000 & Strong & \(-\)0.642 & Strong & Negative & 0.002 \\ Avg. N-Total (mg/L) Out & \(-\)0.785 & 0.000 & Strong & \(-\)0.69 & Strong & Negative & 0.002 \\ Avg. NH\({}_{4}\) (mg/L) In & \(-\)0.619 & 0.003 & Moderate & \(-\)0.619 & Moderate & 0.003 \\  & & & Negative & Negative & Negative & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Qualitative strength of Spearman’s correlation coefficients among water quality parameters.
## 3 Proposed Scheme
### Overview of Proposed SDPCACM Corresponding to NDN
#### 3.2.3 Intra-Domain Handover Operating Scenario  
This section describes the handover handling scenario of SDPCACM to the NDN. In NDN, an MC only needs to know what packet they are requesting from the provider, not who provided it. Consider an MC watching a real-time video in a state of mobility at attachment point-1 (AP-1) provided by NAR-1, during this time their position is changing and moving from AP-1 to AP-2 and later from AP-3 to AP-4, as shown in Figure 5. The following steps will be required.  
Before handover: in NDN architecture, a user MC want to watch a video, first must issue an interest packet and then the corresponding data packet is sent back to the MC. These data packets are stored in the CS of each intermediary router that delivers them [29]. Step 1: when the impending movement event is detected, the user, i.e., the MC, receives a control interest packet from NAR-1. The handover indication bit is then set to 1 by NAR-1, which also adds a handover field to the appropriate PIT entries. Following that, NAR-1 determines if a given PIT entry matches a data packet when it is received. The received data packet is cached by NAR-1 if the handover indication bit is set to 1 [29]. Step 2: in the meantime, NAR-1 has sent the control interest packet that contains all of the above information to the SDN controller. Thus, the SDN has a clear scope of the state of the entire movement.  
During handover: as NAR-1 is aware of MC's movement and has particular PIT entries, the specific data packets that come into NAR-1 are not forwarded to the CS and are only cached. In this way, packets are prevented from being lost on the route to the MC's old location [28]. Meanwhile, NAR-1 sends a control interest packet (type: handoff initiation interest message) to the SDN consolidated controller. Furthermore, the SDN has a clear reach of the state of the fully updated movement toward the NAR-2.  
Step 3: The controller within the SDN chooses the flow entry, hence, an updated routing table is shared with the NAR-2 to update the significant FIB entries of the routers providing this packet, triggering proactive caching actions on NAR-2. Therefore, NAR-2 actively initiates requesting and caching data packets previously stored in NAR-1 through the normal exchange of interest packet/data packet. In this way, NAR-2 proactively caches and receives the rest of NAR-1\({}^{\prime}\)s contents.  
Completing handover: Step 4: when the MC reaches within range of NAR-2, i.e., AP-3, it establishes a connection with NAR-2, transmits a handover complete message to NAR-2, and NAR-2 resends the remaining data packets to them. As a result of this command, the packet is sent to a new attachment point, i.e., AP-3 through NAR-2 from NAR-1 and is simply received by MC without any communication delay.  
Figure 5: Single SDN controller or intra-domain handover operating scenario.
## 4 Parameters Analytical Calculation Model
### Turbine Total Adiabatic Efficiency Calculation Model  
In this case, the calculation model is available for a free turbine turboshaft with a gas generator spool and a power turbine spool.  
Regarding the expansion process, from the enthalpy-entropy diagram presented in Figure 6, the turbine adiabatic total efficiency is also determined based on the entropy equality conditions [11].  
Related to the turbine instrumentation method presented above, and according to the established measurement parameters, the total adiabatic efficiency calculation model is defined by the steps below. Depending on the location of the measured gas temperature, as mentioned above, the inlet turbine gas temperature can be determined from the combustor chamber energy equation [11] in the next form:  
\[h_{3.t}(T_{3.t})=\Big{(}h_{2.t}(T_{2.t})+f_{fc}\cdot LHV\cdot\eta_{ca}\Big{)}/ \Big{(}1+f_{fc}\Big{)}\ \ \ \Rightarrow\ \ h_{3.t}(T_{3.t}) \tag{46}\]  
\[h_{3.t}(T_{3.t})=i_{g}(T_{3.t})\ \ \Rightarrow\ \ T_{3.t} \tag{47}\]Therefore, it is necessary to measure the fuel flow (\(F_{f}\)) and to know the air mass flow (\(M_{af}\)) because with these parameters, the fuel flow coefficient (\(f_{fc}\)), and the gas flow (\(G_{f}\)) are determined.  
\[f_{fc}=F_{f}/M_{af}=1/(\alpha_{ex}\cdot\min_{L}) \tag{48}\]  
\[G_{f}=M_{af}+F_{f} \tag{49}\]  
In case the engine has the inlet turbine temperature measured, and the turbine is on the same spool with the compressor, it is no longer necessary to measure the total outlet temperature, as this temperature can now be calculated. In this case, the calculation model is as follows:  
\[h_{3,t}=i_{g}(T_{3,t})\ \ \Rightarrow\ \ h_{3,t} \tag{50}\]  
\[p_{3,t}=p_{2,t}\cdot\sigma_{\alpha,t}\ \ \Rightarrow\ \ p_{3,t} \tag{51}\]  
\[\pi_{T,t}=p_{3,t}/p_{4,t}\ \ \Rightarrow\ \ \pi_{T,t} \tag{52}\]  
\[s_{3,t}=s_{g}(T_{3,t})-R_{g}\cdot\ln(p_{3,t}/p_{0})\ \ \Rightarrow\ \ s_{3,t} \tag{53}\]  
\[l_{T,t}=l_{C,t}/\left(\eta_{m}\cdot\left(1+f_{fc}\right)\right)\ \ \Rightarrow\ \ l_{T,t} \tag{54}\]  
In a particular case, for a free turbine turboshaft with a gas generator spool and a power turbine spool, the total specific actual work (\(l_{T,t}\)) on the whole turbine is the sum between the specific actual work for the gas generator turbine and the free turbine.  
\[l_{T,t}=l_{TG,t}+l_{TP,t} \tag{55}\]  
Considering that the power (\(P_{TP}\)) is measured on the free turbine shaft, the total specific actual work (\(l_{T,t}\)) is calculated on the free turbine.  
\[l_{TP,t}=\frac{P_{TP}}{G_{f}  
\[s_{g}(T_{4.t.id})=s_{3.t}+R_{g}\cdot\ln(p_{4.t}/p_{0})\ \ \ \Rightarrow\ \ T_{4.t.id} \tag{60}\]  
\[l_{T.t.id}=h_{3.t}-h_{4.t.id}=h_{3.t}-i_{g}(T_{4.t.id}) \tag{61}\]  
\[\eta_{T.t}=l_{T.t}/l_{T.t.id}\ \ \ \Rightarrow\ \ \eta_{T.t} \tag{62}\]  
In the case of the second instrumentation method, the model calculation for the turbine total adiabatic efficiency is similar to the compressor model and follows the same steps, but it uses the parameters from the outlet and inlet turbine stations.
## 2 System Model and Connection Setup  
Let us refer to an underwater link between two nodes, namely \(u_{1}\) and \(u_{2}\), where data communication can be potentially bidirectional. Therefore, \(u_{1}\) and \(u_{2}\) can act as both transmitter and receiver. The transmission is considered to be OFDM-based and frame oriented, with frame length defined as \(T_{f}\). By definition, each frame is then organized in slots of duration equal to \(T_{s}\). Due to the time-varying nature of the underwater acoustic channel, we assume \(T_{f}\) to be shorter than the channel coherence time \(T_{coh}\), so that \(T_{f}\leq T_{coh}\).  
The interaction between \(u_{1}\) and \(u_{2}\) is organized in two different phases, referred to as connection setup (CS) and established connection (EC), respectively, described in Figure 1. During CS, both the nodes are involved in interference and channel acquisition, including the evaluation of the statistical properties of both interference and channel, necessary for the initial communication setup and for managing the whole communication. The EC stage instead concerns data transmission and detection, with some (small) time intervals dedicated to refreshing the channel and interference statistics. The stages of CS and EC are detailed in the following:  
Before starting the description of the whole signaling, we report in Table 1 all the time interval definitions we use in this work.  
Figure 1: Graphical description of the three phases for interference and channel estimation and data transmission/detection during CS and ES.