esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy-Efficient On-Off Power Control of Femto-Cell Base Stations for Cooperative Cellular Networks
## 1 Introduction  
### Energy Efficiency Problem in MCS  
Recently, the environmental pollution caused by CO\({}_{2}\) emission has become one of the biggest threats to humanity. Given that the generation of electricity is the dominating source of CO\({}_{2}\) emission, a lot of effort has been made in order to reduce the use of electricity in all areas including telecommunication systems [1]. Developing energy efficient mobile communication systems (MCSs), i.e., green cellular networks (GCNs), is important in this regard because the amount of electricity consumed by the information and communication infrastructure increases nearly 20% per year [1; 2]. Moreover, improving energy efficiency (EE) of MCSs is also important because the amount of mobile traffic is expected to increase exponentially due to the widespread use of Internet of Things (IoT) technology [3].  
As a consequence, various methods have been devised to improve the EE of MCSs [4; 5; 6; 7; 8; 9; 10]. The energy harvesting (EH) from radio frequency (RF) signals has recently been investigated, in which RF signals are converted into energy that can be used for data processing and transmission [11; 12]. Energy efficient clustering of FBS using coordinated multi-point (CoMP) has been taken into account in [5]. The effective frequency resource allocation scheme with interference mitigation and energyefficient power allocation for two-tier Orthogonal Frequency-Division Multiple Access (OFDMA) femtocell networks was considered in [8; 9], respectively. The authors of [7] investigated the energy efficient issues of cognitive radio networks (CRNs) where energy efficient spectrum sensing, resource allocation, and deployment of CRN were taken into account. Note that aforementioned works did not consider on-off power control of FBSs, which is described in the next section.
# Novel Materials for Heavy Metal Removal in Capacitive Deionization
## 2 A Brief Review of Electrochemical Methods
The major merits of these materials are their high stability and long lifetimes even in low-pH solution; thus, these anodes provide an attractive alternation for EF. Electrodialysis (ED) is used to transport metal ions between two solutions along ion-exchange membranes under the influence of an applied electric potential difference. ED is an established technology in treating industrial wastewater; wastewater from the drug and food industries; table salt production; chemical processes; and HMs removal [22; 39]. It is conducted in a configuration called an ED cell [40]. The ED process can purify water without phase change, reaction, or chemicals [41]. These merits provide environmental benefits without the use of fossil fuels or chemical detergents. However, ED technology has limitations, including scaling, membrane fouling, energy consumption, and selectivity [42; 43]. ED has been investigated for the removal of Cr(VI) [44; 45], Cu(II) [46], Zn(II) [47], Ni(II) [48], and Ag(I) [49]. ED systems necessitate excellent ion exchange membranes with lower electrical resistance, mechanical durability, improved permeability, and higher selectivity for specific ions. Production costs have also come under consideration [32], which is discussed in this paper's section on membranes. Electrodeposition (EP) is a practical method that relies on the redox reduction of anodes alone. Heavy metal ions are reduced and deposited on an electrode in solid form. This process allows the simultaneous removal and recovery of HMs from wastewater [50; 51]. High removal and recovery efficiency have been reported for the disposal of As(V)-, Cr(VI)-, and Cu(II)-containing wastewaters [51; 52]. However, the reaction degree and side reactions such as water splitting were hard to control due to high energy dissipation [25]. Table 1 summarizes the application of EC, EF, and EP for heavy metal removal. Capacitive deionization (CDI) is among emerging separation technologies for removing charged ions from wastewater using electrochemical principles on electrode surfaces. Figure 2 shows the mechanism of CDI for HM removal. Ions are removed from the aqueous solution and are stored on the internal surface areas of the porous electrodes; this results in an effluent product stream with reduced ion concentration. Moreover, ions can be reversed into solution by reducing or even reversing the applied voltage, resulting in a product stream concentrated in ions. In principle, CDI is based on the electrosorption of cations and anions on the electrode surface captured by electrical double layers [77]. Generally, the electrical properties of electrodes govern the adsorption capacity, chiefly specific capacitance, and their surface areas are believed to be closely related to the removal efficiency
## 4 Experimental Verification
### Pressure Relief Hole Identification Test
#### 4.3.2 Results and Analysis
Considering that the target recognition of the pressure relief hole has a high requirement for the recall rate, the recognition model with the above two improved strategies shows a better comprehensive performance. Figure 14: Change curves of the loss function of the improved SqueezeNet + BN-based network with different measures: (**a**) classification loss of the fully connected layer; (**b**) regression loss of the fully connected layer. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Index** & **SqueezeNet** & \
\begin{tabular}{c} **Improved** \\ **SqueezeNet** \\ \end{tabular} &
\begin{tabular}{c} **Improved** \\ **SqueezeNet + BN** \\ \end{tabular} \\ \hline Number of identified samples & 813 & 854 & 905 \\ Number of missed samples & 315 & 274 & 223 \\ Number of false samples & 252 & 149 & 117 \\ Accuracy rate/\% & 76.34 & 85.14 & 88.55 \\ Recall rate/\% & 72.07 & 75.71 & 80.23 \\ Average training time/s & 153 & 155 & 160 \\ Average recognition time/s & 0.11 & 0.11 & 0.11 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Relief hole target recognition results based on different SqueezeNet models. 3. Test III: Explore the performance of basic network models based on improved SqueezeNet, VGG-16, and ZF-Net in pressure relief hole identification. The parameter settings of the three models are the same, and the comparison results are shown in Table 5. It can be seen that the designed Faster R-CNN model with improved SqueezeNet is superior to the other two models in terms of accuracy rate, training time, and recognition time. Although the recall rate (98.32%) is slightly lower than the VGG-16 model (98.67%), the comprehensive performance of the proposed recognition model is optimal, and is more suitable for rapid and accurate recognition of pressure relief holes in coal mine images.
## 3 Results and Discussion
### Nature of the Antimicrobial Compounds Contained in CFS
#### 3.5.2 Antimicrobial Peptide Production by the Cell-Free Supernatants  
The antimicrobial activity of the CFS after a protease treatment with proteinase K and popsin was performed by the agar well diffusion assay against _L. monocytogenes_ and _L. parabuchneri_. The original antimicrobial activity achieved with the CFSs of the three tested LAB strains was absent when the CFSs were treated with proteases. This suggests that the antimicrobial activity of these three strains is likely attributed to their ability to produce bacteriocins, as observed for other strains of these species. The ability of _P. acidilactici_ to produce bacteriocins was supported by the findings of [PERSON] et al. [76] that described the production of pedicion PA-1 in both _P. acidilactici_ and _P. pentosaccus_. Similarly, [PERSON] et al. [77] substantiated the bacteriocin-producing capacity of _L. plantarum_ RUB 1 via bacteriocin-synthesis-related genes, while [PERSON] et al. [78] and [PERSON] et al. [74] supported the BLIS production in _L. brevis_.  
The protease-treated CFS of the _B. subtilis_ strain CNTA 517 achieved an inhibition zone diameter of 26.40 \(\pm\) 0.69 mm, indicating no statistically significant differences (\(p\) > 0.05) compared to the untreated CFS. A subsequent proteolytic step was conducted on the _B. subtilis_ CFS with the enzyme pancreatin and the commercially available protease mix PROTAMEX, but no further reduction of the inhibition zone diameter was achieved. Although _B. subtilis_ is known to produce over 20 bacteriocins with distinct functional and structural properties [79], in this case, the nature of the antimicrobial activity remained undetermined, and, therefore, the WGS of this strain was performed.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Strain**} & \multirow{2}{*}{**CFS pH**} & \multicolumn{2}{c}{**Halo Diameter (mm)**} \\ \cline{3-4}  & & **CFS pH 3.50** & **CFS pH 7.00** \\ \hline _Pediococcus acidilactici_ CNTA 1059 & 4.33 & 23.27 \(\pm\) 0.72 & 23.10 \(\pm\) 0.56 \\ _Lactiplantibacillus plantarum_ CNTA 600 & 4.44 & 20.14 \({}^{*}\) + 0.95 & 19.32 \({}^{*}\) \(\pm\) 0.26 \\ _Levilactokallus brevis_ CNTA 1374 & 5.06 & 31.89 \(\pm\) 0.46 & 31.52 \(\pm\) 0.12 \\ _Bacillus subtilis_ CNTA 517 & 6.77 & 14.93 \({}^{*}\) \(\pm\) 0.01 & 28.98 \(\pm\) 0.08 \\ \hline \hline \end{tabular} \({}^{*}\)Significantly different from native pH halo size (\(p\) \(\leq\) 0.05).  
\end{table}
Table 3: Antimicrobial activity of the cell-free supernatants (CFSs) at pH 3.50 and 7.00.
# Passive Planar Microwave Devices
###### Abstract  
Passive planar circuits play a key role in many RF/microwave applications, such as in wireless communications, medical instrumentation, and remote sensing. From their earliest developments during World War II to the present day, they have become indispensable for their low cost and low weight while maintaining high performance. As a result, they are still undergoing research and development. In recent years, multiple technologies have been proposed with the aim of combining the characteristics of traditional planar and nonplanar transmission lines, highlighting substrate integrated waveguide (SIW) technology as the most popular among them.  
This Special Issue is focused on highlighting recent contributions to microwave device development in planar technologies. A total of twelve papers have been published in this volume, each addressing several important research problems and advancements in the field of filters, multiport circuits, dividers, combiners, couplers, multiplexers, microwave sensors, and antennas. These articles provide a significant contribution to the state of the art.
## 3 Results
### PC Retrieval Results and Validation  
Selecting automatically monitored data with good weather conditions and the corresponding satellite overpass times reduced systematic errors caused by data mismatches. The samples were randomly divided into a 6:4 ratio using cross-validation, with 60% utilized for model training and the remaining data used to assess the generalization ability of  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Band/Spectral Index** & **R** & **Band/Spectral Index** & **R** & **Band/Spectral Index** & **R** \\ \hline B1 & 0.633 ** & B2/B1 & \(-\)0.222 & NDVI & \(-\)0.400 ** \\ B2 & 0.500 ** & B2/B3 & \(-\)0.422 & NDWI & 0.071 \\ B3 & 0.640 ** & B2/B4 & \(-\)0.538 & NDWI & 0.146 \\ B4 & 0.646 ** & B2/B5 & \(-\)0.346 & (B2 + B8)/B4 & \(-\)0.651 ** \\ B5 & 0.482 ** & B4/B3 & 0.500 & (B3 + B8)/B4 & \(-\)0.644 ** \\ B6 & 0.206 & B4/B8 & 0.401 & B6/B1 + B3/B4 & \(-\)0.615 ** \\ B7 & 0.140 & B5/B6 & 0.423 & B6/B1 + B2/B4 & \(-\)0.630 ** \\ B8 & 0.221 & B5/B7 & 0.503 & B7/B1 + B3/B4 & \(-\)0.615 ** \\ \hline \hline \end{tabular} Where ** correlation is significant at the 0.1 level.  
\end{table}
Table 4: Statistical analysis of band and spectral index correlations with PC concentrations. NDVI stands for Normalized Difference Vegetation Index; NDWI stands for Normalized Difference Water Index; MNDWI refers to Modified Normalized Difference Water Index.  
each PC concentration retrieval model. The retrieval results for each model are presented in Table 5, while the scatter plot depicting the retrieval values against the measured values for model validation is shown in Figure 5.  
The results indicate that both PSO-BPNN and BPNN outperformed SVR in terms of retrieval performance. This could be attributed to SVR's heightened sensitivity to noise in the data [40]. The objective of SVR regression is to maximize the margin while maintaining the predicted values within a certain range. However, noisy data can have a greater impact, leading to overfitting and insufficient generalization during model training. Compared to all the machine learning models, BPNN showed better performance in terms of RMSE. However, the model displayed underestimation when the PC concentration exceeded 2.5 \(\upmu\)g/L, resulting in suboptimal fitting. PSO-BPNN improved the model's fitting capability by 17% compared to BPNN and outperformed SVR by 36.5%. The predicted values closely matched the measured values, predominantly distributed around the 45-degree line, showing a better fit as well as stronger generalization capabilities (R\({}^{2}\) = 0.703, MAE = 0.469, RMSE = 0.615). This illustrates that PSO can optimize BPNN, mitigating the problem of getting stuck in local minima and providing a distinct advantage for low PC concentration remote sensing retrieval in complex coastal optical environments. Consequently, PSO-BPNN was chosen as the optimal PC retrieval model.
# A Cyber-Physical System for Wildfire Detection and Firefighting
## 3 Materials and Methods
### Cyber-Physical System for Wildfire Detection and Firefighting  
The concept of the CPS, originally proposed by NASA for unmanned space exploration, extended to the military environment to reduce military casualties by allowing soldiers to remotely control weapons from a command post. Nowadays, CPS has been widely adopted in critical sectors such as transportation, energy, healthcare, and manufacturing as part of _Industry 4.0_, where human-machine Interfaces are widely utilized to enhance productionprocess performance while simultaneously decreasing the occurrence of emergencies and accidents [37; 38]. The CPS is an extension of control systems and embedded systems and has evolved rapidly due to advancements in cloud computing, sensing technology, communication, and intelligent control. The fundamental principle of the CPS involves integrating cyber processes such as sensing, communication, computation, and control into physical devices to monitor and manage the physical world. In the context of Cyber-Physical Systems (CPSs), effective communication is essential for facilitating seamless coordination and integration among various components. Machine-to-Machine (M2M) communication stands out as a significant communication paradigm within CPS. M2M communication specifically refers to the direct exchange of data between sensors, machines, robots, and other devices without requiring human intervention. After taking into account the previous considerations and current research on forest prevention, a new solution has emerged in the form of a CPS designed for wildfire detection and firefighting. This innovative design aims to provide firefighters with a tool that can better support their daily activities and prevent fires at an early stage. The CPS for wildfire detection and firefighting will incorporate advanced technologies such as machine learning, sensors, and autonomous systems. These components will work together to detect and fight wildfires in real time, allowing firefighters to respond quickly and effectively. The system will also provide valuable data and insights that can be used to improve future prevention efforts. For example, the data collected can be analyzed to identify areas that are at high risk for wildfires, and preventive measures can be taken to reduce the likelihood of fires starting in those areas. Overall, the CPS for wildfire detection and firefighting could potentially represent a significant step forward in the fight against wildfires. By leveraging the latest technologies and research, firefighters will be better equipped to prevent and respond to fires, ultimately helping to protect communities and preserve natural habitats. In the upcoming section, there will be a comprehensive overview of the methods and tools used to design and develop cyber-physical systems for wildfire detection and firefighting.
# Dealing with Gender Bias Issues in Data-Algorithmic Processes: A Social-Statistical Perspective
## 4 Examples of Gender Bias
### Natural Language Processing and Generation  
Most of the research looking at gender biases in AI and algorithms has been conducted in the field of computational linguistics. The goal is to avoid biases in the training of those algorithms involved in the development of voice assistants.  
Gender bias is susceptible of being exhibited in multiple parts of an NLP system, including training data, resources, pre-trained models (for example, word embeds), and the algorithms themselves. One of the first works to point out gender stereotypes within NLP was the one carried out by [36] on Word2 Vec. For these authors, the blind application of ML runs the risk of amplifying the biases already present in the data. Such a risk confronts us with word embedding, a popular framework for representing text data as vectors that has been used in many NLP and ML tasks. From here, they define gender bias as the correlation between the magnitude of the projection in the gender subspace of an embedded word representing a gender-neutral word and the bias rating of that word, as rated by crowd workers. In their research, they identified problematic stereotypical representations of men and women. Ref. [8] adopt the core concept of the implicit association test (IAT), which is used in psychology to measure bias in word embedding. The authors confirm in their work that there are gender biases found through the IAT test in the GloVe and Word2 Vec embedding tools, highlighting the stereotypical association of masculinity and femininity with sciences and arts respectively. The previous studies show that word embedding learns from large corpus of text available online, and that the presence of gender biases in them might be a reflection of underlying biases in the society.
# On the Evaluation of the NB-IoT Random Access Procedure in Monitoring Infrastructures
## 2 State of the Art
### NarrowBand--Internet of Things
Specifically, the end device is considered in a specific enhanced coverage level if its measured Reference Signal Received Power (RSRP) is less than the RSRP threshold configured for that enhanced coverage class. According to the 3 GPP specification [30], the number of coverage classes is configurable by system information. Let \(n\) be the number of available coverage classes so that \(1\leq n\leq 3\). Different coverage classes correspond to operation with different modulation orders, coding rates, repetition factors, and subcarrier spacings, in order to match the data rate for each user to its available link budget. This allows devices having good coverage to operate at higher data rates and with lower latency than devices that have poor coverage. Therefore, the system is designed to meet the throughput and latency requirements for devices in _Extreme_ coverage, while devices in _Normal_ or _Extended_ coverage achieve improved performance. NB-IoT generally takes advantage of the existing LTE physical channels, revised properly to fit into the narrower bandwidth [31]. In the downlink direction, there are three channels:  
* Narrowband Physical Downlink Control Channel (NPDCCH) carries scheduling assignments, as well as Hybrid Automatic Repeat reQuest (HARQ) acknowledgments, paging indication, and system information update. It is considered as the core element of the scheduling procedure as it carries Downlink Control Indicators (DCIs), which contain uplink scheduling grants, downlink scheduling assignments, and the type of modulation. * Narrowband Physical Downlink Shared Channel (NPDSCH) carries data from the higher layers as well as paging message, system information messages, and the Random Access Response (RAR) message. It is scheduled by the NPDCCH but is transmitted after a certain time delay (indicated in the NPDCCH too) to allow the low-complexity NB-IoT devices enough time to decode the NPDCCH. * NarrowBand (MIB-NB) over eight consecutive radio frames, repeated 8 times to cope with extreme coverage conditions; the MIB-NB content remains, therefore, unchanged for 640 ms.  
On the other hand, for the uplink direction, there are two different channels, specifically:  
* Narrowband Physical Uplink Shared Channel (NPUSCH) has two formats. Format 1 is used for carrying uplink data and uses the same LTE error correction code. Format 2 is used for signaling HARQ acknowledgment for NPDSCH and uses a repetition code for error correction. * Narrowband Physical Random Access Channel (NPRACH) enables the random access procedure. Thus, it is described in details in the following section. Figure 1 shows a representative time-frequency structure of NB-IoT uplink channels when the subcarrier spacing of the NPUSCH is set to 15 kHz. As regards synchronization signals, Narrowband Primary Synchronization Signal (NPSS) and Narrowband Secondary Synchronization Signal (NSSS) are used by an NB-IoT device to perform cell search, which includes time and frequency synchronization and cell identity detection.
## 3 Results and Discussion
### Effect of Compression Direction on Start Performance of Inlet
The inverted inlet layout also shows a higher mass flow rate (0.270 kg/s) than the normal inlet layout (0.155 kg/s). This indicates that the inverted configuration is capable of capturing a greater amount of airflow, which can be attributed to reduced high-energy flow spillage. Both configurations have the same mass-weighted average Mach number (1.66). This implies that the Mach number is unaffected by the configuration. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type of Inlet** & \(\sigma_{inlet}\) & \(\overset{\bullet}{\mathbf{m}}_{air}\) & **Ma** \\ \hline Normal inlet & 0.296 & 0.155 & 1.66 \\ Inverted inlet & 0.802 & 0.270 & 1.66 \\ Difference (\%) & +171.62\% & +74.19\% & 0\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Overall inlet unsstart performance comparison at Ma = 3.88. Figure 13: Comparison of unsstart flow regimes for different inlet layouts (Ma = 3.88).
## 4 Results and Discussion
### AM-HDGAT for Text Performance Evaluation  
#### 4.3.1 Classification Results  
Table 4 presents the classification performance of AM-HDGAT for text compared to state-of-the-art methods across multiple text classification datasets.  
AM-HDGAT consistently outperforms baseline methods across all datasets, with an average improvement of 2.23% in the F1-score. The method's effectiveness stems from its ability to capture both local document features through hybrid-dimensional representation and global corpus relationships through graph attention mechanisms.  
Figure 3: The relationship between retrieval consensus and retrieval accuracy in different datasets.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Method** & **NDCG@10** & **MAP** & **Recall@100** & **MRR** \\ \hline Linear Combination & 0.4523 & 0.3821 & 0.7245 & 0.5124 \\ RRF & 0.4687 & 0.3956 & 0.7389 & 0.5289 \\ CombSUM & 0.4612 & 0.3894 & 0.7312 & 0.5198 \\ RCCF (Ours) & **0.4921** & **0.4187** & **0.7623** & **0.5467** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Retrieval performance comparison on BEIR benchmark datasets (averaged across 10 datasets). Bold indicates the best performance.