esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
As interest in WSNs has developed, the technology has been widely implemented in numerous civilian and military applications. Among the challenges are building, implementing, and scaling routing protocols for WSNs due to their inefficiency, resilience, and scalability. However, the limitations and design challenges of WSNs are resolved by employing clustered routing algorithms. In this situation, it is proven that over the years works have been conducted to improve the effectiveness and efficiency of clustering routing protocols in the area of WSNs. Therefore, this study discussed the overview of clustering routing techniques. In addition, a new taxonomy of clustering routing approaches is proposed that focuses on significant clustering properties rather than the clustering's overall objective. Using the defined taxonomy considering essential criteria, a long-term study was conducted to analyze classic WSNs clustering routing algorithms scientifically.  
Identifying and resolving the QoS challenges in routing clustering, particularly in real-time circumstances such as target tracking and emerging event monitoring is important for future research. In the WSN resource-constrained context, limited studies have been conducted on QoS requirements. Further investigation needs to be conducted on the movement or stability of the sink and sensor nodes. Various node mobility applications such as war simulations need a comprehensive analysis of the overhead issues associated with node mobility and topology changes. The growth of network size in WSNs will result in the creation of duplicate data. Therefore, a degree of redundancy is important to increase network reliability. There is still an unresolved issue of whether a trade-off exists between redundancy reduction and redundancy usage.  
The outcome of this study is to motivate more research on WSNs by considering various properties of clustered routing methods. The new protocols should utilize clustered routing technologies effectively. WSNs, informally known as less well-known small networks, have great potential in the implementation of router-assisted clustering routing systems due to their increasing adoption in more complicated applications. For future work, we will study the cross-technology communication, and its applications in heterogeneous WSN, which changes the structure of the WSN topology, which in turn, yields the inefficiency of energy consumption especially with the IoT devices.  
**Author Contributions:** Conceptualization, M.F.A.; methodology, M.F.A.; resources, R.R.; writing--original draft preparation, M.F.A.; writing--review and editing, M.A.M. and R.R.; supervision, M.A.M.; project administration, M.A.M.; funding acquisition, R.R. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work is sponsored by Universiti Tenaga Nasional (UNITEN) under the Bold Re-search Grant Scheme No. J510050002.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Self-Supervised Spatio-Temporal Graph Learning for Point-of-Interest Recommendation
## 4 Experiments
### Experimental Setup
#### 4.1.2 Baselines  
We compared SSTGL with the following ten models, which can be classified according to Table 2:  
* NeuMF [20]: NeuMF is a classical MF-based model that combines matrix factorization and multi-layer perceptron to learn both low-dimensional and high-dimensional embeddings.
* NGCF [21]: NGCF is a GNN-based model capturing high-order information through message passing and aggregation.
* DGCF [22]: DGCF is a GNN-based model, which models different relationships and separates user intents in the representation.
* LightGCN [23]: LightGCN is a GNN-based recommendation model, which simplifies the aggregation step by deleting the weight matrix and activation function.
* SGL [12]: SGL is a graph-based self-supervised method that proposes three data augmentation strategies based on the graph structure.
* NCL [13]: NCL is a graph-based contrastive learning method that improves neural graph collaborative filtering by considering structural and semantic neighbors.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **\#Check-Ins** & **\#POI** & **\#User** & **Sparsity** & **Time Span** \\ \hline Foursquare & 1,196,248 & 28,593 & 24,941 & 99.90\% & April 2012-September 2013 \\ Gowalla & 1,278,274 & 32,510 & 18,737 & 99.87\% & February 2009-October 2010 \\ Meituan & 602,331 & 3182 & 38,904 & 99.51\% & 1 st March 2021–28 th March 2021 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dataset statistics.  
* LGLMF [3]: LGLMF is an MF-based POI recommendation model, which combines logistic matrix factorization with a region-based geographical model.
* STACP [5]: STACP is also an MF-based POI recommendation model, which combines matrix factorization with a spatio-temporal activity-centers algorithm.
* GPR [9]: GPR is a GNN-based model designed for POI recommendation that uses an extra POI-POI graph to learn item embeddings and improve performance.
* MPGRec [24]: MPGRec is the newest GNN-based POI recommendation model, which uses a dynamic memory module to store global information for spatial consistency.  
For all these baselines, we followed the default hyper-parameter settings as stated in their papers. Note that, since our datasets do not have auxiliary information such as social relationships or user attributes, we did not use the methods from [10; 11; 25; 26] that utilize the aforementioned auxiliary information as baselines. In addition, since we focused on the classical POI recommendation task, we did not choose GNN models for the next POI recommendation [27; 28] or tour the recommendations [29] as baselines.
## 4 IoT for Geospatial Research
### Data Sources and Types  
Besides geo-located information, various different data sources were used for enriching the geospatial analysis performed. In 13 papers (23%), data were acquired directly from datasets created/developed in previous projects and research efforts, originating from ground sensors recording traffic flow data [42], taxi ride data [43], or soil fertility data [72]. Other sources involved mobile phone-based crowdsourcing [45; 48], or satellites [33; 35; 36; 49; 50; 58].  
Some authors supplemented their data using web-based sources such as online news, expert-curated discussion, validated official reports [29], unofficial online reports [48], road data, medical facility, information, demographics, and other core operational information for disaster relief scenarios [61], as well as geo-tagged photos [37; 38].  
Offline data were also used, such as governmental data repositories [35; 47], topographic sheets [69] and surveys [65; 67], as well as information of hospital discharges for violent assaults [41].  
Map layers prepared in previous research works were harnessed as well, including digitized vector maps [39], digital thematic maps [34], digital soil maps [35], and digital elevation models and maps [47; 64; 70].  
The nature of data was mostly text (i.e., data measurements from IoT sensors), images (i.e., from mobile phone cameras, satellites, spectrophotometers and fixed cameras in transportation systems), sound (i.e., from mobile phone microphones), video (i.e., from mobile phone cameras and fixed cameras in transportation systems and farms for animal monitoring [59]) and time-series text-based data (i.e., from mobile phones and vehicular sensors for taxi rides [43]).
# Simulation Framework for Detection and Localization in Integrated Sensing and Communication Systems
## 5 Evaluation and Abstraction  
The proposed algorithms are evaluated in a stochastic scenario based on [7], aligned with 3 GPP Indoor Hotspot (InH) specifications [24]. This procedure involves an exhaustive series of simulations in which input parameters are detailed in Table 1. We used the carrier frequency recommended by 3 GPP TR 38.901 in calibration process. Since our scenario is InH, a carrier frequency of 30 GHz was selected to obtain valuable information about the potential of the mmW band in ISAC system.  
The BS location remains constant across all simulations to streamline the simulation process. In a 120 \(\times\) 50 m hall, the UE is positioned at pre-determined distances 32 and 64 m from the BS. These distances represent the typical link lengths within this spatial configuration but in a random direction, ensuring a Line of Sight (LoS) condition.  
A unique set of input parameters characterizes each simulation seed. This includes variations in the number of OFDM symbols (\(L\)) used for channel estimation, different SNR  
\begin{table}
\begin{tabular}{l c} \hline \hline \multicolumn{2}{c}{**Simulation parameters**} \\ \hline Number of simulation seeds (\(J\)) & 30,000 \\ \hline Number of scatterers & 30 \\ \hline Number of OFDM symbols (\(L\)) & \(2^{n}\), \(n=1,2,\ldots 10\) \\ \hline \multicolumn{2}{c}{**Baseline evaluation configuration parameters**} \\ \hline Carrier frequency for evaluation & 30 GHz \\ \hline Sampling frequency & 30.44 MHz \\ \hline Number of sub-carriers (\(K\)) & 2048 \\ \hline BS antenna height & 3 m \\ \hline UE antenna height & 1.5 m \\ \hline Total transmit power per TR \(\times\) P & SNR dependent \\ \hline SNR & \(\{-20,-19,\ldots 20\}\) dB \\ \hline \multicolumn{2}{c}{**Additional parameters for system-level simulation**} \\ \hline Number of BS antenna elements (\(M\)) & 16 \\ \hline Number of UE antenna elements & 1 \\ \hline Hall size & \(120\times 50\) m \\ \hline BS–UE distances (\(d\)) & 32 and 64 m \\ \hline ISAC Channel model & Stochastic [7] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation assumptions considered to evaluate KPIs and develop the lookup tables.  
levels, and two possible distances between the BS and the UE. Outputs, such as distance and position target estimations, are given for these variations.  
For the results analysis, we aim to define the distribution of the estimation errors; for this objective, we have conducted a post-processing methodology that will be described below.
## 4 Conclusions  
In this work, to avoid the aggregation of MWCNTs, well-defined PMMA functionalized the combination of RAFT synthesized MWCNTs (MWCNTs-\(g\)-PMMA) and the clicked reaction. The success of PMMA grafting onto MWCNTs was determined by GPC, Raman spectroscopy, FT-IR, TGA, and TEM measurements. The kinetic reaction results show that the grafting percentage of PMMA chains grafted onto the MWCNTs surface rises with the increase of reaction time. Even at a low temperature (50 \({}^{\circ}\)C), the grafting rate of azide-terminated PMMA is comparatively fast during the click reaction when combining the alkyne-MWCNTs and azide-terminated PMMA in less than 24 h. As calculated by TGA analysis, the highest grafting degree of PMMA chains reaches 21.9%. Compared with the pristine MWCNTs, a thicker diameter of the MWCNTs-\(g\)-PMMA was observed by TEM, which confirmed that the PMMA chain grafted onto the surface of nanotubes. Therefore, the MWCNTs-\(g\)-PMMA could be dispersed and stably suspended in water, even for 24 h.  
Figure 8: TEM photographs of the pristine MWCNTs, MWCNT-COOH, and MWCNTs-\(g\)-PMMA (GP = 22.0%).  
Figure 9: The dispersion images of pristine MWCNTs (A) and MWCNTs-\(g\)-PMMA (GP = 22.0%) (B) in water. (**a**): after 0 h; (**b**): after 1 h; (**c**): after 2 h; (**d**): after 24 h.  
**Author Contributions:** [PERSON] conceived the experiments and wrote the manuscript; [PERSON] designed the experiments; [PERSON] and [PERSON] performed the click reaction experiments; [PERSON] and [PERSON] contributed to the results and discussions; [PERSON] and [PERSON] gave the RAFT synthesis methods; [PERSON] reviewed this manuscript and checked the English language corrections.  
**Funding:** This research was funded by the Natural Science Foundation of China (Grant No. 21406017); the Natural Science Foundation of Jiangsu Province, China (Grant No. BK20140254); the Natural Science Foundation of the Jiangsu Higher Institutions of China (18 KJA430005).  
**Acknowledgments:** Thanks to the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) support.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results
### Technological and Chemical Properties of Bread
#### 3.3.3 Bread Moisture and Water Activity  
Moisture content and water activity (a\({}_{w}\)) are relevant factors in assessing both bread quality and its susceptibility to microbial spoilage. Moisture content affects the texture and overall quality of bread, while water activity is important to determining the potential for microbial growth [56]. The results in Table 5 showed no significant differences in moisture content among the control, _C. vulgaris_, and _P. tricornutum_ bread samples, whereas the addition of _T. chuii_ resulted in a significant reduction (\(p<0.05\)) in moisture content.  
Figure 3: Appearance of breadcrumbs prepared with 4% (\(w/w\)) microalgae in comparison to control bread (without microalgae).  
Likewise, a reduction in moisture content was also observed in wheat bread and crackers containing microalgae [7]. Conversely, another study on gluten-free bread with microalgae indicated no significant change in moisture content [22]. However, the moisture content of enriched bread is aligned with the acceptable range of normal bread, which is between 35 and 45% [57]. No significant changes in water activity were noticed between the control bread and _C. vulgaris_ bread samples; lower a\({}_{\text{w}}\) levels (\(p\) < 0.05) were obtained in the _T. chuii_ and _P. tricornutum_ samples (Table 5). [PERSON] et al. [24] observed a reduction in water activity in bread as microalgae levels increased, with the most significant decrease occurring at higher microalgae concentrations. Reduced water activity contributes positively to shelf life. For instance, optimal bread quality is achieved by maintaining a lower water activity level, as higher values increase microbial spoilage. Specifically, the threshold for bacteria and mould growth is around 0.91 a\({}_{\text{w}}\)[58].
## 3 Proposed Method
### Global Feature Attention Module
#### 3.2.2 Spatial Attention Module  
This module can selectively focus on important spatial locations in the input feature map, while suppressing or ignoring less important regions.  
First, for a given input feature map \(f\in\mathbb{R}^{C\times H\times W}\), where \(H\), \(W\), and \(C\) denote its height, width, and the channel numbers, respectively, we can obtain a spatial vector through this attention module, as illustrated in Figure 5b.  
Secondly, we can obtain two vectors by calculating the input feature map \(f\) with two different pathways, one is \(f_{s1}\), the other is \(f_{s2}\). Specifically, we can obtain the \(f_{s1}\in\mathbb{R}^{C/4\times H\times W}\) with a \(1\times 1\) convolution, and then we reshape it to \(f_{s1}\in\mathbb{R}^{C/4\times HW}\). Meanwhile, we can obtain the \(f_{s2}\in\mathbb{R}^{C\times 1\times 1}\) by using global pooling, and next we reduce its channel numbers through a \(1\times 1\) convolution, and then we reshape and transpose it to \(f_{s2}\in\mathbb{R}^{1\times C/4}\). After that, a sigmoid activation is employed to make it nonlinear.  
Thirdly, we apply a matrix multiplication between \(f_{s1}\) and \(f_{s2}\) to obtain the \(f_{s}\in\mathbb{R}^{1\times HW}\). This process can be described by  
\[f_{s}=RS(\mathcal{W}_{1\times 1}\odot f+b)\otimes\sigma(RT(\mathcal{W}_{1 \times 1}\odot(\mathbb{G}(f)+b)), \tag{3}\]  
where \(\otimes\) and \(\odot\) represent element-wise multiplication and the convolution operation, respectively, and \(RS(\cdot)\), \(RT(\cdot)\), \(\mathbb{G}(\cdot)\), and \(\sigma(\cdot)\) denote the reshape operation, reshape and transpose operation, global pooling, and sigmoid activation, respectively. \(W_{1\times 1}\) denotes the filter value, and \(b\) indicates the bias value.  
Finally, with the reshaping and sigmoid operations, we can obtain the output \(y_{s}\in\mathbb{R}^{1\times H\times W}\). The process can be described by  
\[y_{s}=\sigma(RS(f_{s})). \tag{4}\]  
Overall, our proposed GFA module addresses the limitations of existing approaches by selectively using both high-level and low-level feature maps and producing more diverse feature representations for improved segmentation performance.
## 2 Cost Model of Predictive Maintenance
### Assumptions  
Our methodology for maintenance cost optimization is based on the following assumptions:  
* The system under study is a single component.
* The system under study is part of a whole complex system, which has a duration of exploitation known beforehand called \(D_{T}\).
* One inspection is performed on the system with a regular step. The inspection provides an information on the actual health state of the system. For instance, the inspection gives a real estimation of the RUL. After simulations, the RUL is the expected interval of time the system is likely to operate before it fails [4, 5, 6]. The expression of RUL is given by: \(RUL(t)=E[T-t\backslash T>t]=\frac{l_{t}^{\infty}f(u)\cdot du}{S(t)}\), where \(E[X]\) is the function that gives the expected value of a random variable \(X,f\) is the density function of failure probability distribution, \(T\) is the random variable describing the lifetime of the system and \(S\) is the survival function of the system.
* The inspection has no impact on the performance of the system.
* A primary inspection is required at the beginning of life of the system, but the health of the system is not supposed to require replacement because it is a new one.
* Between inspection \(i\) and \(i\)+1, one of the following scenarios may occur:
* _PdM scenario:_ the RUL of the system attains some threshold value called \(RUL_{lim}\) under which the system is considered as deteriorated: in fact, as the system's life expectancy decreases, the chance of system failure increases. This may lead to high maintenance costs related to corrective replacement. Therefore, it is judicious
to consider a RUL threshold for PdM in order to avoid as much as possible a potential failure. The system is replaced by a new one once the RUL of the system is under this threshold of RUL.
* _Non-PdM scenario:_ in this scenario, the system does not undergo PdM. In such case, the system can break down or not:
* The system breaks down before inspection _i_+1, the system undergoes then corrective maintenance and is replaced by a new one. This happens with the probability of occurrence \(\int_{t_{i}}^{t_{i+1}}f_{i}(t\backslash T\geq t_{i})\cdot dt\) where \(t_{i}\) is the time of inspection \(i\), \(f_{i}\) is the probability density function of failure distribution between inspections \(i\) and \(i\)+1.
* The system works normally without failure until inspection _i_+1 with the complementary probability \(1-\int_{t_{i}}^{t_{i+1}}f_{i}(t\backslash T\geq t_{i})\cdot dt\).
# Distance-Based Resource Allocation for Vehicle-to-Pedestrian Safety Communication
## 6 One-to-Many Bipartite Matching  
In the last section, distance-based optimum power was assigned to the users. However, since the user share the uplink frequency resource blocks, interference can degrade the performance of the system. Therefore, we aim to perform a pair matching technique that can cater the interference by using matching. Matching has been used extensively in various wireless network problems [27]. It has mostly found its way in problems related to resource sharing. Most matching problems are two dimensional that employ one-to-one matching like the Hungarian algorithm used in [9]. While some authors have used three-dimensional matching for resource allocation in more complex problems [28; 29]. In this paper, we have used a variant of the bipartite matching known as one-to-many bipartite matching with maximum flow. The motivation behind using the one-to-many bipartite matching is inspired by the coalition formation for multi robot task in [30], where a single task is assigned to multiple robots. We have taken the key idea from here to create connections and assign weights.
## 2 Study Area  
The study area has an extension of 60 km\({}^{2}\) and is located in the Reno River valley South of Bologna in Italy's Northern Apennines. The Northern Apennines is a collisional belt formed from the convergence of the European and Adriatic plates between the Cretaceous and Early Miocene [26; 27]. In the study area, the most common lithologies are chaotic clay-shales with block-in-matrix fabric [28; 29] and deposits of turbidity currents, also known as flysch [30]. Flysch consists of a stratified alternation of sandstone, siltstone, and marlstone beds. In the following, we use the term \"coarse-grained flysch\" and \"pelitic flysch\" to indicate heterogeneous rocks where fine-grained beds are, respectively, less or more abundant than coarse-grained beds. Landslide types are related to bedrock lithology: earthflows dominate in the clay-shales [31], while a broader spectrum of landslide types can be observed in the flysch. However, the most common phenomena consist of translational or rotational earth- or rockslides in the upper part of the slopes that propagate as earthflow further down-slope [32; 33; 34].  
This style of activity is typical for the whole Emilia-Romagna Region. [PERSON] and [PERSON], 2001 [4] reported more than 32,000 landslide bodies in the regional territory, most of which can be described as complex landslides, associating roto- transitional slides with earthquakes. According to the authors, typical velocities are millimeters to centimeters per year during the dormant phase (which may last years to hundreds of years) and increase up to meters per hour during periodic reactivations. These rapid stages of movement typically occur after periods of heavy rainfall and can lead to the transition of the moving mass from slide to flow [35].  
Within the area of interest (Figure 1), the regional inventory reports the presence of 186 landslides that were classified by the local Geological Survey as \"active\" or \"dormant\" based on geographic evidence. Approximately 18.8% of them are set within coarse-grained flysch, 31.2% within peolitic flysch, 38.2% within chaotic clay shales, and only 11.8% within other sedimentary rocks. Only four landslides underwent paroxysmal reactivation during the analysis period (2015-2019). All of them are located inside the chaotic clay shales. One of these landslides (the Marano earthflow) occurred in March 2020 and has already been studied using radar interferometry [8]. The other three (Braina, Carbona, and Spareda) occurred in November-December 2019 and are the subject of this study.  
The area has a Mediterranean climate, with total annual precipitation of about 1300-1400 mm [36]. The temporal precipitation pattern is characterized by intense rainfall in spring and autumn, separated by dry summers and relatively dry winter months when precipitation partly occurs as snowfall [37; 38; 39].
## 5 Discussion  
In this paper, we derived the closed-form expressions of outage probabilities and ergodic capacities. Both AF and DF relaying protocols were considered to perform a comprehensive analysis. It was observed that the ergodic capacity curves of far users continue to increase with an increase in SNR, whereas, the ergodic capacity curves of near user reach a ceiling due to the effect of interference. Furthermore, it was unveiled that the outage probability of HS is significantly higher than the outage probability of AS because of the impact of communication conditions between satellite and relay.  
This work can be extended in several ways. One of the exciting research directions, in this regard, can be the consideration of hardware impairments in the analysis. This would help unveil the impact of such impairments on network performance. Another important aspect to explore is relay selection, whereby the satellite could select the appropriate relay for transmission of messages. It is anticipated to further improve the performance of HSTRNs. These important yet challenging issues will be addressed in future studies.