esa-sceva/satcom-chunk-collection huggingface dataset:
# POI Recommendation Scheme Based on User Activity Patterns and Category Similarity
## 3 The Proposed POI Recommendation Scheme
### Recommendation Phase
#### 3.4.3 POI Candidate Extraction and Recommendation  
The activity patterns of all users, category similarity of the visited locations, and check-in frequency are used to determine the recommendation score of the low-activity group. The proposed scheme prioritizes the category of the places that the user frequently visits during the specified time period and evaluates the activity patterns of all users once the user's time and location information are entered. A higher weight is assigned when the recommendation score of the category is higher. Category similarity is determined using a pre-constructed ontology. Each category in the ontology is assigned a score based on the number of visits by users across different time periods. The locations are compared with the ontology's categories, and the final category similarity score is calculated based on relationships such as an exact match, a parent inclusion, or a child inclusion of the location's category. Furthermore, visited locations with a high frequency of check-ins are assigned a high weight during the calculation of the recommendation score. Thus, the proposed scheme recommends k POIs with the maximum recommendation score among the candidates [15].
## 2 Materials and Methods
### Model Dam Configuration and Measurement Location  
The experimental soil tank model, constructed from concrete, steel, transparent, and reinforcing materials, measured 95 cm (height) \(\times\) 270 cm (length) \(\times\) 670 (width) cm. The experimental dam model was constructed to a scale of 1/30 of the standard cross-section of a prototype reservoir (see Figure 4). Based on the actual reservoir cross-section, model slopes of 1:2.0 (downstream) and 1:2.5 (upstream) were applied. The vertical filter in the filter zone extended to the bottom of the gabion structure, and a horizontal filter was installed in the toe drain. Homogeny of the fill layers was ensured by compacting 10 cm thick layers using soils passing a 4.75 mm sieve. The relative compaction for each layer exceeded 90% maximum dry density. Pore water pressure (PWP) gauges with rated capacities of 50 kPa were used to measure water leakage at several locations: below the upstream bottom (P1, depth of 25 cm), at the bottom of the dam center (P2, depth of 20 cm), above the downstream bottom (P3, depth of 5 cm), below the dam crest core (P4, depth of 44 cm), and at the top of the downstream slope (P5, depth of 38 cm). Linear variable differential transformers with rated capacities of 50 mm were used as vertical and horizontal displacement meters centered on the dam crest core of the upstream (H1, V1) and downstream (H2 and V2) slopes. All measured values were recorded on a computer via a data logger.  
The overtopping prevention structure comprised gabions, geomembranes, and a central core (see Figure 3). This core was installed between the upstream and downstream gabion retaining walls using formwork (see Figure 5) to address potential events related to rising water levels. The gabions, placed on both the upstream and downstream sides of the dam crest, had a height of 12 cm, width in the range of 4-6 cm, and length of 210 cm; the reduction ratios considered were based on gabion retaining wall standards [31]. The stepped gabion retaining wall was installed according to the design standards, ensuring that the front of the upper gabion step had a minimum width half that of the lower gabion step. Crushed stone samples filtered using 5 mm and 10 mm sieves were used as fill material [31]. A geomembrane was installed between the core and gabion. The core, geomembrane, and gabion were connected by wires to prevent gaps owing to conflicting material properties and to ensure close adhesion between the materials.  
Figure 3: Overtopping prevention structure.
## 4 Experiment Results  
In this section, the OCM-DRW is tested and compared against several state-of-art detectors in texture-less object genres. In order to evaluate the performance of OCM-DRW for object instance detection, we include in our comparison template-based methods for texture-less object detection such as LINE2D [33], as well as popular descriptor-based keypoint detectors like SIFT [16], BOLD [23] and BORDER [27]. In addition, to ensure comprehensiveness, two datasets of experiments are conducted, the D-Textureless dataset [23] for models appearing rotated, translated and scaled in the scenes and the CMU Kitchen Occlusion dataset (CMU-KO8) [36] for its highly cluttered and occlusive scenes.  
The OCM-DRW is compared with LINE2D, SIFT, BOLD and BORDER, which are all implemented in C++ set with their proposed parameters in the literature. The OCM-DRW code is as we show in Ref. [37]. The implementations of LINE2D and SIFT are taken from OpenCV, while BOLD and BORDER are realized by the library from their project sites. Similar to [23] and [27], the performance of these algorithms are evaluated measuring its true positive rate and false positive rate. The test samples are divided into positive samples which contain the target object and negative samples which don't contain the target object. The true positive ratio (TPR), also known as sensitivity, is defined as follows:  
\[\text{TPR}= \text{TP}/(\text{TP}+\text{FN}), \tag{16}\]  
where TP are the number of positive samples which are correctly detected (True Positives) and FN is the number of positive samples which are incorrectly detected as the negative sample (False Negative). And the False positive ratio (FPR) can be defined as follows:  
\[\text{FPR}= \text{FP}/(\text{FP}+\text{TN}), \tag{17}\]  
where FP is the number of negative samples which are incorrectly detected as the positive sample (False Positive) while TN is the number of negative samples which are correctly detected (True Negative). Detector that have a good performance are indicated by high TPR value and low FPR value.
## 5 Results
### Classification Performance  
#### 5.1.1 Bicubic Interpolation of Videos  
The classification performance of the upscaled images using bicubic interpolation is provided in the confusion matrix in Figure 6. It is to be noted that out of 50 videos in each class, 40 videos are used for model training, and 10 videos are used for model testing. The confusion matrix provides the actual number of videos classified into each category. Table 3 provides the performance metrics for each class as well as the macro-averaged value for all classes. Classification accuracy is usually regarded as the most important performance metric for anomaly classification, followed by the FPR. Moreover, the values of precision, recall (sensitivity), F1-score, specificity, FPR, and FNR are also reported for each class and are averaged for all classes.
## 4 Simulation of Running Motion Generation  
### Running Experiment  
The running experiment was conducted to identify the parameters of the SLIP model and to validate the simulation result with the proposed model. Four healthy participants took part in the running experiment. Their height and weight are presented in Table 1. All participants provided informed consent. Our study protocol was approved by the local institutional review board and conformed to the guidelines outlined in the Declaration of Helsinki (1983). A split-belt treadmill equipped with force plates (ITR5018-11C, Bertec, Columbus, OH, USA) was used to measure the ground reaction force at a sampling rate of 1 kHz. The running motion was captured using a commercial marker-based optical motion-capture system (Raptor-4S, Motion Analysis Co., Santa Rosa, CA, USA) operating at a frame rate of 200 Hz. The participants wore 57 markers, and their positions were determined based on an improved version of the Helen Hayes Hospital marker set. Participants ran at the speed of 2.22 m/s, and the running was measured for 2 min. The collected data were low-pass filtered using a fourth-order zero-lag Butterworth filter with a 20 Hz cutoff frequency [27]. The stance phase, during which the foot is in contact with the ground, was defined as the period when the ground reaction force equaled or exceeded 50 N. Conversely, the flight phase was defined as the period when neither foot was in contact with the ground, indicated by a ground reaction force below 50 N for both feet.  
From the processed data, the parameters of the SLIP model (mass \(m\), stiffness \(k\), the damper's viscosity \(c\), and spring's natural length \(L_{0}\)) were identified. The model's mass was set to the participant's body weight. The remaining parameters were identified using Equation (11) to develop a running model tailored for each participant.  
\[J_{k,c,L_{0}}=\sum\biggl{\{}f_{\text{grf}}(t)-c\frac{dl(t)}{dt}-k\{L_{0}-l(t) \}\biggr{\}}^{2} \tag{11}\]  
Here, \(f_{\text{grf}}(t)\) represents the ground reaction force, and \(l(t)\) donates the distance from the center of pressure (COP) to the COG. Each parameter was derived as an average value of the entire running. Table 2 also presents the model parameters identified from the experimental data for each participant.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Participant** & **Height (m)** & **Weight (kg)** & \(m\) **(kg)** & \(k\) **(kN/m)** & \(c\) **(Ns/m)** & \(L_{0}\) **(m)** \\ \hline P.1 & 1.76 & 72.4 & 72.4 & 28.5 & 205 & 1.03 \\ P.2 & 1.72 & 71.6 & 71.6 & 27.3 & 121 & 1.02 \\ P.3 & 1.64 & 64.7 & 64.7 & 31.8 & 264 & 0.97 \\ P.4 & 1.71 & 55.0 & 55.0 & 20.0 & 67 & 1.00 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Participants details(left) and SLIP parameters(right).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Participant** & **\(\theta_{0}\) (deg)** & \(v_{x0}\) (m/s)** & \(y_{0}\) (m) \\ \hline P.1 & 10.7 & 2.25 & 1.10 \\ P.2 & 11.8 & 2.29 & 1.05 \\ P.3 & 10.6 & 2.29 & 1.01 \\ P.4 & 11.7 & 2.28 & 1.05 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation initial values.
## 4 Materials and Methods
### Data Processing
#### 4.8.3 Quantitative Data Analysis  
The peak intensity values of the iTRAQ reporter ions were derived from the CAD scans (reporter_iTRAQ_mz) and adjusted for recognized isotopic contaminants (iTRAQ_software_corrections.mz (version V1.05)). Independent LC-MS/MS analyses of the Fe3-NTA resin supernatant were conducted, and the signals from every quantitation channel (114, 115, 116, and 117) were summed (across all peptides) to calculate normalization factors and correct the phosphopeptide data for minor variations in the source protein concentrations. The iTRAQ reporter signal intensities from the peptides, derived from several MS/MS scans, were accumulated prior to ratio calculation The \"error model\" incorporated into the Multiplierz script was employed to calculate the proportion of identified phosphorylated peptides between groups and the p-value of significant changes. Phosphorylated peptides displaying a change factor above 1.5 times and a p-value below 0.05 were identified.
## 4 Results and Discussion
### The Influence of Chain Count on the Execution Time  
In the second experiment, we tested the scalability of our parallel implementation when the number of chains being processed is increasing. This experiment was executed for the same four cryptographic hash functions that were mentioned in Section 4.1; those hash functions are SHA-256, SHA-512, MD5 and NTLMv2. The implementation was executed on 15 computing nodes, and the execution times were measured for processing 100, 1000, 10,000 and 100,000 chains while keeping the chain length constant at 10,000. Those values were chosen to evaluate the scalability of the parallel implementation against different table sizes.  
The results of this experiment are presented in Figure 5, which illustrates the relative execution times for generating rainbow tables using four cryptographic hash functions, SHA-256, SHA-512, MD5 and NTLMv2, with varying counts of chains. The values shown in the figure are relative to the fastest algorithm in each group. The \(x\)-axis represents the number of chains, while the \(y\)-axis shows the relative execution time. As shown in the figure, SHA-512 consistently exhibits the highest relative execution time, reflecting its greater computational complexity, while MD5 and NTLMv2 perform much faster, as expected due to their algorithmic weaknesses. SHA-256 occupies the middle ground.  
Figure 5: Relative execution time for parallel rainbow tables generation with varying chain counts and 15 computing nodes for SHA-256, SHA-512, MD5 and NTLM (NTLMv2) cryptographic hash functions (the values are relative to the fastest algorithm in each group).
## Recommendations and Future Work  
Future work, as well as some important recommendations are:  
* The performance of the proposed parallelization strategy in other CPU-based architectures than the one used in this work, remains to be tested.
* For extremely large structures, compression techniques for sensitivity matrices can be considered to alleviate the memory allocation.
* It is also possible to follow [60] to obtain a more accurate initial density model, considering also information from a velocities model of the area under study.
* In this work we did not consider other parallel computing architectures like GPUs, mainly for their limited memory size. Nevertheless, in a future when GPUs have more memory, this algorithm can be migrated to such architecture to compare performance.  
As previously mentioned, we did not invert simultaneously magnetic, gravimetric and gradiometric data due to memory limitations in our workstation. To further constrain the possible solutions of the model to be estimated, it remains to assess the behavior of the growth inversion algorithm when inverting such datasets jointly.  
Conceptualization, C.C.-C. and C.O.-A.; data curation, A.T.-A. and C.O.-A.; formal analysis, J.H. and N.V.; funding acquisition, C.C.-C. and A.T.-A. investigation, J.H., A.T.-A. and M.O.-d.-C.; methodology, C.C.-C. and N.V.; project administration, C.C.-A.; resources, M.O.-d.-C. and C.C.-C.; software, N.V., C.C.-C. and J.H.; supervision, C.O.-A.; validation, C.C.-C.; visualization, N.V. and M.O.-d.-C.; writing--original draft preparation, J.H., A.T.-A. and M.O.-d.-C.; writing--review and editing, J.H., C.C.-C., N.V., M.O.-d.-C., C.O.-A. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Secretaria de Investigacion y Posgrado, Instituto Politecnico Nacional grant numbers 20200378, 20201040 and 20200259, by Centro de Desarrollo Aeroespacial, Instituto Politecnico Nacional computational time grant number FS0008, by project 292637 \"Lioteca Nacional Sede Yucatan\", from fund Secretaria de Energia--Consejo Nacional de Ciencia y Tecnologia--Hidrocarburos, as well as by Tecnologico Nacional de Mexico/IT de Merida project # 8285.20-P. The APC was funded by Comision de Operacion y Fomento de Actividades Academicas del Instituto Politecnico Nacional and Secretaria de Investigacion y Posgrado, Instituto Politecnico Nacional.  
Authors acknowledge Secretaria de Energia--Consejo Nacional de Ciencia y Tecnologia fund for the data as well as the scholarship provided to perform this study, as well as EDI grant by Secretaria de Investigacion y Posgrado, Instituto Politecnico Nacional.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 4 Conclusions  
It has been frequently demonstrated that utilizing natural fibers and agro-waste materials for building products is an inexpensive, feasible, and practical option. To achieve sustainable solutions, these materials can be employed in various building applications, such as insulation boards, masonry bricks, and mortars. Given the continuous and widespread production of agricultural materials, a large quantity of them is thrown out as waste materials, which causes environmental problems and has adverse effects on human life. Incorporating these materials into buildings not only facilitates recycling but also represents an eco-effective solution for construction.  
The application of sugarcane bagasse ash in concrete improved its compressive strength over different lengths of time, although there is no general consensus among researchers about using agro-waste ash in concrete. It was observed that combinations of different ashes and also the size of the additive particles had an increasing effect on the compressive strength and kept it higher than 155 MPa.  
Coconut shells showed satisfactory results alongside decreasing physical properties of the concrete. However, when coconut shells and coconut fibers were added into the concrete, its compressive strength increased by 17% compared to fiber-free concrete.  
Chemical treatment could increase the surface wrinkling of agricultural fibers and improve the adhesion between the cement and the fibers, but it may lead to environmental damage. In addition, the application of chemical treatments, particularly alkaline solutions, can significantly enhance the tensile and adhesion properties of natural fibers.  
The application of agricultural fibers could effectively save energy for the insulation of buildings. An energy saving of 69% was reported after adding wheat straw into hollow clay bricks.  
Yet some of the agro-waste applications in construction, like using ash as a cement additive, did not show a high added value. The environmental concern, economic costs, and high grinding cycles are the three main barriers of using agro-waste materials as cement supplementary materials, which shows that there is still a gap in the research.  
However, among the research regarding the application of agro-waste materials that can be used in construction, despite the many advantages of using of these materials, including their low cost, better performance, and better insulation, some problems such as people's low enthusiasm as well as the lack of proper building construction laws have become an obstacle preventing the expanded use of these materials in construction.  
In conclusion, the utilization of natural fibers and agro-waste materials presents a promising avenue for sustainable construction practices. From improving the compressive strength of concrete to enhancing energy efficiency in insulation, these materials offer a range of benefits for building products. However, challenges such as environmental concerns, economic costs, and regulatory barriers persist, hindering their widespread adoption.
###### Abstract  
It is important to develop modelling tools to predict unstable situations resulting from the interactions between the wind power plant and the weak power system. This paper presents a unified methodology to model and analyse a wind power plant connected to weak grids in the frequency-domain by considering the dynamics of the phase lock loop (PLL) and controller delays, which have been neglected in most of the previous research into modelling of wind power plants to simplify modelling. The presented approach combines both dq and positive/negative sequence domain modelling, where a single wind turbine is modelled in the dq domain but the whole wind power plant connected to the weak grid is analysed in the positive/negative sequence domain. As the proposed modelling of the wind power plant is systematic and modular and based on the decoupled positive/negative sequence impedances, the application of the proposed methodology is relevant for transmission system operators (TSOs) to assess stability easily with a very low compactional burden. In addition, as the analytical dq impedance models of the single wind turbine are provided, the proposed methodology is an optimization design tool permitting wind turbine manufacturers to tune their converter control. As a case study, a 108 MW wind power plant connected to a weak grid was used to study its sensitivity to variations in network short-circuit level, X/R ratio and line series capacitor compensation (Xc/Xg).  
wind power plants; wind turbines; weak grids; impedance-based modelling; small-signal stability
## 3 Network Architecture Description
### Sensor Nodes Layer
Therefore, to test the network functionalities, minimal sensor nodes were developed by resorting to off-the-shelf components only (see Figure 3). Moreover, hardware components (e.g., microcontrollers) of LoRaWAN Nodes were different with respect to the ones of NB-IoT Nodes to pinpoint the modularity and the flexibility of the network. However, it has to be underlined that whichever node that is compliant, at least in a weak sense, with the block scheme of Figure 2 can be exploited within the network. LoRaWAN Nodes were composed of an Arduino UNO board and a LoRa shield manufactured by Dragino. The latter embeds a LoRa transceiver (i.e., the HRF95 made by HopeRF), which relies on a Semtech SX1276 LoRa module. The shield also features an antenna connector to which a \(\lambda/8\) whip antenna was attached. The microcontroller implements a LoRaWAN Class A device. In particular, it encrypts the packets payload twice by exploiting both the Network Session Key (NwkSKey) and the Application Session Key (AppSKey) by means of an Advanced Encryption Standard (AES) (i.e., AES-128). In addition, packets are wirelessly sent to any LoRaWAN-NB-IoT Gateway in the nodes closeness via LoRa links. They are set up by establishing a frequency diversity scheme via a frequency hopping technique amid eight different channels belonging to the 863-870 MHz Industrial, Scientific and Medical (ISM) band. Nonetheless, regional regulation concerning the time occupancy of ISM bands (i.e., in Europe and for the aforementioned ISM band, the spectrum can be occupied for no longer than the 1% of the time by each of the transmitters) are anode by. Finally, a simple temperature sensor (i.e., the LM35 made by Texas Instruments) was included since it is useful for the testing phases of the network. Conversely, NB-IoT Nodes were composed of a Nucleo-L476 RG board produced by STMicroelectronics, which drives an NB-IoT transceiver (i.e., the SIM7000E manufactured by SIMCom). Such a transceiver allows for both LTE Machine Type Communication (LTE-MTC) and NB-IoT communication. Connectivity is ensured via a a micro Subscriber Identity Module (SIM) card, belonging to the Italian telco provider TIM, which is especially devoted to M2M cellular communication purposes. As it will be explained later on, all of the network nodes were powered via a USB port of a PC since it is exploited as a sort of data-logger throughout  
Figure 3: Sensor node hardware implementation: (**a**) LoRaWAN Node; (**b**) NB-IoT Node. the network testing procedures. Nevertheless, the energy source chosen had to be feasible provided that it fits the application scenario at hand.