esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Absolute Dose Measurement  
A reliable dose monitoring system is sufficient to deliver the desired dose in a well-known clinical setting, as described in the previous section. As dose delivery in the FLASH mode is still being explored, and accurate dose monitoring in this regime is challenging, an independent measurement of the absolute dose in the experiments and possible first experiences with patients will be necessary to validate the whole dosimetry protocol.  
The absolute dose can be measured through active or passive detectors. Active detectors provide real-time dose measurement, while passive ones need to be read out by specific methods and cannot be used to evaluate the delivered dose immediately after irradiation. The standard detector used in a clinical setting is the ionization chamber. As mentioned in Section 3, ionization chambers at ultra-high dose rates suffer from reduced ion collection efficiency, primarily due to volume recombination. Therefore, it is worth exploring other dose-rate-independent possibilities, at least to complement ionization chambers. In this section, examples of both active and passive alternatives, which can be deployed for FLASH irradiation, are given and briefly described.
# A Communication-Efficient Secure Routing Protocol for IoT Networks +
## 6 Performance Evaluation
### Emulation-Based Analysis for IoT Networks
#### 6.4.3 Routing Overhead  
Here, we present a comparison of the total routing time. This performance can be obtained by summing the calculation time for cryptographic processing (obtained in Section 6.4.2) and the communication time for relaying the routing packets.  
Let us consider a situation in which a packet is transmitted from one node to another node with a transmission rate \(R\) (in bps) and the receiver is within the transmitter's communication coverage. If routing is performed by \(n\) end-to-end nodes, the communication time can be derived as  
\[t_{\text{com}}=\sum_{i=1}^{n+1}\frac{M_{\text{req},i}}{R}+\sum_{j=1}^{n+1} \frac{M_{\text{rep},j}}{R}\ \ [\text{s}]. \tag{24}\]  
where \(M_{\text{req},i}\) is the packet length (bits) in the \(i\)-th hop of the RREQ phase (counted from the source node) and \(M_{\text{rep},j}\) is the packet length (bits) in the \(j\)-th hop of the RREQ phase (counted from the destination node). Similarly, the performance of the proposed method when an intermediate node initiates the RREP can be derived by substituting \(N\) into \(n\) in Equation (24).  
Based on the ZigBee specification [32], we assume \(R=250\times 10^{3}\,\text{(bps)}\). When the communication time is obtained, we can derive the routing overhead by summing it with the calculation time obtained from Figure 5.  
Figure 7 shows the overhead performance, where \(n=5\). If \(N=0\) or \(1\) (i.e., the proposed method can construct the route in four hops), our proposed method can reduce the route construction time relative to the compared methods. Next, we illustrate the maximum number of nodes between S and the replying node \(N\) at which the proposed method is superior to the comparison methods in terms of routing overhead in Figure 8. This figure reveals that as the network becomes larger (i.e., \(n\) increases), our proposed method is better able to reduce the construction time. However, compared with SAODV and ISDSR+, the conditions for reducing the route construction time are limited (e.g., \(N=0\) at \(n=3\)). Although the proposed method can reduce the required number of communications for route construction, the cryptographic computation time becomes a bottleneck, as shown in Figure 5. However, as discussed in Section 6.3, the proposed method outperforms all of the compared methods in terms of the total number of bytes.  
Figure 7: Routing overhead performance where \(n=5\).
###### Abstract  
Non-destructive assessment of fruits for grading and quality determination is essential to automate pre- and post-harvest handling. Near-infrared (NIR) hyperspectral imaging (HSI) has already established itself as a powerful tool for characterizing the quality parameters of various fruits, including apples. The adoption of HSI is expected to grow exponentially if inexpensive tools are made available to growers and traders at the grassroots levels. To this end, the present study aims to explore the feasibility of using a low-cost visible-near-infrared (VIS-NIR) HSI in the 386-1028 nm wavelength range to predict the moisture content (MC) and pH of Pink Lady apples harvested at three different maturity stages. Five different machine learning algorithms, viz. partial least squares regression (PLSR), multiple linear regression (MLR), k-nearest neighbor (kNN), decision tree (DT), and artificial neural network (ANN) were utilized to analyze HSI data cubes. In the case of ANN, PISR, and MLR models, data analysis modeling was performed using 11 optimum features identified using a Bootstrap Random Forest feature selection approach. Among the tested algorithms, ANN provided the best performance with \(R\) (correlation), and root mean squared error (\(RMSE\)) values of 0.868 and 0.756 for MC and 0.383 and 0.044 for pH prediction, respectively. The obtained results indicate that while the VIS-NIR HSI promises success in non-destructively measuring the MC of apples, its performance for pH prediction of the studied apple variety is poor. The present work contributes to the ongoing research in determining the full potential of VIS-NIR HSI technology in apple grading, maturity assessment, and shelf-life estimation.  
hyperspectral imaging; apple; pH; moisture content; machine learning Article
# Time of Flight Distance Sensor-Based Construction Equipment Activity Detection Method
## 2 Literature Review and Background
### Vision-Based Method for Recognizing Equipment Activity  
Vision-based methods have emerged as a cornerstone for recognizing and monitoring construction equipment activities through the use of image and video data. These techniques leverage advanced computer vision technologies such as convolutional neural networks (CNNs) for spatial feature analysis and support vector machines (SVMs) for classification tasks. The deployment of the histogram of oriented gradients (HOG) featuresin conjunction with SVM classifiers has proven effective in identifying earthmoving equipment activity [16; 17]. Furthermore, the application of 3D convolutional neural networks (3D CNNs) underscores the progression in capturing the spatial and temporal dynamics of construction activities through video analysis [7; 18]. Recent advancements include the integration of CNN with long short-term memory (LSTM) networks, presenting a hybrid model adept at capturing the sequential nature of construction equipment activities. This approach addresses the temporal complexities inherent in these operations, marking a significant leap in accurately predicting the actions of construction machinery [19].  
Despite their benefits, vision-based methods encounter significant challenges. These include obstructions by adverse weather conditions (e.g., fog, dust, rain, and snow), unfavorable lighting conditions (e.g., luminous flux, illuminance, beam angle, and color temperature), and occlusions by construction equipment, which can significantly impair activity recognition [20; 21]. For example, poor lighting and occlusions have been identified as factors that may lead to inaccurate activity detection and classification [22]. The necessity for extensive labeled data for training deep learning models further adds to the complexity and cost of system implementation. The high computational demand for processing and analyzing video data in real time presents another hurdle, limiting the practicality of these methods on construction sites. This challenge is exacerbated by the need for powerful computing hardware to effectively implement complex deep learning models. Moreover, the capture of video data on construction sites raises privacy concerns, highlighting the importance of implementing robust privacy measures to mitigate potential issues [1; 23].
# Technique Evolutions for Microorganism Detection in Complex Samples: A Review
## 3 Developments in Innovative Detection Methods
### Antimicrobial Peptides  
Antimicrobial peptides (AMPs) are another promising possibility for broad-spectrum ligands. AMPs are short (usually less than fifty amino acids) and natural peptides found in most living animals, both eukaryotic and prokaryotic organisms. They are powerful broad-spectrum antibiotics. They can destabilize biological membranes to form transmembrane channels. In addition, they are effective against biofilms, where antibiotics are often ineffective [61; 62; 63].  
Apart from antibiotic applications, their strong affinity to bacterial membranes makes them interesting ligands for biosensing applications [64; 65]. Through molecular engineering, the bactericidal portion of the AMP can be removed, allowing the bacteria to bind without being destroyed so that a signal can be produced for later detection. Furthermore, AMPs are highly stable in storage and relatively easy and cheap to produce [66]. Surprisingly, their use for biosensors is only recent. So far, several examples of surface-immobilized AMPs used for detection have been described in the literature [67; 68]. [PERSON] et al. devised a microfluid chip for multiplexed detection of bacterial using AMPs [69]. Demonstrated on _Streptococcus_ and _Pseudomonas_, bacteria were detected at minimum concentrations of \(10^{5}\) CFU/mL within 25 min, due to electrical detection. Using an AMP called Nisin and an impedimetric detection, [PERSON] et al. successfully detected _Salmonella_ with an LOD of 15 CFU/mL with a 30 min incubation period [70]. This represents a significant improvement in the detection limit compared with [69].  
The broad-spectrum affinity of AMPs can enable biosensors to detect multiple bacteria strains at once. To have an exhaustive detection, few AMPs are sufficient instead of a panel of antibodies that are more specific to a bacterial genus. It would be interesting to evaluate  
Figure 2: Representation of RFD-based sensor. The targets produced by the bacterium bind to the inactive DNAzyme sequence, which undergoes a conformational change to activate the DNAzyme. Then, activated DNAzyme catalyzes the cleavage of the fluorogenic substrate at the ribonucleotide junction (R), leading to the separation of the fluorophore (F) and the quencher (Q) to produce a fluorescence signal.  
the effectiveness of AMPs inside blood or other complex environment and evaluate the possibility of profiling bacteria with only a small set of AMPs.
## 1 Introduction
It was found that PSO-SVM with the radial basis function (RBF) kernel function model was more reliable in predicting scour depth around bridge piers. [PERSON] et al. [28] applied hybrid models of particle swarm optimization (PSO) and firefly algorithms (FFA) with ANN to optimize the parameters of the ANN model to improve estimating the pier scour depth. The results of these hybrid models were compared with the results of the standalone ANN model trained by the Levenberg-Marquardt (LM) algorithm and with an empirical formula. Results showed a better performance of the developed hybrid models. In a related context, recently intense application of soft computing techniques (simple and hybrid) has been found in civil engineering for modeling various aspects such as prediction of safety factor values of retaining walls [29], prediction of the critical buckling load of structural members under compression [30], estimation of surface treatment effects on the tribological performance of steel tools [31], modeling the Marshall stability of stone matrix asphalt materials [32], estimation of soil compression coefficient [33], rock tensile strength prediction [34], and forecasting pile settlement [35]. This study employs a novel hybrid optimization tool, namely, Harris hawks optimization (HHO) algorithm, for finding the most appropriate parameters of an ANN model for estimating scour depth downstream of a ski-jump spillway. To the best knowledge of the authors, there is no literature on the application of this hybrid ANN-HHO model in hydraulics and especially for the prediction of scouring depth downstream of hydraulic structures. The hybrid ANN-HHO model was employed in some cases in other disciplines such as [PERSON] et al. [36] used ANN-HHO in predicting the soil compression coefficient and [PERSON] et al. [37] applied it in a prediction of the productivity of distilling systems. Therefore, in the present research, three hybrid intelligent models ANN-HHO, ANN-PSO, and ANN-GA with the standalone ANN technique, and the Wu model were employed to predict scour depth downstream of a ski-jump spillway. The effectiveness of these models during training and testing was evaluated using five performance metrics (i.e., mean absolute error: MAE, root mean square error: RMSE, coefficient of correlation: CC, Willmott index: WI, and mean absolute percentage error: MAPE) and graphical interpretation (line, scatter, and box plots, and Taylor diagram).
## 4 Shape Simulation of Ice Accretion on Sectional Model  
The experimental results in Section 3 can provide references for shape simulation of ice accretions on pipeline suspension bridges. To investigate the influence of ice accretion on the aerodynamic characteristics of pipeline suspension girder with girder A, three types of radial ice (the crescent-shaped, D-shaped, and sector-shaped ice accretion) and two length of icicles on the pipeline were selected referencing experimental results in the Sections 3.1 and 3.4. Circular truncated cone shaped icicles were selected. Under some external factors (e.g., aerodynamic interference, wind speed, and freeze-thaw), the D-shaped ice might be prior to the sector-shaped ice. Thickness of crescent-shaped radial ice was smaller than that of the other radial ice, therefore icicles with crescent-shaped radial ice were shorter than those with the other radial ice. Combining the above reasons, the corresponding ice shapes on pipeline are shown in Figures 22 and 23. The large ice thicknesses at the top of the three radial ice were designed to simulate the extreme condition. The ice accretion on angle steel and U-steel were simulated by adhesive tapes with a width of 20 mm and thickness of 3 mm, which were glued onto the surfaces that were facing droplets. A plexiglass plate with a larger thickness (10 mm) and smaller porosity (64%) was used to simulate the icing grate plate. Crescent-shaped ice was applied to simulate the radial ice of a guardrail (Figure 22d), while \(d_{t}\) and \(d_{b}\) were 5 and 2 mm, respectively, and the icicle spacing is shown in Figure 23c. The ice accretion models of the pipelines and guardrails were printed by 3D printing technology and then glued on the sectional model. The surface of the artificial ice was smooth, and the effect of roughness of ice accretion was not considered.  
Figure 23: _Cont._  
Figure 22: Schematic diagram of iced pipeline and guardrail (mm): (**a**) pipeline with crescent-shaped ice, (**b**) pipeline with D-shaped ice, (**c**) pipeline with sector-shaped ice, and (**d**) guardrail with crescent-shaped ice.  
Icing of a pipeline suspension girder is a very complicated process. Wet growth (with icicle) and dry growth (without icicle) may occur under low-temperature conditions [46]. To investigate the influence of the above process and ice accretion on various components, five ice classes C0 (ice-free), C1 (pipeline with radial ice), C2 (C1 + pipeline with icicle), C3 (C2 + guardrail with ice), and C4 (C3 + truss stiffening girder and grate plate with ice) were studied. In addition, the influence of the ice accretion types (Figures 22 and 23) on the aerodynamic force coefficients was also comprehensively investigated.
# Using ChatGPT in Software Requirements Engineering: A Comprehensive Review
## 2 Background
### Software Requirements Engineering  
Software requirements engineering is a systematic and process-driven approach to defining, documenting, and maintaining software requirements across the software development lifecycle [1]. This multidimensional task requires robust information retrieval, effective communication with multiple stakeholders, and the creation of detailed textual descriptions. It comprises two main phases: requirements development and management [9]:  
* **Requirements development:** This involves activities such as eliciting, analyzing, specifying, and validating the requirements.  
The requirements elicitation phase involves identifying stakeholders, selecting representatives, and determining their needs. It serves as the information-gathering step in requirements development. Various techniques for requirements elicitation include stakeholder interviews, focus groups, workshops, observations, questionnaires, document analysis, and benchmarking.  
The requirements analysis step synthesizes and refines information gathered during requirements elicitation. Stakeholder needs, assumptions, and other information are integrated and further detailed. This phase involves representing requirements in various forms, such as prototypes and models, conducting trade-off analysis, setting priorities, assessing feasibility, and identifying gaps to uncover missing requirements.  
A recommended practice for requirements specification is to utilize predefined templates. These templates enable requirements engineers to concentrate on content rather than format, reducing the risk of overlooking critical items while documenting requirements.  
The final step in the requirements development process is to validate the requirements to ensure they are well-written, complete, and aligned with customer needs. Validation may result in iterations of previous steps due to identified defects, gaps, additional information or analysis requirements, clarification, or other issues.  
* **Requirements management:** This includes the processes for requesting changes to established requirements, performing impact analysis on those changes, approving or rejecting them, and then implementing the approved changes.  
Requirements management is a continuous process that spans the entire software development lifecycle. During testing, the software product is validated against its requirements to identify and correct defects, ensure that it meets the specified requirements, and provide confidence in its functionality [9].
## 4 Analysis Method for Source Appointment and Contribution
### NP Method for Source Contribution
\[[\rm 1\text{-}NP_{\rm h}]/[\rm Pyr_{\rm h}]=[\rm 1\text{-}NP]a/[\rm Pyr]b \tag{6}\]  
\[[\rm 1\text{-}NP_{\rm I}]/[\rm Pyr_{\rm I}]=[\rm 1\text{-}NP](1-a)/[\rm Pyr](1-b) \tag{7}\]  
By introducing the above [1-NP\({}_{\rm h}\)]/[\(\rm Pyr_{\rm h}\)] and [1-NP\({}_{\rm I}\)]/[\(\rm Pyr_{\rm I}\)] values into Equations (6) to (7), \(a\) and \(b\) can be obtained [71]. The NP-method has advantages over the conventional methods. This method can calculate contributions of high- and low-temperature combustion separately not only for PAHs and NPAHs but also to PM. Moreover, contributions of high- and low-temperature combustion to 1-NP and Pyr can be obtained by Equations (4)-(7) without the information of PM. All results can be deduced to primary emitted PAHs and NPAHs. Although this method has just been developed, long-term, seasonal, and daily changes in contributions of vehicles and coal combustion to not only P\({}_{\rm c}\) but also PAHs and NPAHs in urban air in Japan, China, Russia, and Korea have been demonstrated [71; 72; 73]. Figure 2, as an application of the NP method, shows atmospheric concentrations of TSP, P\({}_{\rm c}\), PAHs and NPAHs, and contributions of vehicles and coal combustion to them in (A) Beijing, China and (B) Kanazawa, Japan over two decades. Contributions of vehicles and coal combustion were calculated using the NP method and previously published data [74; 75]. This figure shows: (1) Concentrations of TSP, P\({}_{\rm c}\), primary PAHs and NPAHs were all higher in Beijing than in Kanazawa, and the differences in TSP, P\({}_{\rm c}\), and primary PAHs were particularly large. (2) In Beijing, the proportion of P\({}_{\rm c}\) in TSP increased to 40% in the winter. (3) The P\({}_{\rm c}\) concentration in Beijing showed a seasonal change (winter \(>\) summer), and the contribution of coal combustion was more than 80% in the winter. (4) The contribution of vehicles to P\({}_{\rm c}\) in Kanazawa decreased significantly over the years and is currently less than 50%; (5) In both Beijing and Kanazawa, the main sources of primary PAHs and NPAHs were coal combustion and vehicles, respectively.
# Distributed Localization with Complemented RSS and AOA Measurements: Theory and Methods
## 3 Existing Methods
### Generalized Trust Region Sub-Problem (GTRS) Method  
A geometric approach was presented in [68], where the authors considered that AOA measurements are acquired at anchor nodes, exclusively. They considered a multi-hop approach in order to form triangles in which a target node, \(\mathbf{x}_{i}\), with a direct edge to an anchor node, \(\mathbf{a}_{j}\), plays a role of a pivot and _connects_ its target neighbor, \(\mathbf{x}_{k}\), to the anchor, as illustrated in Figure 10.  
The procedure proposed in [68] can be explained in three steps. Firstly, all target nodes with direct edges towards anchor nodes are localized, by means of weighted central mass (WCM). In the second step, the localized target nodes were considered as quasi-anchors in order to localize the remaining target nodes. This was done by taking advantage of the network geometry in the formed triangles and applying the law of cosines, which led to straightforward formulation of the problem in a GTRS framework [69; 70]. This type of problems has a very convenient characteristic, which is that, even though non-convex in general, one can determine an interval \(I\) on which the function  
Figure 9: Illustration of the Markov chain Monte Carlo with Metropolis-Hastings (MCMC-MH) procedure.  
is monotonically decreasing [69]. Therefore, GTRS can be efficiently solved by merely a bisection procedure, as illustrated in Figure 11. Finally, in the last step of the algorithm, the authors performed a refinement step based on WCM, in which they exploited the estimated locations of the target nodes in the previous steps and the acquired angle and distance measurements/estimates.  
An overview of the main characteristics of all described algorithms is given in Table 1, where the notations \"noise STD\" and \"P_T\" are used to denote the noise standard deviation and node's transmit power. Table 1 shows that all considered algorithms require that all nodes measure the RSS quantity, while some of the algorithms require that only anchor nodes measure the AOA quantity. It can also be seen from the table that SOCP is perhaps the most universal approach in terms of applicability, since it can be used for both cases of known and unknown \(P_{T}\), and does not require knowledge about noise STD. The remaining methods (at least in their original form) are only suitable for the case where \(P_{T}\) is assumed known. Furthermore, some of them require knowledge about the noise STD, which might  
Figure 11: Illustration of the generalized trust region sub-problem (GTRS) solved by a bisection procedure.  
Figure 10: Illustration of the geometric approach in [68] for a 2-hop case.  
eopardize their localization accuracy in the case where this information is not perfectly known, as it will be shown in the following section.