esa-sceva/satcom-chunk-collection huggingface dataset:
# Recent Developments in Plasmonic Sensors of Phenol and Its Derivatives
## 6 Conclusions  
This review focused on recent developments in optical sensors, including colorimetric, fluorescence, LSPR, and plasmon-enhanced Raman scattering, used to determine phenol and phenolic compounds. Combined with numerous plasmonic nanomaterials such as noble metals, hybrids, and MOFs, these optical methods have been widely applied in many chemical and biological fields by improving sensing performance. Here, this review mainly discussed numerous novel designs of unique nanostructures, inducing enhanced signals, and selective sensing analytes. Among these optical sensors, colorimetric methods showed many benefits such as facile observation with the naked eyes, whereas the Raman method exhibited more selective molecular fingerprints. While the LSPR method revealed an excellent feature of high sensitivity, fluorescence assays showed an ultra-sensitive detection and limited stability for photo-bleaching. Notably, the development of plasmon resonance-based optical sensors for phenol detection indicates that future work would continue exploring superior nanomaterials for phenol determination with simpler manipulation, faster response, higher sensitivity, and better selectivity. We also believe that the combination of new plasmonic nanomaterials with traditional molecular design can improve sensing performance even further.  
Conceptualization, N.H.L., H.H.K. and S.J.S.; methodology, H.H.K.; resources, H.H.K.; data writing--original draft preparation, N.H.L.; writing--review and editing, S.J.S.; visualization, S.-W.J.; supervision, S.J.S. and S.-W.J. All authors have read and agreed to the published version of the manuscript.  
Korea Ministry of Environment (MOE) as The Chemical Accident Prevention Technology Development Project and Korea Environment Industry and Technology Institute (KEITI) through Technology Development Project for Safety Management of Household Chemical Products Program, funded by the Korea Ministry of Environment (MOE) (2020002970005, 1485017845).  
Not applicable.  
The authors declare no conflict of interest.
## 5 Performance Analysis and Discussion
### Closed-Form Performance Analysis for Identical Local Detection Performance Indices
For the statistic \(N^{\prime}_{1}\), the threshold is \(T\), while for the statistic \(\Lambda^{\prime}_{\text{BER}}\), the threshold is \(T^{\prime}=C_{1}T+C_{2}\). According to the N-P criterion, it is required to maximize the system detection probability of the FC while the system false alarm probability is below a given value \(\alpha\). Note that the threshold \(T\) only takes values from zero to \(N\). For such a case with finite integer numbers of the fusion threshold, the desired system false alarm probability \(\alpha\) cannot be achieved. Hence, one can use the randomized fusion strategy under the N-P criterion. Under such a strategy, the FC declares the \"detect\" randomly with a probability [32,33]. The fusion space can be partitioned into three parts, given as:  
\[\delta(\mathbf{y})= \begin{cases}1,&\Lambda^{\prime}_{\text{BER}}(\mathbf{y})>T_{\text {R}}\\ \omega,&\Lambda^{\prime}_{\text{BER}}(\mathbf{y})=T_{\text{R}}\\ 0,&\Lambda^{\prime}_{\text{BER}}(\mathbf{y})<T_{\text{R}}\end{cases}  \tag{26}\]  
where \(\omega\), \(0\leq\omega\leq 1\) is a randomized factor and \(T_{\text{R}}\) is the randomized fusion threshold. The procedure to obtain the fusion threshold \(T_{\text{R}}\) and the randomized factor \(\omega\) is summarized as follows. 1. Search and obtain the randomized fusion thresholds \(\mathbf{T}_{\text{R}}=[T^{1}_{\text{R}},\cdots,T^{L}_{\text{R}}]^{T}\) to satisfy the inequality \[\sum_{k\in\kappa_{\text{R}}}\binom{N}{k}P^{k}_{0}(1-P_{0})^{N-k}\leq\alpha\] (27) where \(\kappa_{\text{R}}\) is defined as \(\kappa_{\text{R}}=\{k:\,k=\sum_{i=1}^{N}y_{i}{\geq}T^{l}_{\text{R}}\}\), \(1\leq l\leq L\). \(L\) is the number of thresholds that satisfy the inequality. 2. Maximize the searching thresholds \(\mathbf{T}_{\text{R}}\) in step 1 to obtain \(T_{\text{R}}\), _i.e._, \(T_{\text{R}}=\max(\mathbf{T}_{\text{R}})\). 3. Obtain the randomized factor \(\omega\) by: \[\omega=\frac{\alpha-\sum_{k\in\kappa_{\text{R}}}\binom{N}{k}P^{k}_{0}(1-P_{0} )^{N-k}}{\binom{N}{k}P^{\text{R}_{\text{R}}}_{0}(1-P_{0})^{N-T_{\text{R}}}}\] (28)4. The system false alarm probability \(P_{\text{FA}}^{\text{R}}\) for randomized detection can be written as: \[P_{\text{FA}}^{\text{R}} =\alpha=\sum_{k\in\kappa_{\text{R}}}{N\choose k}P_{0}^{k}(1-P_{0})^ {N-k}\] \[+\omega{N\choose T_{\text{R}}}P_{0}^{T_{\text{R}}}(1-P_{0})^{N-T_{ \text{R}}}\] (29) Similarly, the system detection probability \(P_{\text{D}}^{\text{R}}\) is: \[P_{\text{D}}^{\text{R}} =\sum_{k\in\kappa_{\text{R}}}{N\choose k}P_{1}^{k}(1-P_{1})^{N-k}\] \[+\omega{N\choose T_{\text{R}}}P_{1}^{T_{\text{R}}}(1-P_{1})^{N-T_ {\text{R}}}\] (30) For randomized fusion strategy, the given false alarm probability \(\alpha\) can be achieved by adjusting the term \(\omega{N\choose T_{\text{R}}}P_{0}^{T_{\text{R}}}(1-P_{0})^{N-T_{\text{R}}}\) under the N-P criterion. However, for the non-randomized fusion rule in [18], there always exists a gap between the required system false alarm probability \(\alpha\) and \(P_{\text{FA}}\).
# Composition and Basic Physical Properties of the Phobos Surface: A Comprehensive Review
## 4 Surface Composition of Phobos
### High-Resolution Imaging (HiRISE)  
Details regarding the two types of materials detected on Phobos were further examined using enhanced-color images from HiRISE (High-Resolution Imaging Science Experiment) (Figure 8).  
Despite the high detail of the images, neither a straightforward spatial distribution nor a stratigraphic relationship between the \"red\" and \"blue\" materials was observed. Some boundaries between them appear relatively well-defined (Figure 8B), while others are blurry (Figure 8A). The red material dominates within craters of all sizes (Figure 8C), except for the eastern rim of the Stickney crater. There is also no apparent color-age relationship; fresh (recent) craters expose both types of materials in various locations (red and blue arrows in Figure 8) [35].  
Considering the observed spatial relationships, it is challenging to formulate a single model in which the red unit is predominantly depositional or originates from the blue unit due to cosmic weathering [35]. The preferred model for Phobos, consistent with the currently available observational data, suggests that it is primarily a heterogeneous object composed of a mixture of red and blue material blocks of varying sizes (Figure 9) [12].  
Figure 8: (**A–C**) HiRISE PSP_007769_9010_IRB–”red” and “blue” surface units (red and blue arrows—craters located, respectively, in the “red” and “blue” unit; black arrows—exposed rocks). Subsection D illustrating relations between materials along the rim of Stickney [35].  
It is worth emphasizing here that even if the above model is correct, the composition of these two components and how the material was mixed and transformed remain unknown [35].
## 4 Experimental Results
### The Planetary Gear Train Optimization
Specifically, it yields a 35% decrease in gearbox volume and a 27.5% reduction in center distance, which directly corresponds to gearbox size, and a 2.56% gain in gearbox efficiency. By analyzing the Pareto fronts on Figures 6-9, we can see that the NSGA-II is not always able to obtain the whole Pareto-optimal set. Comparing the spacing values obtained in 3D Pareto fronts, we can see that the proposed algorithm is able to achieve higher spacing, which is more appropriate for complex design processes. Overall, the results show that the proposed algorithm offers significant improvements in terms of gearbox size, efficiency, and spacing compared to the NSGA-II. The reduction in gearbox volume and center distance not only reduces the overall size of the gearbox but also contributes to improved efficiency. Additionally, the higher spacing achieved using the proposed algorithm indicates that it is better suited to complex design processes, making it a more reliable and effective solution.
## 2 Methodology
### Monitoring of Grouting Compactness Based on Multi-Sensing EMI Method  
The schematic of the principle of monitoring of grouting compactness based on the multi-sensing EMI method is shown in Figure 3. Here, an example of three PZT patches connected in series is introduced. Three PZT patches of different sizes are installed at the bottom, middle, and top of the duct and connected to the impedance analyzer through a series connection. The impedance signatures of the PZT patches that are connected in series and mounted onto an empty duct are obtained by triggering a single sweep signal, as shown in Figure 4. We can clearly see the three peaks P1, P2, and P3 in the impedance signature, which respectively correspond to the mechanical characteristics of structures near PZT1, PZT2, and PZT3. According to the aforementioned analysis in Section 2.1, as the grout compactness increases, the stiffness of the prestressed duct will increase, which will be reflected by the changes of the impedance signatures at different frequency peaks. Additionally, since the EMI method has different sensitivities to different regions, when the grouting reaches a certain level, the impedance signals at different peak frequencies change differently. As shown in Figures 3 and 4, when the grouting compactness is greater than 50% and less than 100%, the impedance corresponding to the peak frequency P1 and P2 changes greatly, while the impedance signal corresponding to the peak frequency P3 changes slightly.  
Figure 3: The schematic principle of monitoring of grouting compactness based on the multi-sensing EMI method. (**a**) Schematic; (**b**) Connection diagram of PZT patches.  
Figure 2: The schematic diagram of the multi-sensing electro-mechanical impedance (EMI) method.  
To determine the variation of the impedance signatures at different frequency peaks quantitatively, the 3-dB mean absolute percentage deviation (MAPD)-based index is proposed. The 3-dB MAPD is a MAPD value of the impedance in the 3-dB bandwidth of peak frequency, which is shown in Figure 4. The MAPD is defined as follows [70]:  
\[MAPD(\%)=\!\frac{1}{N}\!\sum_{i=1}^{N}\left|\frac{Re(Z_{i}^{\star})-Re(Z_{i})}{ Re(Z_{i})}\right|, \tag{2}\]  
where \(N\) represents the total number of sampling points of the impedance signature, \(Z_{i}\) represents the impedance signature in empty grouting compactness for the \(i\)th frequency, and \(Z_{i}^{\star}\) represents the actual impedance signature for the \(i\)th frequency.
## 3 Results and Discussion
### True Positive Rate Studies of Military Explosives  
For the military explosives study, 1 min dynamic extractions followed by IMS detection using the portable system was performed with a maximum of 12 replicates for the different explosives available. Most of the military explosives were not detected in the portable IMS system due to their low vapor pressure. A true positive rate of 0 was determined for the vapor sampling of 500 mg of ETN, PETN and RDX in a 3-4 L plastic container. Volatile explosives such as NG and EGDN resulted in excellent detection performance with a TPR of 1.0 with EDGN and NG producing an alarm in the IMS for the NITRO alarm set. The high volatility of these explosives resulted in relatively large amounts of the volatiles to be preconcentrated onto the PSPME device. Additionally, wrappers of explosives were sampled resulting in a TPR of 0.60 for TNT, in which the alarm was based on the detection of 2,4-DNT, the primary volatile organic compound associated with TNT [32] from the headspace of the wrappers. The C4 explosives are primarily composed of RDX, thus, resulting in no detection of explosives from the wrappers. Detection of the 2,3-dimethyl-2,3-dinitrobutane (DMNB) taggant present in plastic explosives was possible; however, when a lower drift tube temperature was used as previously reported [33].
## 2 Problem Definition  
This problem derives from a real case study concerning a small medium service enterprise, which manages domestic gas utilities in the metropolitan area of Genoa (Italy). The problem it faces is quite complex and is related to its field service operations of utility networks in the metropolitan area. The heuristic approach proposed in this paper is based on the CVRP with time constraints, and it exploits the well-known cluster-first-route-second problem decomposition technique to minimize the distance within the cluster by generalized assignment problem.  
The study deals with the planning of service visits to a set of customers, performed by a set of company operators, each using a vehicle from the company fleet.  
The service company provides two types of operations: Activation and Deactivation. Activation is when a new customer opens an account and deactivation is when a customer closes an account. Activations and deactivations are fixed for a specific day by the gas company provider who arranges an appointment with the customers within a specific time window.  
The fleet is a set of vehicles each associated with a single driver whose workload should not exceed two-time based restrictions:  
* A maximum daily workload (visits per day);
* A maximum workload for the time window (time allotted per visit).  
Hence, the problem consists of finding a method to optimize company logistic resources, while maintaining a maximum level of customer satisfaction in the most economical way.  
This algorithm improves the distribution efficiency and increases customer service quality using the aforementioned heuristics, the Saving Algorithm and 2-Opt.  
The algorithm assigns a sequence of interventions to the various operators, which are carried out during the working day (divided into time slots) therefore minimizing the total distance traveled in compliance with the following operational constraints:  
* Activation: time necessary for the operation;
* Deactivation: time necessary for the operation;
* Each operation (activation and deactivation) has an account number ID;
* Location expressed in terms of GIS coordinates (latitude and longitude);
* Time Window (TW) of the operation (Table 1);
* Total time of all operations (activation and deactivation).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Preset Daily Time Windows (TW)** \\ \hline TW1 & 8:00–10:00 \\ TW2 & 9:00–11:00 \\ TW3 & 10:00–12:00 \\ TW4 & 13:00–15:00 \\ TW5 & 14:00–16:00 \\ TW6 & 15:00–17:00 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Time windows considered.  
The algorithm calculates the distances between the various interventions and provides a solution in terms of:  
* A list of interventions for each operator consisting of an account number ID, location and time window for each customer is assigned;
* The total time of each intervention (including transport time).
# The Effects of Chewing Exercises on Masticatory Function after Surgical Orthodontic Treatment
## 1 Introduction  
One of the most important objectives of orthognathic surgery is the recovery of oral function. Numerous investigators have reported on oral function, including masticatory efficiency [1, 2, 3, 4, 5, 6], muscle of mastication activity [5, 7, 8], occlusal force [2, 3, 9, 10, 11, 12], masticatory movement [8, 13, 14], and occlusal contacts [3, 4, 10, 11, 15], following orthognathic surgery. After sagittal split ramus osteotomy (SSRO), the displacement of the proximal bone fragment often causes the condyle to move in the mandibular fossa, affecting the stability of the postoperative orthodontic treatment.  
[PERSON] et al. [16] reported that the displacement of bone fragments was compensated by resorption and the addition of the condyle. Furthermore, [PERSON] et al. [4] reported a significant change in masticatory efficiency after orthognathic surgery in patients with skeletal mandibular pragnathism. Additionally, the normal chewing pattern showed a change in the condylear position compared with the abnormal chewing pattern. [PERSON] et al. [17] reported that the masticatory pattern normalizes after surgery for skeletal mandibular pragnathism, but the preoperative masticatory movement pattern tends to be retained compared with the control group. This study investigated the effects of a chewing exercise on chewing patterns and other oral functions in patients with skeletal mandibular pragnathism after SSRO.
# Multi-Device to Multi-Device (MD2 MD) Content-Centric Networking Based on Multi-RAT Device
## 3 Proposed Method
### Scenario for the Proposed Method
#### 3.2.2 Network Configuration between Devices  
We can find an example of a network configuration between devices that uses the method proposed earlier. In much the same way as in an IoT environment, it is assumed that there is a sink node, which performs the role of server or AP. The network topology is such that one sink node is connected to several nodes, in much the same way as a tree topology. The device is equipped with one or more WLAN interfaces and one Bluetooth interface. The connection between the devices is established through WLAN. If the device is equipped with two or more WLAN interfaces, it is possible to set up a wireless relay. The sink node not only establishes the AP mode, but also broadcasts Bluetooth Beacon, including information on the sink node. The distance can be estimated on the basis of the WLAN and Bluetooth Beacon signal strength. As shown in Figure 10, given rough values for the distance from the sink node, the area around the AP is divided into immediate, near, and far sections.  
The first section is immediate, which is the case when the device is inside the coverage area of the sink node and the signal from the sink node is strong. As there are several APs in this section, the communication performance declines owing to interference from the different APs. To prevent this, when the signal from the sink node is strong, other devices do not enter AP mode and connect to the sink node only. The second section is nearby,  
Because the three sections are variable depending on the location of the device, they are modified to fit each circumstance by periodically monitoring the AP signal strength and Beacon signal depending on the situation, as shown in Figure 11. By using Bluetooth Beacon, it is possible to identify the sink node and the device. Since Bluetooth Beacon is suitable for identifying a location, it is possible to more accurately estimate the distance between devices. When a WLAN interface is being used, the Bluetooth interface instead performs a scanning process. For these reasons, the proposed network configuration scenario is suitable for an IoT environment incorporating many mobile devices. This scenario can be adopted when transmitting and receiving content between people having similar interests. For example, it would be possible to share materials such as lecture notes and reference materials.
## 3 Materials and Methods
### Synthesis of N-(4-antipyrinylthiazole-2-yl)benzofuran-2-carboxamide (14)  
A solution of chloroacetamido derivative **1** (2 mmol, 0.72 g) and salicaldehyde (2 mmol, 0.21 mL) in 15 mL DMSO containing anhydrous potassium carbonate (2 mmol, 0.28 g) was stirred at room temperature for 8 h; after which, the reaction mixture was poured into ice-cooled water and neutralized by diluted HCl. The precipitate was filtered off and recrystallized from methanol to afford thiazolyl-benzofuran derivative **14**. Grey solid, yield = 64%, m.p. = 187-188 \"C. IR (\(\overline{v}\)/cm\({}^{-1}\)): 3369 (N-H), 1689 (C=O). 1H-NMR (DMSO-\(d_{6}\)): 8 2.62 (s, 3H, CH\({}_{3}\)), 3.33 (s, 3H, CH\({}_{3}\)), 7.28 (s, 1H, thiazole-H5), 7.36-7.71 (m, 10H, Ar-H and furan-H3), 12.31 (s, 1H, NH). 13C-NMR (DMSO-\(d_{6}\)): 8 12.24, 34.35, 106.75, 108.84, 112.36, 115.27, 120.04, 122.17, 122.58 (2C), 123.80, 124.91, 127.38, 129.11 (2C), 133.39, 143.64, 151.48, 155.08, 157.42, 159.88, 161.40, 163.71. Analysis for C\({}_{23}\)H\({}_{18}\)N\({}_{4}\)O\({}_{5}\)S (430): Calcd.: C, 64.17; H, 4.21; N, 13.02%. Found: C, 64.29; H, 4.16; N, 13.11%.
# Spreading-Based Voice Encryption by Means of OVSF Codes
## 4 Experimental Results and Analysis
### Results and Analysis
#### 4.3.2 Similarity  
Regarding the similarity between the original, encrypted, and recovered signals, the \(CC\) and the \(DL\) results are shown in Figure 3. In these results it can be seen that the similarity between the original and encrypted signal is practically null. In this case, the correlation between the two signals was practically zero (Figure 3b), while the spectral distortion between the two was concentrated around 43 dB (Figure 3c). On the other hand, the correlation between the input signal and recovered signal showed that the original content of the signal was recovered. Here it is important to highlight that the encrypted signal differed both in duration and content, with respect to the original signal. In addition, its content was unintelligible.  
Figure 2: Uncertainty analysis. Confidence range charts (95%) for Entropy and Disorder Level (\(DL\)).
## 3 Application of Hyperspectral RS into Plant RS and Military Actions  
With the advancements of optics, microelectronics, and image processing technology in the past two decades, hyperspectral imaging technology has been significantly improved and can provide details about the Earth's vegetation and battlefield space. In theory, hyperspectral reconnaissance can identify all artificial camouflage. This is because artificial camouflage materials cannot completely match with the spectral characteristics of the environmental background (e.g., vegetation) in all the hundreds of detectable subtle wavelengths [41]. Hyperspectral imaging is one of the major technological breakthroughs made by humans in the field of earth observation in the last two decades of the twentieth century. It is also the frontier technology of RS today and will be so in the next few decades [42].
# ZigBeeNet: Decrypted Zigbee IoT Network Traffic Dataset in Smart Home Environment
## 4 Fundamentals of Zigbee
### Zigbee Network Structure  
The Zigbee protocol consists of four layers [28]. The Zigbee protocol operates on the IEEE 802.15.4 standard, which specifies physical (PHY) and medium access control (MAC) layers. Zigbee defines the network (NWK) and application (APL) layers, enabling it to offer scalable and flexible solutions for wireless communication. Zigbee networks consist of three types of devices: the coordinator, router, and end device. The coordinator is the central node responsible for network creation, management, and security settings, including distributing the network key for encryption. Routers extend the network range by forwarding data packets and can also allow new devices to join the network. End devices, typically battery-operated sensors or actuators, are designed to conserve energy by sleeping between transmissions and are unable to forward packets. This multi-tier structure allows Zigbee to support a large number of devices while maintaining low power consumption and efficient communication.  
Moreover, the Zigbee network can operate in one of three primary topologies: star, tree, or mesh. Figure 1 shows different types of network topologies based on wireless Zigbee. In the star topology, all devices (end devices) communicate through a central coordinator, while in the tree topology, (end) devices communicate via a hierarchical structure of routers. Mesh topology, the most robust, allows devices (called nodes) to communicate directly with each other, extending the network range and enhancing fault tolerance by enabling multiple communication paths. Thus, the Zigbee protocol generally uses mesh topology in smart homes.  
In Zigbee, device addressing is hierarchical, with the coordinator assigned the first address, followed by routers and end devices. The coordinator initiates the network by assigning a unique 16-bit address to each device joining the network. Additionally, Zigbee supports up to 65,536 nodes in a single network, making it highly scalable. The hierarchical structure of Zigbee's addressing system, combined with its flexible topologies, ensures that networks can be dynamically expanded without sacrificing performance or reliability.