esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Study Design and Sampling  
Salmon, carp and trout meat smoked by hot smoking in an industrial fish smoking plant in Serbia were used in this study. The smoked meat of each species was cut into pieces of at least 10 g each, divided into two groups and packed as follows: samples from the first group included salmon (36 samples), carp (36 samples) and trout (36 samples) meat packed in six different plastic packages, including polyethylene terephthalate (PET) boxes, high-density polyethylene (HDPE) bags, biodegradable HDPE, low-density polyethylene (LDPE) bags, polypropylene (PP) bags and polyamide polyethylene (PAPE) vacuum bags (90 \(\upmu\)m) (MCC Trading International GmbH, Dusseldorf, Germany). Samples packed in PET were placed in transparent boxes. Samples packed in HDPE, biodegradable HDPE, LDPE and PP were put in bags intended for food packaging, while samples packed in PAPE were also in bags, but they were vacuum-sealed. Packaged samples can be seen in Figure 1. The samples from the second group (3 salmon samples, 3 carp and 3 trout samples) were not packed in plastic packaging and served as control samples. Their fat content was also determined. After packaging, 54 samples from the first group, including salmon, carp and trout, were frozen at a temperature of -18 \({}^{\circ}\)C for 3 months, while 54 samples of the investigated fish species were stored for 6 months at -18 \({}^{\circ}\)C. The samples were analyzed in triplicate so that a total of 117 samples were processed. Each sample was analyzed for the presence of dimethyl phthalate (DMP), diethyl phthalate (DEP), diisobutyl phthalate (DiBP), dibutyl phthalate (DBP), bis(2-ethylhexyl) phthalate (DEHP) and di-\(n\)-octyl-phthalate (DnOP).
# A PEKS-Based NDN Strategy for Name Privacy
## 2 Background and Related Work
### NDN Architecture
#### 2.1.3 Routing and Forwarding  
Routing and forwarding in NDN are based on names, which eliminates the problems caused by IP addressing scheme such as managing the addresses, Network Address Translation, and IP address exhaustion. An NDN Forwarder called Named Forwarding Daemon (NFD) is responsible for forwarding packets. Every NDN router contains three types of data structures which are shown in Figure 3[2]:  
* Pending Interest Table (PIT): This table is used to store incoming interest packets which arrive at a router. The router reads the interest packet's parameters such as incoming interface, interest lifetime, and Nonce number. Then it stores them in the PIT. It also checks the name and the Nonce field of the incoming packets with PIT. If there is the same Nonce Number, the router assumes that this is an interest loop and simply discards it [2; 4; 18]. The router will then discuss the interest name with FIB and forward the packet to the nexthop.
* Content Store (CS): NDN router can cache the data packets passing through them and store those packets in CS. This property called in-network caching allows NDN hosts to share contents with low usage of bandwidth and latency [2; 4; 18].
* Forwarding Information Base (FIB): As traditional routers do, NDN router needs to have the information that shows where the incoming interest packets should be forwarded. Therefore, the role of FIB is to record the information for the name to reach the destination. The FIB can be updated by routing protocol such as OSPF based Routing Protocol for NDN (OSPFN) [2; 4; 18].  
Figure 2: Signed interest name format [17].
## 6 Conclusions  
The diverse analyses conducted in this study enabled us to characterize the dynamic temporal patterns of fire occurrence and their relationship with drought in the Basilicata region of Southern Italy. We discovered that the fire sequence exhibits clustering over time, with characteristic timescales ranging from weekly, likely stemming from anthropogenic causes, to 3 months, attributed to meteorological and climatic influences. These distinct timescales overlay a clustered, non-Poissonian behavior that exhibits fractal properties between them. The comparative analysis of the monthly number of fires and the total burned area reveals a strong correlation between them. This correlation is further supported by the common cycles, especially the annual one evident in the second IMF of their EMD. This cycle clearly originates from meteorological factors, as it mirrors the annual cycle of the meteo-climatic conditions of the region under study, featured by the Mediterranean climate. The links between the SPEI and fire activity were investigated by analyzing the correlations between SPEI values (computed over accumulation periods ranging from 1 to 48 months) and the IMFs obtained from the EMD of both the monthly number of fires and the total burned area; the correlation analysis unveiled that the accumulation time of the SPEI corresponds to the cycle length of the fire time series: the longer the cycle of the fire time series, the higher the accumulation time of the SPEI (Figure 13).  
Conceptualization, L.T. and R.L.; Methodology, L.T.; Software, L.T.; Formal analysis, L.T.; Investigation, L.T. and R.L.; Resources, R.L.; Data curation, R.L.; Writing--original draft, L.T.; Writing--review & editing, L.T. and R.L.; Visualization, L.T.; Project administration, R.L.; Funding acquisition, R.L. All authors have read and agreed to the published version of the manuscript.  
Figure 13: Dispersion plot between the highest cycles of monthly fire counts and total burned area and the accumulation time that exhibits the maximum absolute value of Pearson’s correlation (with \(p\)-value less than 0.05), as illustrated in Figures 10 and 11.  
**Funding:** This research was supported by the project DRAGON 6 project ID 95387 Multi-Sensor Remote Sensing for Cultural Heritage Climate Change Resilience funded by European Space Agency and by the project Fireast (2024-2025) funded by Civil Protection Department of Regione Bassilicata.  
**Data Availability Statement:** The catalogue of forest fires is freely available at [[https://rsdi.regione.basilica.it/](https://rsdi.regione.basilica.it/)]([https://rsdi.regione.basilica.it/](https://rsdi.regione.basilica.it/)) (accessed on 20 March 2025). The SPEI data are available at [[https://spei.csic.es/](https://spei.csic.es/)]([https://spei.csic.es/](https://spei.csic.es/)) (accessed on 20 March 2025).  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 4 Case Study
### The Energy Saving Effect Test Based on Field Data  
To obtain the actual value of the energy-saving effect, we performed our optimization strategies in a pilot project in Guangdong province. The project is located in Guangzhou, Guangdong province, China, and the electrical appliances are listed in Table 3.  
To realize a real-time collection of power consumption data and remote control, smart plugs were installed for the time-type equipment (water dispensers, lighting equipment) in the pilot project, and smart plugs and intelligent infrared controllers were installed for the temperature-type equipment (air conditioners). Additionally, the temperature sensors were set to monitor the indoor and outdoor temperature in real time. Acoustic sensors were installed to monitor lighting requirements in real time.  
Then, the above-mentioned energy-saving optimization algorithms were performed, and the power consumption for one week of the station was calculated before and after the implementation of the automatic energy-saving optimization strategy. Based on this, the energy-saving related indicators were computed. The results are shown in Table 4.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type of Area** & **Appliances** & **Number** \\ \hline Hyperbaric chamber/Central control room & Air conditioner & 6 \\ Office & Air conditioner & 3 \\ Office & Water dispenser & 3 \\ Outdoor area & Lighting & 5 \\ Indoor area & Lighting & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Electrical appliances in the pilot project.  
Figure 7: Energy saving optimization for air conditioning. (**a**) Indoor temperature of a certain office. (**b**) The optimal set temperature of the office. (**c**) Indoor temperature in a hyperbaric chamber. (**d**) The optimal set temperature of the hyperbaric chamber.  
As can be seen from Table 4, the most crucial energy-saving source of the pilot project is air conditioning. The energy-saving effect of air-conditioning is very significant, and can even reach 65.6%, which is much higher than the energy-saving level in the optimization simulation. This is because the optimization strategy is implemented in winter, when the outdoor temperature is within the interval of [20 \({}^{\circ}\)C, 28 \({}^{\circ}\)C], and before optimization the setting temperature was 22 \({}^{\circ}\)C. This is approximate to the outdoor temperature, so the operation of air conditioning is not necessary most of the time, resulting in a large amount of waste. After implementing the optimization strategy, most of the meaningless cooling operation power and invalid standby power was saved. Taking the three air conditioners in the central control room as an example, the operation before and after energy saving is shown in Figure 8.
# Broadcasting in Stars of Cliques and Path-Connected Cliques +
## 4 Decision Algorithm for the Broadcast Time of Star of Cliques
### Bounds on Broadcast Time from an Arbitrary Originator  
Consider \(G_{k}\) as a star of cliques with a universal vertex \(u\), and let \(v\) be an arbitrary originator such that \(v\
eq u\) and \(v\in C_{i}\), where \(1\leq i\leq k\). Consider the broadcast scheme for an arbitrary originator presented in Algorithm 4. In an optimal broadcast scheme, \(v\) will transmit the message to \(u\) at time unit \(1\), as \(u\) is directly connected to every vertex in \(G_{k}\). If \(v\in C_{1}\), then \(b(v,G_{k})=b(u,G_{k})\), because, after the first time unit, the two informed vertices in \(G_{k}\) are \(v\) and \(u\). Similarly, if \(C_{i}\
eq C_{1}\) but \(l_{i}=l_{1}\), it follows that \(b(v,G_{k})=b(u,G_{k})\) since the broadcast scheme can interchange the roles of \(C_{i}\) and \(C_{1}\).  
For all other cases, the following bounds apply.  
**Lemma 7**.: _Let \(G_{k}\) be a star of cliques where the originator \(v\in C_{i}\), for \(1<i\leq k\) and \(|C_{i}|<|C_{1}|\). Then, \(b(v,G_{k})\geq b(u,G_{k}\setminus C_{i})+1\)._  
**Lemma 8**.: _Let \(G_{k}\) be a star of cliques where the originator \(v\in C_{i}\), for \(1<i\leq k\) and \(|C_{i}|<|C_{1}|\). Then, \(b(v,G_{k})\leq b(u,G_{k}\setminus C_{i})+1\)._  
**Algorithm 4** Broadcast algorithm for star of cliques from an arbitrary originator \(v\)  
**Input** Star of cliques \(G_{k}\), originator vertex \(v\)  
**Output** A broadcast scheme for \(G_{k}\) from originator \(v\) with time \(b(v,G_{k})\leq b(u,G_{k}\setminus C_{i})+1\) where \(u\) is the universal vertex.  
```
1:\(v\) informs \(u\) at time \(1\)
2:\(u\) broadcasts to \(G_{k}\setminus C_{i}\) according to Algorithm 3
3:\(v\) broadcasts to \(C_{i}\)
```  
**Algorithm 5** Broadcast algorithm for star of cliques from an arbitrary originator \(v\)  
**Remark 2**.: _From Lemmata 7 and 8, we can see that when \(v\
eq u\) and \(v\in C_{i}\), \(b(v,G_{k})=b(u,G_{k}\setminus C_{i})+1\) for all \(1<i\leq k\) if \(l_{1}>l_{i}\). For all other cases, \(b(v,G_{k})=b(u,G_{k})\)._
## 2 Theory and Method
### Derivation of the Reflection Coefficient Equation
Based on [PERSON] and [PERSON]'s (2004) theory of reflection coefficient equation derivation based on seismic scattering [15], the following equation is produced:  
\[R_{\rm PP}(\theta,\phi)=\frac{1}{4\rho_{b}\cos^{2}\theta}(\Delta\rho_{b}\chi+ \Delta C\eta) \tag{4}\]  
where \(\rho_{b}\) represents the density of the medium, \(\chi\) is a parameter associated with the polarization vector, \(\eta\) is the matrix that accounts for the incident angle and azimuth angle of the seismic data, and \(\Delta C\) is the perturbation matrix of the medium. [PERSON] (1986) identified that the majority of rocks demonstrate weak anisotropy. Furthermore, studies on TTI media by [PERSON] (2004) and [PERSON] (2021) assume that such media are weakly anisotropic and have drawn reasonable conclusions [1; 15; 17]. When deriving the perturbation matrix for TTI media, we neglect the effects of \(\Delta M\delta_{N}\), \(\Delta M\delta_{T}\), \(\Delta\mu\delta_{N}\) and \(\Delta\mu\delta_{T}\) due to the assumption of weak anisotropy. Using this theoretical framework for the derivation of the reflection coefficient, the final expression for the reflection coefficient is as follows:  
\[R_{\rm PP}(\theta,\phi)=a(\theta)\frac{\Delta M}{M}+b(\theta)\frac{\Delta\mu}{ \mu}+c(\theta,\phi)\Delta\delta_{N}+d(\theta,\phi)\Delta\delta_{T} \tag{5}\]  
where  
\[ \begin{array}{l}a(\theta)=\frac{\sec^{2}\theta}{4},\quad\text{b}(\theta)=-2 g\sin^{2}\theta\\ c(\theta,\phi)=\left\{\begin{array}{l}\frac{\sin\sec^{2}\theta}{4}\big{(}1-2 g\sin^{2}\theta\big{)}^{2}\\ -\frac{\cos\xi\sec^{2}\theta}{4}\big{[}2g\big{(}\sin^{2}\theta\sin^{2}\phi+ \cos^{2}\theta\big{)}-1\big{]}^{2}\end{array}\right\}\\ d(\theta,\phi)=\big{[}g\sin\zeta\sin^{2}\theta+g\cos\zeta\sin^{2}\theta\cos^{2} \phi(1-\tan^{2}\theta\sin^{2}\phi)\big{]}\end{array} \]
## 3 Results
### Analysis the MTF of Sub-Satellite Point Imaging Interfered by Random Vibration  
When the attitude angle of satellite is \(0^{\circ}\), both the ground resolution \(GSD\) and the ground scanning velocity \(V_{c}\) are fixed values. Based on Formula (10), the exposure time is \(t=0.0051\) s. The MTF curves of different error velocities are simulated, as shown in Figure 7.  
It can be seen that, when the velocity error is caused by random vibration increases, the error image shift on the focal plane of the camera gets larger. Therefore, the MTF curve shows a significant downward trend, which means that the clarity of the output image is reduced.
## 2 Optimization
### Adaptive Learning Rate Optimizers
#### 2.2.1 Adagrad  
Adagrad optimization algorithm was first introduced by [PERSON] et al. [24]. It implements parameter-specific learning rates: corresponding learning rates of parameters that are updated more frequently are smaller and larger for parameters that are updated infrequently. The update rule for Adagrad is given by  
\[\mathbf{v}_{0} =\mathbf{0}\quad\text{(initialize squared gradient accumulator)}\] \[\mathbf{v}_{t} =\mathbf{v}_{t-1}+\left(\
abla J(\mathbf{\theta}_{t-1})\right)^{2}\quad \text{(accumulate squared gradient)} \tag{9}\] \[\mathbf{\theta}_{t} =\mathbf{\theta}_{t-1}-\frac{\eta}{\sqrt{\mathbf{v}_{t}}+\epsilon} \circ\
abla J(\mathbf{\theta}_{t-1})\quad\text{(apply update)} \tag{10}\]  
where \(\circ\) denotes Hadamard (element-wise) product and \((\
abla J(\mathbf{\theta}_{t-1}))^{2}\) denotes the element-wise square of the given gradient. Division and square root in \(\frac{\eta}{\sqrt{\mathbf{v}_{t}}+\epsilon}\) are also calculated element-wise. The \(i\)th component \(\frac{\eta}{\sqrt{\sum_{k=1}^{t}(\partial_{i}J(\mathbf{\theta}_{k-1}))^{2}+ \epsilon}}\) of the latter vector corresponds to the learning rate that is used to update parameter \(i\) in iteration \(t\). The main weakness of Adagrad optimizer is the constant growth of accumulator \(\mathbf{v}\) during the whole training process, in each iteration on \(i\)th coordinate the corresponding squared (and therefore non-negative) partial derivative of the cost function \(J\) is added, which eventually results with infinitely small learning rates approximately \(\approx\)0 that basically stops the training process.
# A New Sensor System for Accurate 3D Surface Measurements and Modeling of Underwater Objects
## 2 Materials and Methods
### New Sensor System
#### 2.1.4 Sensor Calibration  
One precondition for the 3D measurement process is the calibration of the sensor system. The parts of the UWS to be calibrated are:  
* The 3D scanning unit, consisting of two monochrome measurement cameras arranged in a stereo array;
* The color camera (to be calibrated with respect to one of the stereo cameras);
* The IMU (to be calibrated with respect to the color camera).  
In the following section, the calibration of the 3D sensor is briefly described. According to the theoretical analysis mentioned in Section 2.1.3, we selected the extended pinhole model with distortion correction function.  
Sensor calibration was performed in a water basin using a set of ArUco [44] and circle markers in a plane-near arrangement (see Figure 2). The marker boards were placed on the floor of the basin, and the sensor was positioned in certain orientations according to the scene, using a gantry system. The image data were recorded continuously in video mode. Hence, more data than necessary (from approximately ten different sensor positions) were recorded. Calibration points were selected automatically using our own software tool, and calibration parameters were calculated using the commercially available BINGO bundle adjustment software [45].  
Calibration was evaluated using consecutively performed static measurements of given specimens, such as a ball bar and a plane-normal with calibrated measures. After analysis of the evaluation measurements of the ball bar, a 3D error-compensation function was generated and used as the final part of the calibration. Results of the evaluation measurements are documented in Section 3.  
Calibration of the color camera was performed analogously using BINGO software, whereas IMU calibration was performed according to the method introduced by [PERSON] et al. [46].  
An extensive survey of different calibration procedures of underwater systems was conducted by [PERSON] [47].
###### Abstract
presented a reflective linear-to-circular polarization converter based on a graphene metasurface that can perform between 3.75 THz and 6.0 THz with a 46.15% relative bandwidth [17]. Additionally, [PERSON] et al. presented a dual-band linear-to-circular polarization converter with wide-angle and wideband axial ratio, constructed on a three-metallic-layer reflective metasurface. This has a 3 dB axial ratio over the lower-frequency bandwidth of 15-21.2 GHz (34.3%)/16.3-21.1 GHz (25.7%) and higher-frequency bandwidth of 27-30.3 GHz (11.5%)/27-29.8 GHz (9.9%) but has a poor PCR [12]. A further example is that of [PERSON] et al., who demonstrated the result of a bilayered metasurface via an ultrawideband LTC polarization converter [16]. This article proposes a high-efficiency dual-frequency wideband LTC polarization converter based on a single-layer reflective metasurface. The proposed converter can convert linearly polarized electromagnetic waves over the dual-frequency bands of 16.49-23.54 GHz and 26.44-34.56 GHz into right- and left-handed circularly polarized (RHCP/LHCP) waves under a \(y\)-polarized incident wave. The physical interpretation is given, and the efficiency is evaluated by a mathematical equation. The execution of parametric analysis provides design specifications for various frequency ranges. We employ simulations, mathematical operations, and comprehensive theoretical analysis of the dual-frequency wideband and high-efficiency LTC polarization conversion to corroborate the proposed polarization converter.
# An Optimized Load Balancing Using Firefly Algorithm in Flying Ad-Hoc Network
## 5 Conclusions  
This work provides a traffic congestion management method for FANETs based on the firefly algorithm, which may enhance throughput and restrict EED to a specific approach while controlling other parameters, including end-to-end delay, packet delivery ratio, fuel emission, and throughput. The FANET framework is specifically utilized as the mathematical version of the primal problem. In addition, to improve the end-to-end delay limitation outcome, the single-hop delay is predicted with a new form of delay value that is merged with a dual strategy to fix the optimal solution in a distributed system using the firefly algorithm and the traffic congestion control algorithm. The simulation results show that the suggested algorithms boost network throughput and reduce packet delay rates significantly. Because of the best attributes of the firefly algorithm, the proposed approach is inspired by it. Experiments are carried out on the NS2 simulator, and the performance of the proposed approach is evaluated. It is used to compute the most recent attractiveness value of the fireflies, which displays the most desirable and efficient result for the flying nodes. The performance analysis yielded expected results, demonstrating that the proposed approach is effective at optimizing schedules by balancing loads of flying nodes. More effort will be made in the future to incorporate these issues and applicable solutions into the optimization framework and transmission-related strategy.  
Conceptualization, D.P.; methodology, M.K.; validation, D.P. and M.R.; formal analysis, M.K.; writing--original draft preparation, M.K.; writing--review and editing, M.R. and Z.K.; supervision, S.S.A.; funding acquisition, A.S.A. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Taif University Research Supporting Project number (TURSP-2020/311), Taif University, Taif, Saudi Arabia.  
Figure 13: Throughput analysis of flying nodes.  
Data Availability Statement: The data used in this article will be shared by requesting to corresponding authors.
# Short-Text Semantic Similarity (STSS): Techniques, Challenges and Future Perspectives
## 8 Discussion
There is still room to improve the performance of these models for other available metrics, as is mentioned in Table 11.  
Moreover, a number of 34 studies have been included in the selection of the dataset. We added six datasets, such as movie review, TREC, AG news, Mohler, SST-2, and SciEntsBank. These datasets are suitable for short answers, movie reviews, and short text classification. The average length of these datasets is 19-20 words, and the category for responses used includes positive, negative, neutral, contradictory, three-way, and five-way. Furthermore, we selected approx. 25 studies for reviewing the challenges that are specific to short questions and answers. We have identified the number of challenges in teacher and student-provided answers. The student's answers are short, sometimes irrelevant, and unclear to understand. There is no standard rule to grade and score such answers. However, the suggested changes can also be used to address problems and broaden the research area for future studies.