esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The Internet of things (IoT) points to billions of devices located worldwide which are connected and share their data based on the Internet. Due to the new technologies that provide cheap computer chips and universal wireless networks, it is feasible that everything from a small tablet to a very large airplane will be connected to the Internet and will be a part of the IoT. In most applications, IoT network nodes face limitations in terms of energy source and cost. Therefore, the need for innovative methods to improve quality indicators that increase the lifespan of networks is evident. Here, a novel technique is presented to increase the quality of service (QoS) in IoT using an improved meta-heuristic algorithm, called the improved seagull optimization algorithm (ISOA), along with traffic management in these networks. Based on this subject, the traffic-aware algorithm can manage the sending of packets and increase the QoS provision in terms of time to a great extent. The performance evaluation of the proposed method and comparison with the previous methods demonstrated the accuracy and efficiency of this method and its superiority over the previous works.  
IoT; ISOA; QoS; quality indicators +
Footnote †: journal:  
designing an energy-saving network by considering the average implementation time, makespan, network cost, and virtual machine computing cost.  
Due to their large number, small size, and contingent placement, network nodes still rely on low-power batteries for energy [4]. In IoT networks, energy limitation is a significant issue [5]. Due to their use in harsh and inaccessible environments, network nodes cannot be recharged or replaced. Since IoT efficiency depends on network lifetime and coverage, energy-saving algorithms must be included in long-lived IoT networks [6]. Dynamic power management methods that reduce IoT networks' energy consumption after design and placement are crucial. The ideal IoT uses less energy through smart planning, receives data quickly and precisely over a long period, and requires no maintenance [7].  
So far, many researchers have investigated the challenges of improving quality indicators of IoT networks, and in some of them, prioritization has been done between these challenges [8]. In this research, the improvement of qualitative indicators, including average implementation time, makespan, network cost, and virtual machine computing cost of the network will be investigated using an improved design of a seagull optimizer. The improved seagull optimization algorithm is used for minimizing the mentioned qualitative indicators under investigation to provide an energy-saving system with lower losses.
# Dual Partial Reversible Data Hiding Using Enhanced Hamming Code
## 2 Preliminaries
### [PERSON] et al.'s RDH  
[PERSON] et al. [25] proposed an embedding procedure based on dual images utilizing the Hamming Code (7,4). Given a cover image \(\mathbf{I}\), two identical copies \(\mathbf{I}_{1}\) and \(\mathbf{I}_{2}\) are generated. For the first cover image \(\mathbf{I}_{1}\), a block \(B_{1j}\) of size \(1\times 7\) is selected and transformed into a codeword \(y_{j}\). Binary hidden bits \(\delta_{j}\) are embedded into \(y_{j}\) by flipping its syndrome, computed using Equation (1) with \(\delta_{j}\).  
To apply changes to \(y_{j}\), the new syndrome is calculated as \(\tilde{s}_{j}=s_{j}\oplus\delta_{j}\). The new syndrome \(\tilde{s}_{j}\) is then converted into a decimal value \(D(\tilde{s}_{j})\), with \(y_{j}\) modified as follows (Equation (3)):  
\[y_{j,i}=\begin{cases}y_{j,i},&\text{if }i\
eq D(\tilde{s}_{j})\\ 1-y_{j,i},&\text{if }i=D(\tilde{s}_{j})\end{cases} \tag{3}\]  
where \(i=1,2,\ldots,7\). For example, if \(D(\tilde{s}_{j})=3\), then the least significant bit (LSB) of the third pixel in the block \(y_{j}\) is flipped. The resulting new block \(B^{\prime}_{1j}\) updates the corresponding block \(B_{1j}\) in the image \(\mathbf{I}_{1}\).  
To construct the second stego image \(\mathbf{I}_{2}\), the embedding process involves modifying \(B_{2j}\) based on the hidden bits \(\delta_{j}\). First, the block \(B_{2j}\) is converted into \(t_{j}\), representing the LSBs of the first three pixels of \(B_{2j}\). The hidden bit \(\delta_{j}\) is embedded into \(t_{j}\) by applying \(t_{j}=t_{j}\oplus\delta_{j}\). The modified blocks \(B^{\prime}_{2j}\) are then used to reconstruct the stego image \(\mathbf{I}^{\prime}_{2}\).  
Based on this background knowledge, the following section provides a detailed explanation of the proposed dual PRDH method.
# Delay Management in Regional Railway Transport
## 2 Materials and Methods
### The Maximum Transfer Waiting Times Principle Based on Modified CPM
To increaseof the transfer waiting time \(w_{t,i}\), for which evaluation \(l(d_{t,i})\) is determined, it is necessary, for each limiting path \(m_{lim}[d_{t,i},d_{t^{*},i^{*}}]\), where \(d_{t^{*},i^{*}}\in I_{w}\) and \(l(d_{t^{*},i^{*}})=l_{IN}(d_{t^{*},i^{*}})\), to drop from the operation/technological processes graph at least one transfer binding edge \(t_{u_{1}-u_{2},i}\in m_{lim}[d_{t,i},d_{t^{*},i^{*}}]\). If vertex \(d_{t,i}\in I_{w}\), the increasing of \(w_{t,i}\) is only possible if \(l(d_{t,i})<l_{IN}(d_{t,i})\). Dropping of an edge means resignation from the corresponding transfer binding when connecting train \(u_{1}\) is so delayed at arrival to traffic point \(j\) that continuing train \(u_{2}\) does not wait any longer for connecting train \(u_{1}\). After dropping corresponding edges, it is necessary to repeat the algorithm from step 2. If no edge was dropped, the algorithm is finished and \(w_{t,i}\) are the resulting transfer waiting times. The disadvantage of this algorithm could be its usage for assigning the transfer waiting times while having a high number of different usable disposal measures, especially if they are mutually combined. A combinatorically difficult (NP-complete) task then arises, where it is necessary to form additional different operational/technological processes graphs for one task, which must be assessed. It can be limited to using only those disposal measures that are relevant to those delay values, that are expected and assumed considering the assigned input transfer waiting times, or that enable the maximization of the transfer waiting times. In more complex nets, the only solution for coping with the difficultness of the task and the high number of usable disposal measure combinations might be railway operation simulation [17; 26], because a microsimulation proceeds autonomously (eventually based on assigned input suppositions) to use appropriate disposal measures and their combination according to the delay amounts of particular trains. Then, the result is not the determination of optimal transfer waiting times, but the takeover of tolerable delay values from performed microsimulation, which still ensures convergence of the operation to the scheduled timetable. The principle of maximum transfer waiting times described in this section does not solve the passenger transportation issue, which can sometimes lead to:  
* transfer waiting times that are too short (e.g., for trains where almost all passengers are transferring from connecting trains),
* transfer waiting times that are too long (e.g., for trains where most of the passengers are not transferring from other trains). However, dealing with the transportation aspect is not a goal of this research.
# A Nonlinear Optimization Design Algorithm for Nearly Linear-Phase 2D IIR Digital Filters
## 2 Formulation of the Design Problem
### Passband Group Delay Deviations  
Let \(\phi(c,w_{1},w_{2})\) and \(\psi(c,w_{1},w_{2})\) be the phase angles of \(N(e^{\mathrm{i}w_{1}},e^{\mathrm{i}w_{2}},b)\) and \(D(e^{\mathrm{i}w_{1}},e^{\mathrm{i}w_{2}},a)\), respectively. The group delay of the transfer function \(H(c,z_{1},z_{2})\) is given as a partial derivative with respect to \(w_{1}\) and \(w_{2}\) as follows:  
\[\tau_{1}(c,w_{1},w_{2}) =\frac{\partial\phi(c,w_{1},w_{2})}{\partial w_{1}}-\frac{ \partial\psi(c,w_{1},w_{2})}{\partial w_{1}},\text{ and }\] \[\tau_{2}(c,w_{1},w_{2}) =\frac{\partial\phi(c,w_{1},w_{2})}{\partial w_{2}}-\frac{ \partial\psi(c,w_{1},w_{2})}{\partial w_{2}}.\]  
Letting \(\tau_{10}\) and \(\tau_{20}\) be the intended average passband group delays of the initial filter in each dimension over a domain \(\Omega_{pb}\), the deviation can be computed as follows:  
\[e_{g_{i}}(x,w_{1},w_{2})= \tau_{i}(x,w_{1},w_{2})-\tau_{i0},\] \[\text{for }i=1,2,\forall(w_{1},w_{2})\in\Omega_{pb}, \tag{8}\]  
where \(x=[c^{T}\tau_{10}\tau_{20}]^{T}\), and \(\Omega_{pb}\) is the passband region [44].  
At the start of the optimization, \(\tau_{10}\) and \(\tau_{20}\) can be assigned values equal to the average group delays of the initial filter. As the optimization progresses, their values can be adjusted along with the coefficients of the filter so as to minimize the group delay deviation. The end result will be that \(\tau_{10}\) and \(\tau_{20}\) will track the average value of the group delays in each dimension. As such, we have added them to the vector of variables to be optimized.
# Structural Correlation Based Method for Image Forgery Classification and Localization
## 7 Conclusions  
This paper introduces a novel method to detect and localize authentic images and two types of tampered images: copy-move and spliced images. We propose a robust algorithm to divide relevant images into cluster using BOF and HE based image retrieval. From image clusters, by exploiting the structural correlation between images, the proposed algorithm determines the cluster centroid, which is the only authentic image in the cluster. Afterwards, the image forgery are classified, and the forged regions are localized. The experimental results show that this method achieves higher performance in both forgery detection and localization in comparison with state-of-the-art methods. Notably, the proposed method can indicate the source and target regions of copy-move images.  
Conceptualization, N.T.P., J.-W.L., and C.-S.P.; methodology, N.T.P.; software, N.T.P.; formal analysis, N.T.P.; resources, J.-WL.; data curation, N.T.P.; writing--original draft preparation, N.T.P.; writing--review and editing, J.-W.L. and C.-S.P.; visualization, N.T.P.; supervision, J.-W.L. and C.-S.P.; project administration, J.-WL. and C.-S.P.; funding acquisition, J.-WL. and C.-S.P. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2020-2016-0-00312) supervised by the IITP (Institute for Information & communications Technology Planning & Evaluation). This work was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (NRF-2019R1F1A1055593).  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline  & \(\mathbf{L_{P}}\) & \(\mathbf{L_{R}}\) & \(\mathbf{L_{F}}\) \\ \hline [PERSON] et al. [18] & 77.38 & 59.15 & 67.05 \\ [PERSON] et al. [24] & 81.87 & 61.34 & 70.13 \\ [PERSON] et al. [22] & 67.83 & 85.69 & 75.72 \\ Proposed method & 79.2 & 73.8 & **76.4** \\ \hline \end{tabular}
\end{table}
Table 6: Performance of copy-move images localization on CASIA 2 (%).  
Figure 9: Examples of splicing forgery detection in CASIA 2 dataset. Columns (**a–e**): copy-move images, columns (**d–f**): spliced images. First row: original images, second row: spliced images, third row: ground truth images, and forth row: tampered regions detected by the proposed method.
## 2 Islanding Detection Methods
### Active Methods
#### 2.2.9 Phase-Locked Loop Perturbation Method (PLL)  
By adding the second harmonic components to the inverter reference current, this technique relies on constructing a modified current reference angle. When the main grid is linked, the main grid stabilizes the voltage, which prevents any discernible changes in the generated current. However, when the main grid connection is lost, due to the introduction of second harmonic components, a variation in the voltage at the PCC can be identified, and as a result, the islanding may be identified appropriately [25].  
Various types of PLL can be used for islanding detection. Some methods involve modifying the PLL to continually shift the steady equilibrium point and other methods modifying the characteristics of the PLL small-signal to get the instability performance monotone during the islanding conditions. The modified PLL with a small-signal feed-forward loop is encouraging because the impact of the feed-forward loop can be decoupled by the stiff grid to secure the stability of the PLL in grid-tied environments [26].  
This technique, which can be applied to systems with several parallel-connected inverters, has a tiny NDZ and a small detection error rate. However, compared to other active methods, this method may cause a small disturbance when the main grid is connected.
# Mapping Rice Fields in Urban Shanghai, Southeast China, Using Sentinel-1A and Landsat 8 Datasets
## 5 Conclusions
Moreover, our study, having shown the VH polarization to increase sharply after planting and decrease steadily at heading, demonstrates the potential of Sentinel-1A in monitoring other rice biophysical parameters that show similar temporal behaviors such as above ground biomass and leaf area index. Hence, this scientific contribution would be of much interest to the agricultural remote sensing community. This study was conducted with funds from the Agricultural Project for Scientific and Technological Research (APSTR) of Shanghai, China (Grant No. 2011-2-11), and the National Key Research and Development Plan of China (Grant No. 2016 FYD0300601) which also covered the cost of open access publication. We thank Dr. [PERSON] of the School of Earth Science and Resources of Chang'an University, China, for his help in downloading the Sentinel-1A data while pursuing his post-doctoral research at the University of Alberta, Canada. Our sincere thanks also, to students in the Key Laboratory of Agricultural Remote Sensing and Information Systems of Zhejiang University, for their assistance during field campaigns, and to the anonymous reviewers whose advice and comments were instrumental in shaping the contents and structure of this article. [PERSON] and [PERSON] conceived and designed the study, processed and analyzed the data, and wrote the article; [PERSON] assisted in data analysis; [PERSON] led the field campaigns; and [PERSON] provided relevant reference information and assisted in the preparation of the article. The authors declare no conflict of interest.
## 2 Smart Grids System
### Distributed Energy Resource  
DER are energy generating and storage systems that supply power where required. DER systems, which produce less than 10 megawatts (MWs) of power, may generally be scaled to fit your specific needs and can be installed on-site. Therefore, one single source is limited and can probably be costly, whereas to achieve efficient energy storage, a combination of all technologies is required. Power conversion systems for storage purposes must also be considered [120]. This is required to increase their control and dependability, as well as to ensure that storage systems are properly integrated into power networks [121]. A next-generation SG without energy storage is similar to a computer without a hard drive--severely limited [122]. A suitable EMS is required to obtain the optimum performance for clusters of distributed energy resources (DERs). The multi-agent systems (MASs) paradigm, as utilized and described, may be used to organize distributed control methods [123]. Some of the benefits of employing MASs for successful, intelligent grid operation in the energy market are discussed in [124; 125]. The MAS application reduces the overall cost of power system production, integrated microgrids, comprised dispersed resources, and lumped loads [126]. To maximize the hybrid RE production system's economic performance and energy quality, a hybrid immune-system-based PSO was presented and applied to reduce fuel cost in the generating process [126]. Conversely, the distribution system operator (DSO) can dispatch at least a portion of the DERs; implementation of a coordinated integration of the various DERs recommends a centralized method. The best operating strategy of the DER system is generally analyzed by using a multi-objective linear programming methodology in centralized control methods [127]. The combination of the energy costs with the reduction of environmental effects suggest reducing operational costs, including energy losses, curtailed energy, reactive support, and shed energy [128; 129]. Additionally implemented is a two-stage short-term scheduling process. The first task is to create a day-ahead scheduler to optimize
# A Location Routing Problem with Time Windows Consideration: A Metaheuristics Approach
## 2 Materials and Methods
### Proposed Method
#### 2.2.5 Local Search  
The VNSPR frameworks include variable neighborhood descent (VND) as a local search component to enhance the performance of the VNS. It is inspired by a similar approach by [PERSON] and [PERSON] [49]. The neighborhood was chosen by incorporating the VND method's consecutive deterministic order of neighborhoods [53; 54; 55]. Its processes are involved because the neighborhood structures are investigated in the given order, one by one. Unless it finds a local optimum with regard to the neighborhood \(N_{k}\), the algorithm moves to a neighborhood \(N_{k+1}\). Otherwise, it goes back to the first one, \(N_{1}\). When \(k\) reaches the \(k^{\prime}\), the algorithm will be terminated. This \(k^{\prime}\)_max_ is set to a fixed value of five. The effectiveness of VND is equivalent to the order in which neighborhoods are considered and the order in which solutions are explored in each neighborhood. The neighborhoods are ranked according to their increasing cardinality.
## 1 Introduction
### Review of State-of-the-Art Detection Methods
#### 1.2.2 Constant False Alarm Rate (CFAR) Detectors  
CFAR detectors adapt the detection threshold based on the estimated local noise floor. Variants such as Cell-Averaging CFAR (CA-CFAR), Greatest-Of CFAR (GO-CFAR), and Ordered-Statistic CFAR (OS-CFAR) have been widely used in radar applications [4]. These techniques provide a trade-off between detection probability and false alarm control. However, they may underperform in nonhomogeneous environments or when targets are closely spaced, where guard and training cells may contain target energy or clutter.
## 1 Introduction
### Asynchronous Data Stream  
Most of the previous work on data streams assume that items in a data stream are synchronous in the sense that the order of their arrivals is the same as the order of their creations. This synchronous model is however not suitable to applications that are distributed in nature. For example, in a sensor network, the sink collects data transmitted from sensors over a large area, and the data transmitted from different sensors would suffer different delay. It is possible that an item created at time \(t\) at a certain sensor may arrive at the sink later than an item created after \(t\) at another sensor. From the sink's viewpoint, items in the data stream are out of order with respect to their creation times. Yet the statistics to be computed are usually based on the creation times. More specifically, an _asynchronous data stream_ (a.k.a. _out-of-order data stream_) [1; 10; 11] can be considered as a sequence \((a_{1},t_{1}),(a_{2},t_{2}),(a_{3},t_{3}),\ldots\), where \(a_{i}\) is the name of a data item chosen from a fixed universe \(U\), and \(t_{i}\) is an integer timestamp recording the creation time of this item. Items arriving at the data stream are in arbitrary order regarding their timestamps, and it is possible that more than one data item has the same timestamp.
# Effects of Isochoric Freezing on the Quality Characteristics of Raw Bovine Milk
###### Abstract  
This study investigated the effects of isochoric freezing (IF) on the shelf-life and quality of raw bovine milk over a 5-week period. The results were compared with conventional refrigeration (RF) and refrigeration after pasteurization (HTST). The IF treatment process entailed storing liquid raw milk in isochoric chambers in thermodynamic equilibrium at \(-5\,\mathrm{\SIUnitSymbolCelsius}/77\) MPa and \(-10\,\mathrm{\SIUnitSymbolCelsius}/96\) MPa. Several parameters were analyzed, including microbiology count, physicochemical properties, indigenous enzyme activity, protein content, volatile organic compounds profile, and lipid degradation. Both raw and pasteurized milk experienced increases in the microbial level past the acceptable threshold (\(\geq\)5.5 log CFU/mL) after 2 weeks and 5 weeks, respectively, leading to the deterioration of other parameters during storage. In comparison, microbiology count decreased significantly during storage for both IF treatment conditions but was more pronounced for the higher pressure (96 MPa) treatment, leading to undetectable levels of microorganism after 5 weeks. IF treatment maintained stable pH, titratable acidity, viscosity, lipid oxidation, volatile profiles, total protein content, and lactoperoxidase activity throughout the storage period. Color was preserved during IF treatment at \(-5\,\mathrm{\SIUnitSymbolCelsius}/77\) MPa; however, color was impacted during IF treatment at \(-10\,\mathrm{\SIUnitSymbolCelsius}/96\) MPa. Protein structures were also modified during pressurized storage in both IF treatments. Overall, the study demonstrated that isochoric freezing could significantly increase the shelf-life of milk by reducing microbiology activity, whilst maintaining its nutritional content. These results underscore the potential role of isochoric freezing as a valuable tool in eliminating pathogens while maintaining quality characteristics similar to raw milk over long storage periods.  
isochoric freezing bovine milk microbial inactivation; shelf-life; volatile compounds; thermal pasteurization; protein denaturation 2023  
15 October 2023  
6 November 2023  
14 November 2023  
16 November 2023
# Short Text Clustering Algorithms, Application and Challenges: A Survey
## 3 Components of Short Text Clustering
### External Knowledge
#### 1.1 WordNet  
It is defined as a vast lexical database. Nouns, verbs, adverbs and adjectives are grouped into sets of cognitive synonyms. Each of these expresses a distinct concept. Conceptual-semantic and linguistic relationships link synsets together [96]. WordNet is helpful for computational linguistics and NLP because of its structure.  
WordNet resembles a thesaurus because it groups words depending on their meanings. Nevertheless, some key distinctions are noted. Firstly, WordNet connects not just word forms--letter strings--but also precise meanings of words. Therefore, words in the network close to one another are semantically disambiguated. Secondly, WordNet labels the semantic relationships between words, whereas thesaurus groupings follow no defined pattern other than meaning similarity. One issue with using WordNet is that it does not cover the most recent topics.