esa-sceva/satcom-chunk-collection huggingface dataset:
# Joint Optimization of Data Freshness and Fidelity for Selection Combining-Based Transmissions
## 4 Problem Formulation
### MMSE of the Proposed Model  
Recall that in our model each node transmits its observation through block Rayleigh fading channel, where the channel gain within a block remains the same while it varies independently across different blocks. Based on this model, let us use the MMSE metric to evaluate the data fidelity of the system.  
**Lemma 2**.: _The MMSE \(\overline{D}(N)\) by using SC technique is:_  
\[\overline{D}(N)=\sum_{i=1}^{N}(-1)^{i-1}\binom{i}{N}i\Gamma(0, \frac{i}{\gamma_{0}})e^{i/\gamma_{0}}, \tag{13}\]  
_where \(\Gamma(a,b)=\int_{b}^{\infty}u^{a-1}e^{-u}\mathrm{d}u\) represents the incomplete Gamma function._  
Proof of Lemma 2.: Firstly, one can establish objective problem of minimizing the distortion as shown in Equation (6), which is  
\[\min_{k}\quad\mathbb{E}\big{[}(k|h_{\max}|-1)X+kZ_{i}\big{]}^{2}, \tag{14}\]  
where \(\mathbb{E}[X]^{2}=\sigma_{X}^{2}\) and \(\mathbb{E}[Z_{i}]^{2}=\sigma_{Z_{i}}^{2}\). Notice the irrelevance between source \(X\) and the noise \(Z_{i}\), the objective problem can be further elaborated as  
\[\min_{k}\quad(|h_{\max}|^{2}\sigma_{X}^{2}+\sigma_{Z_{i}}^{2})k ^{2}-2|h_{\max}|\sigma_{X}^{2}k+\sigma_{X}^{2}. \tag{15}\]  
With some manipulations, the distortion, i.e., the optimal value of Equation (15) can be written as  
\[D(N)=\frac{\sigma_{X}^{2}\sigma_{Z_{i}}^{2}}{|h_{\max}|^{2}\sigma _{X}^{2}+\sigma_{Z_{i}}^{2}}. \tag{16}\]  
In our model, we assume the noise power is the same among all the sensor nodes, i.e., \(\sigma_{Z_{i}}^{2}=\sigma_{Z}^{2}=1\). Denote the SNR of each node as \(\gamma_{0}=\sigma_{X}^{2}/\sigma_{Z}^{2}\), then the distortion is  
\[D(N)=\frac{\gamma_{0}}{|h_{\max}|^{2}\gamma_{0}+1}=\frac{1}{|h_ {\max}|^{2}+\frac{1}{\gamma_{0}}}. \tag{17}\]  
Recall \(|h_{i}|^{2}\sim\exp(1)\) and \(\max\{|h_{i}|,1\leq i\leq N\}=|h_{\max}|\). Now we need to figure out the probability density function (pdf) of \(|h_{\max}|^{2}\) to calculate the MMSE. For conciseness we denote \(|h_{\max}|^{2}\) and its corresponding pdf as \(g\) and \(p(g)\), respectively. Given \(N\) i.i.d. exponential distribution random variables with parameter \(1/\theta\), the pdf of the maximum variable among them can be derived:  
\[p(g) =\frac{N}{\theta}e^{-g/\theta}(1-e^{-g/\theta})^{N-1}\] \[=\frac{N}{\theta}e^{-g/\theta}\sum_{i=0}^{N-1}\binom{i}{N-1}(-e^ {-g/\theta})^{i}\] \[=\sum_{i=0}^{N-1}(-1)^{i}N\binom{i}{N-1}\frac{1}{\theta}e^{-g(i+ 1)/\theta}\] \[=\sum_{i=1}^{N}(-1)^{i-1}\binom{i}{N}\frac{i}{\theta}e^{-g{i}/\theta} \tag{18}\]where in our work we have \(1/\theta=1\). After that, the MMSE can be computed as:  
\[\overline{D}(N) =\int_{0}^{\infty}\frac{1}{g+\frac{1}{\tau_{0}}}p(g)\mathrm{d}g\] \[\overset{\mathrm{(a)}}{=}\sum_{i=1}^{N}(-1)^{i-1}\binom{i}{N}i \Big{(}\int_{\frac{i}{\tau_{0}}}^{\infty}\frac{1}{\omega}e^{-\omega}\mathrm{d} \omega\Big{)}e^{i/\tau_{0}}\] \[=\sum_{i=1}^{N}(-1)^{i-1}\binom{i}{N}i\Gamma(0,\frac{i}{\tau_{0} })e^{i/\tau_{0}},\]  
where (a) follows from \(\omega=(g+\frac{1}{\tau_{0}})i\) and \(\mathrm{d}\omega=i\mathrm{d}g+\frac{i}{\tau_{0}}\).
## 2 Materials and Methods
### Methods
#### 2.2.4 Qualitative and Quantitative Analysis of Volatile Components  
Variations in the dried oysters prepared through different ways of drying were analyzed using a headspace solid-phase microextraction (HS-SPME) module and gas chromatography (GC)-mass spectrometry (MS). About 5 g of fresh or 2 g of dry sample was put in a 20 mL top space vial filled with 10 \(\upmu\)L of 50 ng/mL 2-methyl-3-heptanone as an internal standard (IS). The bottle was sealed, and the aged extractor (50/30 \(\upmu\)m, 1 cm, DVB/CAR/PDMS fiber) was inserted into the bottle and left for 40 min at 70 \({}^{\circ}\)C. Then, the volatile compounds were analyzed with the GC-MS system (AOC5000-GC-MS-QP2010 Plus, Shimadzu Corporation, Kyoto, Japan) coupled with a Pure-WAX quartz capillary column (30 m \(\times\) 0.25 mm, 0.25 \(\upmu\)m, Shimadzu Corporation, Japan).  
The GC conditions were as follows: the actual temperature was 50 \({}^{\circ}\)C for 3 min, followed by ramping up to 100 \({}^{\circ}\)C, 140 \({}^{\circ}\)C, and 180 \({}^{\circ}\)C at 5 \({}^{\circ}\)C/min, 4 \({}^{\circ}\)C/min, and 4 \({}^{\circ}\)C/min, respectively, and holding time was 2, 1, and 2 min, respectively, before ramping up to 250 \({}^{\circ}\)C at 10 \({}^{\circ}\)C/min for 5 min. The carrier gas was helium at a constant flow rate of 1 mL/min, an inlet temperature of 250 \({}^{\circ}\)C, a pressure of 112.0 kPa, and no splitting. The MS conditions were as follows: the ion source and injector temperatures were 230 \({}^{\circ}\)C and 250 \({}^{\circ}\)C, respectively; A 70 eV electronic shock mass selection detector with an MS scan range of \(m/z\) 35-450 was used.  
Qualitative analysis was carried out using the following methods. The calculated retention index (RI) was compared with the Wiley07 and NIST05 databases and with the reservation index of literature reports to identify volatile mixtures. The retention index of volatile elements was determined by comparing the reservation time of C5-C32 n-alkane standards under identical analytical conditions [20].  
Quantitative analysis was performed as follows. The content of each volatile substance in each group of samples was calculated from the peak area of 2-methyl-3-heptanone at a known mass concentration using the internal-standard semiquantitative method [21; 22]. The formula was as follows:  
\[C_{i}=\frac{10\cdot C_{is}\cdot A_{j}}{A_{is}\cdot M_{s}} \tag{2}\]  
where \(C_{i}\) (ng/mg) is the concentration of the compound, \(C_{is}\) (ng/mL) is the concentration of the IS, 10 is the volume of the IS, \(A_{j}\) is the chromatographic peak scope of the compound, \(A_{is}\) is the IS peak scope of the mixture, and \(M_{s}\) (mg) is the sampling mass.
## 6 Conclusions
The daily time series plots from these reference sites indicate that the analysis process can be regarded as consistent with other research, because the dielectric constant from CYGNSS corresponds to the dielectric constant that is converted from SMAP soil moisture using the semi-empirical soil moisture model. The confirmation metrics, RMSE, ubRMSE, and Pearson correlation coefficient were used to validate the dielectric constant results. It is reported that the RMSE and ubRMSE values using dielectric constants from CYGNSS and SMAP are 5.71 and 3.31, respectively, and the Pearson correlation coefficient is approximately 0.4 in the case of analysis on Yanco. The other two locations have comparable results in the confirmation metrics to the results at Yanco. It can be concluded that the multi-step mathematical approach is well established, and it is required to include vegetation and topographical analysis to acquire a precise dielectric constant from GNSS-R. A seasonal analysis was also performed, and shows the annual variation of the derived dielectric constant by comparing it to the SMAP data. The confirmation metrics for seasonal analysis are comparable to the time series results, but there was no correlated tendency among the different analysis sites. This result means that it is necessary to determine the parameters influencing the seasonal variation in soil moisture in future analysis. The types of vegetation, surface roughness, and spatial resolution are considered as variables for improving the accuracy of soil moisture retrieval using GNSS-R instruments. Using a distinct soil moisture model that is derived from GNSS-R observation rather than in-situ measurements is also required for acquiring a precise soil moisture model.
# Enhancing Propaganda Detection in Arabic News Context Through Multi-Task Learning
## 3 Methodology
### Task-Weighting
#### 3.6.1 MTO Weighting Schemes  
Optimization techniques for training MTL models are as crucial as the design of the model architectures, as Section 3.5 describes. Several task-weighting schemes have been proposed to address challenges, such as task imbalance and the need to prioritize specific tasks [11; 48; 49]. These task-weighting schemes can be broadly categorized into two main classes: static weighting and learning (adaptive) weighting. In static weighting, the weights are fixed before training and can be categorized as follows [50]:  
* Equal weighting: This approach involves assigning uniform and constant weights to each task's loss function throughout the training process. Although conceptually straightforward and easy to implement, it inherently assumes that all tasks hold equal importance.
* Proportional weighting: This approach involves a domain expert or heuristic approach because it assigns weights to each task loss in proportion to its relative importance.  
In contrast, learning weighting treats the coefficients as learnable variables by learning the optimal weight for each task loss during training. Unlike static weighting schemes, adaptive methods dynamically update task weights based on real-time signals, potentially leading to the MTL model's improved overall performance. These adaptive weighting techniques can be broadly categorized as follows:  
* Uncertainty weighting (UW): This approach involves learning to estimate homoscedastic noise terms (uncertainty) associated with each task's loss and rescaling each loss inversely to its estimated noise. Therefore, tasks with higher uncertainty (i.e., those that are more difficult to learn) are assigned lower weights, and tasks with lower uncertainty (i.e., those that are easier to learn) are assigned higher weights. This scheme aims to minimize the overall uncertainty in the learning process, effectively prioritize more stable tasks, and down-weight those that contribute more noise or variance.
* Dynamic weighting: This approach involves reweighting tasks according to recent loss function values. Tasks that improve rapidly (i.e., those whose loss decreases quickly) may be down-weighted to prevent them from dominating the learning process, whereas tasks that improve more slowly (i.e., those whose loss decreases slowly) may be up-weighted to accelerate their learning. This scheme aims to dynamically balance each task's contribution, ensuring that the model gives more attention to still-underperforming tasks, thus enhancing the overall task learning efficiency.
# Enhancing Efficiency and Cost-Effectiveness: A Groundbreaking Bi-Algorithm MCDM Approach +
## 2 Methods and Experiment
### Stage I: Decision Expert  
DEX, which stands for decision expert, is a technique for modeling multicriteria decisions. The following characteristics set it apart: DEX follows a hierarchical structure; consequently, its multicriteria models comprise features arranged in a hierarchy. This concern is comparable to other MCDA approaches, such as the analytic hierarchy process (AHP)[9; 10; 39; 58]  
The heart of making decisions involves finding the choice that aligns perfectly with the goals of one or more decision makers. At the time, it requires organizing a list of options carefully ranked from the highest level of excellence to the lowest level of feasibility. These options include computer systems, potential job candidates, or in-vestment strategies, as shown in Figure 1. This approach is evident in some areas, which cover everything from personal preferences to complex fields like economics, management, strategic planning, and intricate medical issues. The complexity in situations often comes from the uncertainties surrounding defining and achieving objectives. It becomes more complicated with loosely defined alternatives influenced by various factors impacting decision-making paths.  
Moreover, different decision makers may have conflicting priorities while facing resource limitations and time constraints. Decision makers can access methods and computational tools designed to understand and navigate complexity to overcome these challenges. The tools they have at their disposal include decision support systems, analytical methods of operations, research insights gained from management studies, the foundations of decision theory, and detailed analysis facilitated by decision analysis. A critical aspect of this framework is multi-attribute decision making (MADM), which involves breaking down the judgment challenge into smaller, more manageable subproblems. To accomplish this, decision makers identify the dimensions defining these sub-problems, such as attributes, performance factors and criteria, for evaluating alternatives [1; 3; 6; 15; 23; 24; 58; 64]. A thorough and careful assessment, based on a purpose, combines worth across different options. This method serves as a tool to choose or prioritize the possibilities presented in Figures 2 and 3.
# A Watermark-Based In-Situ Access Control Model for Image Big Data
###### Abstract  
When large images are used for big data analysis, they impose new challenges in protecting image privacy. For example, a geographic image may consist of several sensitive areas or layers. When it is uploaded into servers, the image will be accessed by diverse subjects. Traditional access control methods regulate access privileges to a _single_ image, and their access control strategies are stored in servers, which imposes two shortcomings: (1) fine-grained access control is not guaranteed for areas/layers in a single image that need to maintain secret for different roles; and (2) access control policies that are stored in servers suffers from multiple attacks (e.g., transferring attacks). In this paper, we propose a novel watermark-based access control model in which access control policies are associated with objects being accessed (called an in-situ model). The proposed model integrates access control policies as watermarks within images, without relying on the availability of servers or connecting networks. The access control for images is still maintained even though images are redistributed again to further subjects. Therefore, access control policies can be delivered together with the big data of images. Moreover, we propose a hierarchical key-role-area model for fine-grained encryption, especially for large size images such as geographic maps. The extensive analysis justifies the security and performance of the proposed model.  
access control; watermark; image; big data +
Footnote †: journal: Pattern Recognition  
1
# Automatic Identification of Landslides Based on Deep Learning
## 3 Materials and Methods
### PSPNet  
PSPNet is a semantic segmentation model jointly proposed by the Chinese University of Hong Kong and Shangtang Technology and it won the championship in the 2016 ImageNet Challenge [35]. The original intention of PSPNet was to improve the FCN. The most prominent feature of PSPNet is that it adds a PSP module between the encoder and the decoder, which is also the main difference between it and the FCN.  
The structure of PSPNet is shown in Figure 3; the input layer obtains the feature layer of the input image through CNN, and the feature layer size is changed to 1/5 of the original through. Then, the obtained feature map is input to the pyramid pooling module. First, this module divides the input feature layer into 6 \(\times\) 6, 3 \(\times\) 3, 2 \(\times\) 2 and 1 \(\times\) 1 sized areas; the ave-pooling operation is performed in the divided area to obtain four feature layers of different sizes (corresponding to the green, blue, orange and red outputs in Figure 3, respectively); then, 1 \(\times\) 1 convolution operations are performed on these feature layers; next, the number of channels of the feature layer is changed to one-fourth of the original; and finally, the feature layer is up-sampled by bilinear interpolation. The upsampled feature map and the feature layer obtained by CNN are concatenated, and finally, the final output is obtained through the convolution operation.  
Figure 2: DeepLabv3+ architecture. DCNN stands for deep convolutional neural network, and Atrous Conv stands for atrous convolution. Cyan, orange, pink and green represent the extracted feature layers. The dark blue boxes represent the operations taken on the feature layers.
# A New Association Scheme for Handling Node Mobility in Cluster-Tree Wireless Sensor Networks
## 2 Related Works  
Despite the importance of the association process in the IEEE 802.15.4 networks, most of the research related to this process focuses only on the analysis and the evaluation of its performance [14]. This section briefly describes relevant proposals found in the literature that somehow improves this process.  
The research works reported in this section are organized and classified according to four axes: (i) strategy; (ii) criteria; (iii) topology; and (iv) decision. The first axis is used to classify the state-of-the-art proposals, and is related to how each proposal improves the association process in terms of three aspects: (a) modification to the association process, describing proposals where the message exchange process defined in the standard is modified; (b) coordinator selection, which involves an improvement in the selection of the coordinator point; and (c) connectivity, in which the duration of the association between a node and coordinator is addressed. These approaches are described, and then classified according to the other axes. The second classification axis identifies the criteria used to implement a particular strategy that is defined to improve the handover process. The third axis aims to identify the defined network topology, while the fourth is related to the handover decision, which can be initiated in the MN or the Access Point (AP). Figure 1 illustrates the proposed taxonomy.
## 2 Related Work
### Artificial Fish Swarm Algorithm  
Artificial Fish Swarm Algorithm (AFSA) [12; 35] is an optimisation algorithm, which is based on swarm intelligence. In nature, the fish can discover a more nutritious area by individual search or following after other fish. The area with more fish is generally found to be most nutritious. AFSA has been successfully applied to many engineering design problems with superior performance compared to similar algorithms [36].  
The basic behaviour of artificial fish is defined as follows:  
1. Preying behaviour: This is a basic biological behaviour that tends toward food; generally the Artificial Fish (AF) perceives the concentration of food in water to determine the movement by vision or other sense and then chooses the tendency.
2. Swarming behaviour: The AF will assemble in groups naturally in the moving process, which is considered a living habit, in order to guarantee the existence of the colony and avoid dangers.
3. Follow behaviour: In the moving process of the artificial fish swarm, when a single AF or several ones find food, the neighbourhood partners will trail behind and reach the food quickly.  
In AFSA, the AF can swim fast towards the direction with enough food by sensing and searching for food in the water. Preying behaviour, swarming and following enable AF confined in local extreme locations to move in the direction of a few AF tending to a global extreme value,which results in AF escaping from the local extreme values and thus accelerates the convergence speed of the algorithm [14; 37].  
Due to these inherent features, AFSA can achieve a satisfactory feasible solution quickly, although it lacks the ability to obtain accurate solutions. Furthermore, there is no need for the inspiration of the proposition's prior knowledge, and it is not sensitive to the choice of initial parameters and can therefore be considered robust [38].  
In this paper, after considering the operation of ACO-based routing, we take into account the inherent flaws, and we combine the characteristics of AFSA to propose a hybrid routing algorithm (named FSACO) for WSNs. The main contributions are summarised as follows:  
* A novel AFSA-based initial route discovery algorithm is proposed. The AFSA algorithm achieves a comparatively better global optimisation performance in the early stage and can obtain higher quality routes quickly [12; 14]. We utilise these feasible routes as a heuristic factor for the ACO routing algorithm in our hybrid routing protocol, which can improve the power efficiency of the entire hybrid swarm intelligent routing protocol.
* We propose a hybrid route discovery algorithm, which combines the crowd factor in AFSA and the pseudo-random proportional route select model in ACO-based routing algorithms. The introduction of the crowd factor can effectively prevent the routing algorithm from falling into a local optimum and find the optimal path finally.
* Propose an improved pheromone trail update method based on energy levels and path length to prolong the network lifetime.
## 2 Materials and Methods
### Implementation
#### 2.2.6 Containerized Execution  
All stages depend on the previous step; therefore, if one fails, the pipeline fails. The GitLab Runner pipelines allow us to use a different docker image to run the steps in each job. This functionality is extensively used, as most conversion scripts are based on Python, while the SNOMED CT and the overall test job are based on Java.  
In the convert and test steps, Makefiles [25] are used to abstract from the container and the GitLab Runner pipeline. With the Makefile, a developer can use a single step without having a docker installed. In addition, Makefiles offer a concise way of expressing the different targets, \"install\", \"build\" and \"test\", that fit into the pipeline well. The template for the Makefile is reused across the conversion pipelines.  
The following four different docker containers are used:  
1. GitLab Runner: This is the main container that registers to the GitLab CI/CD pipeline. Besides the MinIO service, it is the only prerequisite to be installed on a machine in order for it to be able to execute the pipelines.
2. Python: Besides the pipeline for SNOMED CT, all pipelines use Python in their conversion. The Python container gives a simple abstraction of a plain Linux with a basic Python installation. Additional packages that are needed are installed using a requirements.txt file through the Python package manager, pip, in the \"install\" target of the Makefile of the individual pipeline.
3. Ubuntu Linux: For the SNOMED CT conversion, Java is used. As additional tools are needed in the conversion, the generic Ubuntu Linux container is used, and the necessary tools are installed using the \"install\" target of the Makefile. The generic Ubuntu Linux container is also used for interaction with MinIO using the mc client tool.  
Apache Jena Fuseki: As part of the pipeline, the triplestore Apache Jena Fuseki is used to test the terminologies in RDF. To be able to make use of the scripts locally in a hospital or BioMedIT node, the triplestore is also offered as a docker container. The same docker container is used in the testing step to ensure correctness. For service providers that do not have a local triplestore of choice, this container can be used to deploy a triplestore. Apache Jena Fuseki is open-source software under the Apache 2.0 license.