esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Modeling and Analysis  
Message dissemination speed and PDR are the metrics used widely to validate the efficiency and reliability of vehicular networks. In the scenario of straight road, the selection of relay node in each hop is independent. However, from the description in the above section, the partition range \(R_{\rm P}\) for the selection of Relay\({}_{\rm B}\) is dependent on the location of Relay\({}_{\rm i}\). It means that the first hop (phase) in the whole procedure decides the latter hop (phase). Thus, we analyze the two metrics in the two hops for our proposed general relay-node selection on intersection. Please note that the two metrics of message dissemination speed and PDR are related to the procedure of the relay-node selection, not the message broadcast.  
In the following analysis and the simulation in Section 5, the protocol in [13] is adopted to proceed the message dissemination in one-hop. Message dissemination in this protocol has four phases: Mini-distribute inter-frame space (mini-DIFS) for channel access, exponent-based partition, CTB contention phase to resolve the problem of multiple nodes in the final segment and data transmission. Thus, one-hop delay \(T_{d}\) consists of initial latency \(T_{\rm init}\) in mini-DIFS, partition latency \(T_{\rm part}\), contention latency \(T_{\rm con}\) and data transmission latency \(T_{\rm data}\). \(T_{\rm d}\) is given as  
\[T_{\rm d}=T_{\rm init}+T_{\rm part}+T_{\rm con}+T_{\rm data} \tag{5}\]  
Certainly, other protocol also can be adopted in the message dissemination, and the analysis is similar. In the CTB-contention phase, the exponential back-off timer is applied to alleviate the collision.
# Image Representation Using Stacked Colour Histogram
###### Abstract  
Image representation plays a vital role in the realisation of Content-Based Image Retrieval (CBIR) system. The representation is performed because pixel-by-pixel matching for image retrieval is impracticable as a result of the rigid nature of such an approach. In CBIR therefore, colour, shape and texture and other visual features are used to represent images for effective retrieval task. Among these visual features, the colour and texture are pretty remarkable in defining the content of the image. However, combining these features does not necessarily guarantee better retrieval accuracy due to image transformations such rotation, scaling, and translation that an image would have gone through. More so, concerns about feature vector representation taking ample memory space affect the running time of the retrieval task. To address these problems, we propose a new colour scheme called Stack Colour Histogram (SCH) which inherently extracts colour and neighbourhood information into a descriptor for indexing images. SCH performs recurrent mean filtering of the image to be indexed. The recurrent blurring in this proposed method works by repeatedly filtering (transforming) the image. The output of a transformation serves as the input for the next transformation, and in each case a histogram is generated. The histograms are summed up bin-by-bin and the resulted vector used to index the image. The image blurring process uses pixel's neighbourhood information, making the proposed SCH exhibit the inherent textural information of the image that has been indexed. The SCH was extensively tested on the Coil100, Outext, Batik and Corel10K datasets. The Coil100, Outext, and Batik datasets are generally used to assess image texture descriptors, while Corel10K is used for heterogeneous descriptors. The experimental results show that our proposed descriptor significantly improves retrieval and classification rate when compared with (CMTH, MTH, TCM, CTM and NRFUCTM) which are the start-of-the-art descriptors for images with textural features.  
colour histogram; image descriptor; texton; features extraction; image representation; Content-Based Image Retrieval +
Footnote †: journal: Applied Soft Matter  
1
Footnote 1: [[https://www.mdpi.com/journal/algorithms](https://www.mdpi.com/journal/algorithms)]([https://www.mdpi.com/journal/algorithms](https://www.mdpi.com/journal/algorithms))
# A Deep Learning Approach for Automatic Hate Speech Detection in the Saudi Twittersphere
## 3 Dataset Construction  
In this work, we built a new dataset that covers different types of hate speech, e.g., racist, religious, and ideological hate speech. We used the standard Twitter search/streaming API with tweepy [27] python library to collect our data. The data was collected in span of 6 months, from March 2018 to August 2018, using a keyword-based and thread-based search. In the keyword-based approach, we included in the search query a total of 164 impartial unique terms (excluding the various morphological variations of the same term) that refer to groups or to people belonging to groups that are likely to be targeted by hate speech. For example, we used terms that refer to people who belong to different tribes (such as \"Otaibi\", \",\",\") and different regions (\"Hijaz\", \",\") to collect tweets related to tribal and regional hate speech, respectively. For the thread-based search, we included in the search query different hashtags that discusses controversial topics that are considered to be strong indicators of hate speech. We monitored twitter trends during the period of the data collection, and we ended up with a total of 10 hashtags used for data retrieving. Examples of the used terms and hashtags are illustrated in Table 1. Since hateful tweets are generally less common than natural tweets, we further boosted our dataset and improved the representation of the hate class by using the top-scored terms in the lexicon of religious hate terms released by [PERSON] et al. [12]. For all queries, we collected only original and Arabic tweets, excluding retweets and non-Arabic tweets from the search. In total, we retrieved 54 million tweets, from which we sampled 10,000 tweets for annotation. To obtain the annotations for the dataset, Figure Eight (now Appen) [28] crowdsourcing platform was utilized to recruit both crowed and internal workers (volunteers and freelancers). Before submitting the task to the annotators, we generated an annotation guideline for hate speech annotation with the help of experts in interreligious dialogue, Islamic jurisprudence, and media studies. The guideline is available online on GitHub [29]. The entire annotation process was completed in a course of 3 weeks, starting from 1 to 23 September 2019. The dataset was submitted to the annotators in different batches. The first batch (1000 tweets) was annotated by crowed workers. The second batch (4000 tweets) was annotated by 15 different Saudi annotators. The third and final batch (5000 tweets) was annotated by three freelancers who are familiar with the Saudi dialect.
## 3 Methods and Modeling
### Modeling
In such contexts, once the initial setup is complete, the marginal cost of replicating and delivering digital services is negligible. By focusing on service costs, we can isolate the impact of service differentiation on supply chain decisions and profitability, providing a clearer understanding of the strategic value of IT investments in enhancing service quality. To simplify calculations and better highlight the impact of service differentiation, it is assumed that Merchant 2's service level equals the industry average. Consumers perceive extra service utility only from Merchant 1's service differentiation \(\Delta s\). This study employs game theory grounded in the RBV to model the strategic decision-making processes of entities within the CBEC supply chain. Service differentiation (\(\Delta s\)) is operationalized as a VRIN resource. Accordingly, consumer utility from purchasing from the respective merchants can be formulated as follows (the derivation process is provided in Appendix A.1). \[\left\{ \begin{array}{l}U_{1}=\delta_{1}\lambda-\alpha p_{1}+\beta\Delta s\\ U_{2}=\delta_{2}\lambda-\alpha p_{2}\end{array} \right. \tag{1}\]  
The demand functions for Merchant 1 and Merchant 2 can be, respectively, articulated as follows. \[\left\{ \begin{array}{l}D_{1}=1-\frac{\alpha\Delta p-\beta\Delta s}{\Delta \delta}\\ D_{2}=\frac{\alpha(\delta_{2}p_{1}-\delta_{1}p_{2})-\delta_{2}\Delta s}{\delta _{2}\Delta\delta}\end{array} \right. \tag{2}\]  
The profits of the individual merchants and the overall cross-border supply chain can be, respectively, delineated as follows. \[\left\{ \begin{array}{l}\pi_{1}=p_{1}D_{1}-C_{1}=p_{1}-\frac{\alpha p_{1} \Delta p-p_{1}\beta\Delta s}{\Delta\delta}-\frac{\eta}{2}(\Delta s)^{2}\\ \pi_{2}=p_{2}D_{2}-C_{2}=\frac{\alpha p_{2}(\delta_{2}p_{1}-\delta_{1}p_{2})- \delta_{2}p_{2}\beta\Delta s}{\delta_{2}\Delta\delta}\\ \pi=\pi_{1}+\pi_{2}=\frac{p_{1}(\Delta\delta-\alpha\Delta p+\beta\Delta s)}{ \Delta\delta}-\frac{\eta}{2}(\Delta s)^{2}+\frac{\alpha p_{2}(\delta_{2}p_{1}- \delta_{1}p_{2})-\delta_{2}p_{2}\beta\Delta s}{\delta_{2}\Delta\delta}\end{array} \right. \tag{3}\]  
The parameters and equations in this model are designed to quantify the trade-offs faced by CBEC traders when adopting advanced IT. Specifically, \(\alpha\) (price sensitivity) and \(\beta\) (service sensitivity) reflect how consumer behavior directly influences pricing and service strategies. By modeling utility functions and demand scenarios, we capture the equilibrium where traders balance IT-induced service differentiation (\(\Delta s\)) against cost increments (\(\eta\)). In the context of price and service competition, the scenarios of merchants operating under CD and DD will be analyzed separately in the following sections.
# Reversible Data Hiding in Encryppted Images with Extended Parametric Binary Tree Labeling
## 3 Proposed EPBTL-RDHEI
### Image Encryption and Pixel Grouping
#### 3.1.2 Image Encryption  
The stream cypher is adopted as the image encryption scheme. A pseudo-random matrix \(R\) is generated with the encryption key \(k_{e}\). Then, the bitwise exclusive-or (XOR) operation is applied as  
\[x_{e}^{k}(i,j)=x^{k}(i,j)\oplus r^{k}(i,j),k=1,2,...,8, \tag{4}\]  
Figure 5: Median-edge detector.  
Figure 6: Example of the prediction error.  
where \(\oplus\) is the XOR operation, the notations \(i\) and \(j\) are the row and column indices of the image, and \(k\) denotes the bit index of a pixel. The resulting pixel array is the encrypted image \(I_{e}\) as shown in Figure 7.
# Learning and Compressing: Low-Rank Matrix Factorization for Deep Neural Network Compression
## 3 Algorithm
### Problem Formulation  
Specifically, inspired by the learning-compression algorithm [11; 15; 38] that formulates neural net compression in a general way via constrained optimization, we first formulate the problem of low-rank compression based on ranks auto-selection as a constrained optimization and then solve it via joint optimization, and finally show how this fits into the DNN compression. Here, we exhibit and extend this approach to joint-optimization functions and iterative enumeration to solve the problem of compressing deep networks using low-rank MF. It assumed that the weight matrix \(\mathbf{W}\) consists of the set \(\{\ \mathbf{W}_{1},\mathbf{W}_{2},...,\mathbf{W}_{k}\}\). \(\mathbf{W}_{k}\) has the dimension of \(m_{k}\times n_{k}\) and the dimension satisfies \(m_{k+1}=n_{k}\) because the output from the previous layer is used as input of the current layer. The objective function is as follows:  
\[\min_{\mathbf{W},\mathbf{\Theta},r}L(\mathbf{W})+\lambda C(r),\text{s.t.} \mathbf{W}_{k}=\mathbf{\Theta}_{k},\text{rank}(\mathbf{\Theta}_{k})=r_{k}\leq R _{k},k=1,...,K, \tag{5}\]  
where \(L(\mathbf{W})\) is defined as classification loss, which can be solved by stochastic gradient descent, and \(C(r)\) is defined as the compression cost function, \(\lambda\) is a prescribed positive parameter which naturally trades off the classification loss \(L(\mathbf{W})\) with the compression cost function \(C(r)\) and determines the distribution of ranks over the deep net layers, \(\mathbf{\Theta_{k}}\) is the low-rank approximation matrix of weight matrix \(\mathbf{W_{k}}\) and the rank of \(\mathbf{\Theta_{k}}\) is \(r_{k}\). Since memory and computation satisfy a linear relationship with rank \(r_{k}\) in \(k\)-th layer, the compression cost function \(C(r)\) uses the rank \(r\) as one of the variables. As mentioned above, function \(C\) can be defined by the ranks in each layer of the following form:  
\[C(r)=C_{1}(r_{1})+C_{2}(r_{2})+...+C_{k}(r_{k}), \tag{6}\]  
where \(C_{k}(r_{k})=(m_{k}+n_{k})\times r_{k}\). From a modeling point of view, \(C(r)\) can represent several related compression costs in the context of DNN compression by appropriate choices of the coefficients rank \(r\).
# The Optimization of Covert Communication in Asymmetric Jammer - Assist Systems
## 4 Analysis of the Transmission Rate  
In this section, we will analyze the transmission rate of covert communication with mutual information. Specifically, we consider two cases: (1) Bob can perfectly eliminate the influence of interference; and (2) Bob can partially eliminate the influence of interference.
## 2 System Architecture
The definition of collected network information is as follows: the set of network nodes \(n\) is \(N\), the set of links \(l\) is \(L\), the capacity is \(C\), and the network topology environment is \(G=(N,L,C)\). The data flow is defined as shown in Equation (7). Figure 1:0where \(d_{i}\) represents the starting node of the data flow, \(h_{i}\) represents the destination node of the data flow,\(p_{i}\) represents the start time of data transmission, \(q_{i}\) represents the end time of the data flow, and \(td_{i}\) represents the traffic demand. \(L\) is the set of links (\(k\in L\)), \(P_{ij}\) denotes the path from node \(i\) to \(j\), and the average delay/throughput is calculated over all node pairs \((i,j)\). The calculation equations for average end-to-end time and average throughput are shown in Equations (8) and (9). Where \(i\) and \(j\) denote source and destination node indices (\(i,j\in[1,|N|]\)), and \(k\) represents the link index within path \(P_{ij}\). The terms \(d_{k}\) and \(\tau_{k}\) denote the delay and throughput of the link \(k\), respectively. Figure 1:1  
Figure 1:2
# The Role of PET and SPECT Imaging in Prostate Cancer Targeted Alpha Therapy: When and How?
## 4 \({}^{223}\)Radium-dichloride (Xofigo)
### Imaging for Patients' Selection and Follow-Up after \({}^{223}\)RaCl2
#### 4.1.3 \({}^{18}\)F-choline PET  
\({}^{18}\)F-choline PET may be useful in patients' selection for \({}^{223}\)Ra-Cl2 treatment, especially in terms of prognosis, and there is evidence in the literature that underline this potential role. In 2018, [PERSON] and collaborators demonstrated that parameters such as metabolically active bone tumor volume (MBTV) and total bone lesion activity (TLA) at baseline may predict hematological toxicity after treatment [56]. The prognostic role of TLA in \({}^{18}\)F-choline PET was highlighted in a study conducted by [PERSON] et al.: evaluating eleven patients with mCRPC treated with \({}^{223}\)Ra-Cl2, the reduction in TLA between pretreatment and post-treatment scan was correlated with a longer survival of patients [57]. Another important study by [PERSON] et al. highlighted the potential role of interim and end-treatment\({}^{18}\)F-choline PET, in which an important adverse factor for overall survival is represented by progression disease at interim \({}^{18}\)F-choline PET [58].
## 2 Materials and Methods
### Analysis of Anthocyanins by UHPLC-DAD-MS/MS  
Aiming at characterizing the phenolic profile of the SD5 sourdough, methanolic extracts were obtained before (SD5-T0) and after 24 h of fermentation (SD5-T24) at 30 \({}^{\circ}\)C. SD0 sourdough, not containing GPP, was analyzed as control.  
A UHPLC Ultimate 3000 RS Dionex interfaced by H-ESI II probe with a LTQ Velos pro linear ion trap mass spectrometer (Thermo Fisher Scientific, Waltham, MA, USA) was used for the analysis of anthocyanins compounds. The UHPLC system was composed by qua-ternary pump, autosampler, column compartment, and detector. The analytical separation was achieved as previously reported [20] with some modification. A Hypersil GOLD aQ C18 column was used (100 mm of length, 2.1 mm internal diameter and 1.9 \(\upmu\)m of particle size), held at 30 \({}^{\circ}\)C and at a constant flow of 0.3 mL min\({}^{-1}\) with water-formic acid (90:10 \(v/v\)) (solvent A) and acetonitrile-formic acid (99.9:0.1 \(v/v\)) (solvent B). The gradient program of solvent A was as follows: 0-20 min from 98% to 30%; 20-24 min isocratic at 30%. Then, equilibration was performed at the initial conditions for 9 min. The PDA detector was set to scan from 220 to 600 nm of wavelength managed by a 3D field.  
The MS parameter conditions were as follows: capillary temperature 320 \({}^{\circ}\)C; source heater temperature 280 \({}^{\circ}\)C; nebulizer gas N2; sheath gas flow 33 psi; auxiliary gas flow 5 arbitrary units; S-Lens RF Level 60%. Data were acquired in positive ionization mode. Samples were analyzed with two methods: a full scan method from 100 to 1000 \(m/z\) and a data-dependent experiment to collect MS2 data. The data-dependent settings were a full scan from 200 to 1000 for positive ionization, activation level 500 counts, isolation width 2 Da, default charge state 2, and CID energy 35.  
The samples were filtered using syringe filters (LLG Labware, Meckenheim, Germany) in RC by 0.22 \(\upmu\)m before injection into the equipment. All data were acquired and processed using Xcalibur v.2 (Thermo Fisher Scientific, Waltham, MA, USA). The injection volume was 5 \(\upmu\)L. Tentative identification of compounds was performed using mass spectra (MS2), \(\uplambda\)max, and retention time according to the literature [21; 22]. Quantitative analysis was performed according to the external standard method based on calibration curves obtained by injecting different concentrations of standard solutions (R2 = 0.9972). Specifically, the standard used was malvidin-3-O-glucose, which was purchased from phyproof(PhytoLab, Dutendorfer, Germany).
## 4 Proposed Techniques
### Dummy Adaptive Distribution  
In order to increase the delivery ratio of delivered real packets to total real packets, DAD is introduced and it works as follows. All nodes in the network are categorized into dummy nodes and real nodes. In the beginning, all nodes will be considered dummy nodes using a transmitting constant rate as presented in DUD. Dummy nodes do not generate dummy packets in every interval. They only generate and transmit dummy packets if the thrown random number between 0 and 1 is less than the selected threshold (transmission rate). However, if a node detects an event or needs to forward packets of real events, it becomes a real node. A real node will increase the rate of its real traffic by a specific value (Equation (6)). Moreover, it decreases the rate of its fake traffic by the same specific value (Equation (7)) after all real packets are transmitted starting from the interval at which an event occurs until the end of the network's lifetime. The real and fake rates will be increased or decreased based on the selected transmission rate. Real and fake rates of the real nodes are given by the following equations:  
\[R_{real}=R_{const}+\frac{N_{real}}{I_{total}} \tag{6}\]  
\[R_{fake}=R_{const}-\frac{N_{real}}{I_{total}} \tag{7}\]  
\[R_{const}=\frac{R_{real}+R_{fake}}{2} \tag{8}\]  
where \(R_{real}\) and \(R_{fake}\) are the real and fake rates respectively. \(R_{const}\) is the constant rate of the network, \(N_{real}\) is the number of real packets and \(I_{total}\) is the number of total intervals.  
Since the average of real traffic and fake traffic is equal to the original constant rate, the adversary will not notice any change in the rate of the network. Also, \(DAD\) needs to satisfy the equation  
\[N_{real}\leq\ R_{fake}*\ I_{total} \tag{9}\]  
in order to perform well; otherwise the average number of the dummy packets will be less than the real packets, meaning that the adversary easily detects the real event. DAD keeps the same level of anonymity as DUD but it will increase the delivery ratio and reduce the delay. However, DAD is still unable to guarantee the arrival of real packets. To clarify DAD, an example is shown in Figure 1. The rate of green nodes will only be modified, because they have real event to report, i.e., forward a packet onward to the sink node. The blue nodes will keep their transmission rate without any change, because they only generate dummy packets.  
Figure 1: An example of the DAD Technique.
# A Flexible FPGA-Based Stochastic Decoder for 5G LDPC Codes
## 7 Conclusions  
In this paper, we constructed a BGM of QC LDPC code meeting the 5G NR standard. We presented stochastic decoding to LDPC codes as a hardware-efficient alternative to SPA- and MS-based LDPC decoders. From simulations, we observed that with the inclusion of features like scaling and edge memory, stochastic decoding performs almost at the level of SPA-based LDPC decoder in terms of BER performance. Compared to the reported decoders, the proposed architecture exhibits area efficiency improvements of 26% compared to the CMS-based decoder, 62% compared to the NMS-based decoder, and 77.46% compared to the MS-based decoder, while delivering energy efficiency improvements of 52% compared to the NMS-based decoder and by 21.6% compared to the MS-based decoder.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Design** & **Proposed** & [32] & [33] & [34] & [35] \\ \hline Standard & 5G-NR & 5G-NR & 802.16e & 802.15.3c & 802.11n \\ \hline Code length & 3808 & 3808 & 2304 & 672 & 1944 \\ \hline Base code rate & 1/3 & 1/3 & 1/2 & 1/2 & 1/2 \\ \hline Decoding algorithm & SD & CMS & NMS & NMS & MS \\ \hline Scheduling & Column-layered & Row-layered & Row-layered & Row-layered & Row-layered \\ \hline Extrinsic message width & 1-bit & 4-bit & 6-bit & 4-bit & 4-bit \\ \hline Sub-matrix size & 56 & 56 & 96 & 21 & 81 \\ \hline DCs or ltrs & 620 & 10 & 10 & 5 & 10 \\ \hline Area (mm\({}^{2}\)) & 1.10 & 1.49 & 2.9 & 2.25 & 4.88 \\ \hline Throughput (Gbps) & 1.12 & 3.04 & 2.20 & 5.28 & 4.5 \\ \hline Power (mW) & 410 & 259 & 870 & 182 & 523 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparative results.  
Data Availability Statement: The data used for the experiments reported in this paper are available upon request from the authors via email.  
The authors declare no conflict of interest.