esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
The defence of network services against DDoS attacks typically involves the use of a combination of traffic classification, attack source detection and threat response. Defenders seek to block traffic that they identify as illegitimate and to allow traffic that they identify as legitimate. In Figure 1, the route was reconstructed using the IPTBK scheme for rebuilding  
Figure 1: Attack path reconstruction for the IP traceback (IPTBK) problem against IRC bot attacks. attack paths between the victim and attackers (e.g., route V-R\({}_{6}\)-R\({}_{5}\)-A\({}_{2}\)). The possible paths of DDoS attacks were calculated from the relative probability of each reachable route. Problem definition: Typically, the IPTBK problem involves collecting sufficient route information to determine all the possible routes between the attackers and the victim for given constraints on the quantity of routing packets collected and the computational time [4]. To defend against DDoS attacks from botnets, defenders often used ingress filtering [5], probabilistic packet marking (PPM) [6; 7; 8], ICMP trackback messages (itrace) [9] or deterministic packet marking (DPM) [10] approaches for sampling and sorting the passing routing packets. These IP traceback techniques are summarized in Table 1.  
They then use optimised route-searching algorithms to reconstruct the attack paths and trace the attack sources to determine (1) the lower bound of the size of the routing packet to achieve a certain level of traceback accuracy, (2) how to trade-off between the number of attack packet used for traceback vs. the accuracy of the traceback and (3) the minimum size of the routing packet for identifying more than 95% of the attack sources. Existing IPTBK research assumes that complete attack path packets can be collected and traced. However, in practice, practical PPM and DPM mechanisms can only sample and collect small fractions of network routing information (e.g., 3%). Accurately tracing the C&C of a botnet is an information security challenge. The IPTBK problem is an NP-hard problem in which the aim is to attain a set of feasible solutions within polynomial time under certain constraints (e.g., a limitation on the number of nodes within the topology). To reduce the computational load of reconstructing the attack path, selecting an effective traceability algorithm with low complexity is a key
# Towards Accurate Children's Arabic Handwriting Recognition via Deep Learning
## 3 Material and Methods  
### Dataset  
We used the Hijja as our main dataset to train our proposed models. The Hijja is a recent and publicly available dataset of single Arabic letters introduced by [PERSON] et al. [1]. It was written by Arabic-speaking school children aged 7 to 12 years in Riyadh, Saudi Arabia. The dataset contains 108 classes that represent each Arabic letter using four different shapes for positioning at the start, center, and end of a word as well as when standing alone. The dataset contains a total of 47,434 images. The dataset is divided into 29 files, and each file contains images for one Arabic letter and one file for the Hamza. Figure 1 presents a sample of the Hijja dataset.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**References** & **Year** & **Model** & **Dataset** & **Type** & **Size** & **Accuracy** \\ \hline [PERSON] et al. [16] & 2017 & CNN & AHCD & Chars & 16,800 & 94.9\% \\ [PERSON] et al. [17] & 2017 & CNN & AHCD & Chars & 16,800 & 97.6\% \\  & & & AIA9K & Chars & 8737 & 94.8\% \\ [PERSON] et al. [1] & 2021 & CNN & Hijja & Chars & 47,434 & 88\% \\  & & & AHCD & Chars & 16,800 & 97\% \\ [PERSON] et al. [18] & 2020 & CNN & AHCD & Chars & 16,800 & 98.3\% \\ [PERSON] et al. [19] & 2020 & CNN + CG & IFHCDB & Chars & 52,380 & 97.4\% \\ [PERSON] et al. [20] & 2020 & DCNN + SVM & AHCD & Chars & 16,800 & 95.07\% \\ [PERSON] et al. [5] & 2021 & CNN & AHCD & Chars & 16,800 & 97.2\% \\  & & & CNN+Data augmentation & AHCD & Chars & 16,800 & 97.7\% \\ [PERSON] et al. [2] & 2021 & CNN + SVM & Hijja & Chars & 12,776 & 96.3\% \\ [PERSON] et al. [21] & 2021 & CNN & HMBD & Chars & 54,115 & 98.4\% \\  & & & CMATER & Chars & 30,000 & 100\% \\  & & & AIA9K & Chars & 89,740 & 99.0\% \\ [PERSON] et al. [22] & 2021 & CNN & HMBD & Chars & 54,115 & 91.96\% \\  & & HMB-AHCR-DLGA & HMBD & Chars & 54,115 & 92.88\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: A summary of deep learning-based handwritten character recognition models.
## 2 Study Area  
This investigation was conducted in Almeria, southern Spain, which has become the site of the greatest concentration of greenhouses in the world, known as the \"Sea of Plastic\" or the \"Poniente\" region. The study area comprises a rectangle area of about 8000 ha centered on the WGS84 geographic coordinates of 36.7824\({}^{\circ}\)N and 2.6867\({}^{\circ}\)W (Figure 1).  
In the 2013-2014 agricultural year (from July 2013 to June 2014), the intensive agriculture in Almeria was mainly dedicated to tomato (26% of total area), pepper (22%), zucchini (16%), cucumber (11%), aubergine (4.5%) and green bean (3%). The rest of the area was cultivated with other crops, such as melon, watermelon or Chinese cabbage. It is important to note that two crop cycles are usually grown in a greenhouse during an agricultural season in Almeria with the greenhouses' empty time being little. The \"first crop\" in Almeria is often planted between June and September and the second one between December and February. Therefore, the different combinations and possibilities of the cropping calendar in a greenhouse are extremely high. It is worth noting that this work will focus on the identification of crops grown in a greenhouse during the first crop or summer planting corresponding to the 2013-2014 agricultural season.  
Moreover, the types of greenhouse plastic covers in the study area are very variable. The most common materials are polyethylene films (e.g., low density, long life, thermic, with or without additives) and ethylene-vinyl acetate copolymer, also known as EVA. Furthermore, both materials can present different thickness (180 \(\upmu\)m or 200 \(\upmu\)m) and colors (white, yellow or green).
# Technologies for the Production of Fertilizable Mammalian Oocytes
## 2 The Production of Fertilizable Oocytes
### Oocyte Maturation  
At puberty, the fully-grown GV-arrested oocytes enclosed in preovulatory Graafian follicles can resume meiosis in response to LH surge (Figure 2a). The molecular mechanisms regulating meiotic resumption has been recently elucidated. As above reported, FSH stimulates the production and expression of E2 and estrogen receptors (ERs) which, in turn, upregulate the expression levels of an inhibitory signal, natriuretic peptide precursor C (NPPC or CNP), produced in MGC. The binding of NPPC to its receptor, natriuretic peptide receptor 2 (NPR2), in CC stimulates the production of cyclic guanosine monophosphate (cGMP), which diffuses via gap junctions into the oocyte. Since meiotic arrest is ensured by a high intraocyte concentration of cyclic adenosine monophosphate (cAMP), the cGMP-dependent inhibition of phosphodiesterase 3A (PDE3A) activity in the oocyte maintains the GV stage. Conversely, preovulatory LH surge downregulates ER and, as a consequence, the decrease of NPPC and NPR2 levels triggers oocyte meiotic resumption [62]. In addition, CC-dependent secretion of a hyaluron-rich matrix facilitates the uncoupling of the cumulus-oocyte contacts (cumulus expansion). The activation of maturation promoting factor (MPF) and mitogen-activated protein kinase (MAPK) signaling pathways drive the formation of the first meiotic spindle (metaphase I, MI) and the extrusion of the first polar body (PB) with the redundant genetic material. After that, the second meiotic division starts but arrests at MII stage with the formation of the second meiotic spindle (Figure 2b). The release of the cumulus-oocyte complex containing MII oocyte is known as ovulation and requires the rupture of follicular wall [63]. During nuclear maturation, which lasts several hours in mammals (12 h in mice and 24 h in humans), the oocyte undergoes cytoplasmic changes evidenced by the rearrangement of mitochondria and cytoplasmic membranes, migration of cortical granules towards the subplasmalemmal areas, to assure the block of polysppermy through the cortical reaction. Both nuclear and cytoplasmic maturation are necessary for being competent for fertilization and producing a viable embryo. The somatic cells of the ovarian follicle differentiate to corpus luteum, which secrete progesterone (P) to prepare the endometrium for implantation [53]. After fertilization, the second meiosis is completed by extrusion of the second PB [64].
# Retrospective Spectrum-Conversion Method Based on Time-Modulated Van Atta Array
## 3 Van Atta Array Design
### Establishment of the On-Off Model of the Van Atta Array  
Based on the phase conjugation principle, the antenna array forms a Van Atta array and can achieve the direction of incoming waves. When not connected, since the mutually symmetrical antenna units cannot form a path, the phase conjugation principle cannot be satisfied, and a Van Atta array cannot be formed to realize the backscattering of the signal. As shown in Figure 7, the on-off control of the Van Atta array is realized by adding electronically controlled microwave switching elements to the transmission line of equal electrical length connecting two symmetrical antenna elements in the array. The design and analysis of the electronically controlled microwave switching elements are as follows.  
The electronically controlled microwave switching element can realize the conduction and cut-off of the transmission line under the condition of external excitation voltage or excitation current. In this way, the Van Atta array can be electrically controlled to switch between the two states of whether the directional backscattering performance of incident signal can be realized. Since the RCS of the Van Atta array in the oblique incidence of plane waves is mainly composed of backscattering signals, it can be achieved by controlling the microwave switch.  
Then the monostatic radiation field intensity of the Van Atta planar array is equivalent to the weighting of the two-dimensional directivity coefficient of the antenna, as shown in Figure 8. The monostatic radiation field intensity is the largest when the wavefront is parallel to the array plane. With the increase in the angle between the wavefront and the \(X\)-axis and the \(Y\)-axis, the radiation field gradually decreases due to the decrease in the directivity coefficient of the antenna. Similar to the radiation field of the linear array, the monostatic radiation field at different angles is equivalent to the maximum value of the bistatic radiation field observed under the condition of the incident field.  
Figure 7: On–off control Van Atta array principle.  
Figure 9 shows the monostatic radiation field of the Van Atta planar array in the cut-off state: The monostatic radiation field intensity is the largest when the wavefront is parallel to the array plane. As the angle between the wavefront and the \(X\)-axis and \(Y\)-axis increases, the radiation field decreases rapidly. Therefore, monostatic radiation field control in the oblique incidence direction can be realized by on-off setting of the designed Van Atta array.  
Figure 8: Monostatic radiation field intensity of Van Atta line array in on state plane.  
Figure 9: Monostatic radiation field intensity of Van Atta array in cut-off state plane.
## 2 Methodology
### Definition of Time-Domain Shielding Effectiveness  
The swift advancement in high-intensity EMP technology reveals that assessing shielding performance solely based on frequency-domain shielding effectiveness presents notable limitations in gauging the protective capability against potent EMPs. Consequently, the evaluation of a material's or structure's resilience to strong electromagnetic pulses is more accurately conducted through time-domain shielding effectiveness measurements.  
Generally, most electronic devices are sensitive to one or more of the following physical quantities: the maximum value of the electric field \(E_{\text{max}}\), the maximum value of induced effects caused by the time variations in electric flux density \(\dot{E}_{\text{max}}\), and the total energy delivered \(W\). Accordingly, there are three definitions of TDSE [21]:  
\[\text{Peak-value-reduction}\;\text{\emph{SE}}: \tag{4}\]  
Derivative-reduction \(\text{\emph{SE}}:\)  
\[\text{\emph{SE}}_{DR}=20\times\text{lg}\Big{(}\dot{E}_{0-\text{max}}/\dot{E}_{ s-\text{max}}\Big{)} \tag{5}\]  
Energy-density-reduction \(\text{\emph{SE}}:\)  
\[\text{\emph{SE}}_{WR}=10\times\text{lg}(W_{0}(x,y,z)/W_{s}(x,y,z)) \tag{6}\]  
where the subscript \"0\" represents the value of the electric field at the observation point in the absence of the enclosure. When the enclosure is present, we use the subscript \"\(s\)\". In practical testing, the peak field strength emerges as the most straightforward parameter to gauge, leading to its prevalent use in evaluating the TDSE of a shielding material.
## 3 Routing Issues and Challenges in WBSNs
### Topological Partitioning  
The network topology of WBSNs often faces the problem of disconnection or partitioning because of body postural movements and short range transmissions. Different researchers have tried to solve the problem of disconnection and partitioning in different ways. For example, the authors of [20] use Line-of-Sight (LoS) and None-Line-of-Sight (NLoS) communication, while the authors of [21; 22; 23] use store-and-forward routing to solve this problem. Therefore, the proposed routing protocols should take care of the different topological changes.  
Figure 1: Architecture of Wireless Body Sensor Networks.
# Generation of Pure State Photon Triplets in the C-Band
## 4 Conclusions  
In summary, we discuss how to select the optimal pump and crystal parameters to obtain the pure state photon triplets by cascaded second-order SPDC using PPLN and MgO-doped PPLN. We chose 520 nm as the pump source with duration of 0.1 ps. After calculating the Schmidt number of photon pairs generated in each SPDC, we determined the crystal lengths of the two PPLN are 0.2 cm and 9.16 cm, respectively. The lengths of the two PPMgLN are 0.2 cm and 10.74 cm, respectively. According to theoretical calculation, the purity of photon pairs from each SPDC can reach 100%, that is, there is no frequency correlation. We have achieved photon triplets with a spectral purity of 100% in the C-band. We firmly believe that in the future development of quantum networks, our scheme can provide reliable pure-state photon triplet sources for various quantum information processes.  
Conceptualization and methodology, X.-R.S. and Y.-W.H.; validation, Y.-W.H., T.X. and Y.-H.L.; writing--original draft preparation, X.-R.S.; writing--review and editing, T.X., Y.-H.L., X.-F.C.  
This work is supported by the National Key R&D Program of China (2017 YFA0303701, 2018 YFA0306301); National Natural Science Foundation of China (NSFC) (11734011, 11804135); The Foundation for Development of Science and Technology of Shanghai (17)C1400400).  
The authors declare no conflict of interest.
# A Survey on Energy-Aware Security Mechanisms for the Internet of Things
## 4 Energy-Aware Security Mechanisms in IoT
### Approaches to Energy-Aware Security
#### 4.3.3 Network-Based Approaches  
* _Energy-efficient Communication Protocols_. Energy-efficient communication protocols reduce packet sizes during data transmission to cut back on energy consumption. These protocols leverage effective data compression algorithms to diminish the number of communications. In addition, energy-efficient communication protocols harness optimized security mechanisms, such as lightweight encryption algorithms and efficient key management systems, for low-power environments.
* _Network Segmentation_. Network segmentation refers to monitoring and maintaining each segment independently in terms of security assurance. This security simplification facilitates reducing computational and communication activities, which in turn lowers power consumption.
* _Collaborative Security Between Devices_. Collaborative security mechanisms remove unnecessary security checks and response activities via information sharing and the collaborative analysis between devices, thereby conserving energy. Devices can also share security-processing tasks, such as distributed intrusion detection, to eliminate the burden on individual devices and thus remove power consumption.
## 4 Ueap-Our Proposed Protocol  
As shown above, the design of RFID security protocols using the rotation operation does not lead to desired security. Therefore, it seems it is not possible to achieve a secure protocol without the use of cryptographic primitives. There are also lightweight cryptographic primitives such as lightweight block ciphers e.g., Skinny [27], SIMON and SPECK [28] that are suggested to be used to design a secure protocol instead of rotation function, although they are more costly. Using a lightweight block cipher, the disadvantages of the ULRAS authentication protocol are resolved, it is also depicted in Figure 3. We call our improved protocol UEAP, which is the acronym for Ultra-lightweight Encryption based Authentication Protocol:The reader starts the protocol by generating and sending a random time stamp \(T_{R}\) and \(Query\) to the tag.
2. The tag, once it receives the message, verifies whether \(T_{R}\stackrel{{?}}{{>}}T_{t}\). If \(T_{R}>T_{t}\), the tag: * generates a random number \(R_{t}\); * calculates \(M_{1}\) as \(E_{K}(ID\|R_{t}\|T_{R})\); * and sends \(IDS\), \(M_{1}\) and \(R_{t}\) to the reader.
3. Upon reception the message, the reader sends \(IDS\), \(M_{1}\), \(R_{t}\) and \(T_{R}\) to the back-end database.
4. Once the back-end database received the message, verifies whether the received \(IDS\) matches with \(IDS_{new}\) or \(IDS_{old}\). If the back-end database does not find any match, stops the protocol; otherwise, the database: * calculates \(M^{\prime}_{1}=E_{K_{X}}(ID\|R_{t}\|T_{R})\) which \(X\) is _new_ or \(old\). Then it verifies whether \(M^{\prime}_{1}\stackrel{{?}}{{=}}M_{1}\). If \(M^{\prime}_{1}\
eq M_{1}\), the back-end database stops the protocol; otherwise, it does as follows: * authenticates the tag; * generates \(i_{sub}\in\{1,2,3,4\}\) and computes \(M_{2}\) and \(M_{3}\) as below: \(M_{2}=E_{K_{X}\oplus R_{t}}(ID\|T_{R}\|K_{X})\); \(M_{3}=E_{K_{X}\oplus T_{R}}((K_{X}\oplus i_{sub})\|R_{t}\|T_{R})\); * generates sub-key as below: \(subkey=Rot(K_{X}(i_{sub}),K_{X}\oplus R_{t}\oplus T_{R})\); * updates its values as below: \(IDS_{old}=IDS_{new}\); \(K_{old}=K_{new}\); \(IDS_{new}=Rot(IDS\oplus R_{t},K\oplus R_{t}\oplus T_{R})\); \(K_{new}\) is generated by replacing \(K_{i_{sub}}\); * and sends \(M_{2}\) and \(M_{3}\) through the reader to the tag.
5. Upon receipt of the messages, the tag calculates \(M^{\prime}_{2}=E_{K_{X}\oplus R_{t}}(ID\|T_{R}\|K_{X})\) by its local values and then verifies whether \(M^{\prime}_{2}\stackrel{{?}}{{=}}M_{2}\). If \(M^{\prime}_{2}{=}M_{2}\), the tag: * successfully authenticates the back-end server; * extracts \(i_{sub}\) from \(M_{3}\); * generates new sub-key as \(subkey=Rot(K(i_{sub}),K\oplus R_{t}\oplus T_{R})\); * and finally updates its \(IDS,K\) and \(T_{t}\) as below: \(IDS_{new}=Rot(IDS\oplus R_{t},K\oplus R_{t}\oplus T_{R})\); \(K_{new}\) is generated by replacing \(K_{i_{sub}}\).
## 2 Related Works
### Electromagnetic Scattering Center Feature for SAR Target Classification  
Different from the feature extracted by deep network that focuses on the image itself, the scattering center feature of radar targets contains abundant physically relevant descriptions of targets. [PERSON] et al. [33] proposed a feature fusion framework called FEC. It achieved effective and robust performance in SAR target classification tasks by utilizing the attributed scattering centers feature of SAR target echo data. The author transferred the scattering centers feature into a feature vector and improved the fusion effects. We are inspired that the stability and completeness of the scattering center feature could improve the performance of CIL for SAR targets. MGSFA-Net [47] obtained the multiscale global scattering features from the scattering centers of ship targets and then fused them with the deep features by weighted integration for ship target classification. EMI-Net [48] proposed an end-to-end classification network that can realize scattering centers extraction. Nowadays, the attention mechanism becomes popular in target detection and classification because of its less parameters, low module complexity, and global information processing capabilities. PAN [49] proposed a part attention network for fusing electromagnetic characteristics to classify SAR targets, which proved the effectiveness of the attention mechanism in feature fusion.  
Electromagnetic scattering characteristics play a crucial role in the aforementioned methods to enhance recognition performance, demonstrating their completeness and effectiveness. This inspires us to explore how electromagnetic scattering characteristics embedded in raw radar echoes can further improve our CIL performance. Accordingly, in this paper, we employ cross-attention to integrate CNN features with scattering center features, resulting in a significant improvement in the performance of our CIL method for SAR targets.
## 3 Experimental Setup  
### System Setup  
To develop and verify the proposed AI-based inspection system, a hardware and software environment was required to be setup to support the processing of a large number of data sets for training and inference [26]. The host computer was equipped with an 8-core Intel i7-7700K CPU and NVIDIA-GTX 1660 Ti GPU core graphics card. The host computer was responsible for the training of the neural networks. The host computer connected the external expansion device, NVIDIA(r) Jetson(tm) TX2 embedded development board, to perform the inference task. The host computer was responsible for the overall data processing and training tasks. Once a model was trained and verified in the host computer, it was compiled and ported to the Jetson(tm) TX2 embedded development board for inference.  
The main operation software was Ubuntu 16.04 version, together with additional installations of NumPy software for C++ and Python programming language interface and the open source software DIGIS (Deep Learning GPU Training System) under the Caffe Deep Learning Framework to provide multi-class training and testing. In the iterations of training, the optimal one-time iteration convergence curve in the trained model was selected as the data for the convergence model. The stored model data were converted to the snapshot image file format by the DIGIS software.  
The snapshot model for deployment was then ported to the TX2 to perform the inference task through the Tensor RT software. The deep learning model performed the object identification module for images in the LMDB format, and the self-labeled defect features model was trained to identify the object localization window and bounding box. The input data library data set used the previous layer, the convolutions layer, the max pooling layer, the normalized exponent function SoftMax layer, a 22-layer neural network, and the optimizer calculation model of a stacked deep neural network module. The custom DNN model recognized eight styles and successfully marked the object feature map segmentation.