esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
A new method was developed for the simultaneous determination of vascular endothelial growth factor (VEGF-A) and fibroblast growth factor-2 (FGF-2) in blood serum, using biosensors with array Surface Plasmon Resonance imaging (SPRi) detection. It can be applied as a single method for simultaneous VEGF-A and FGF-2 determination or as two separate methods for testing only one selected protein in each case. Validation was carried out for each method. Limit of detection (LOD) and limit of quantification (LQQ) values were determined and were found not to differ significantly from the parameters obtained in comparisons with commercial enzyme-linked immunosorbent assay (ELISA) tests. Tests were carried out to check the robustness of the method. The results indicate a lack of robustness of the analytical method to elevated temperature and pH values other than those recommended by the manufacturers of the reagents (recommended pH = 7.40). The values of recoveries were determined and confirmed the reliability of the results obtained with the use of the newly developed method. The selectivity studies showed no negative influence of other proteins present in the matrix of the tested samples on the results of the VEGF-A and FGF-2 concentration measurements. The developed method is also characterized by high reproducibility of the results obtained and agreement with the VEGF-A and FGF-2 concentration values obtained with commercial ELISA tests. The proposed method offers fast, reproducible, and accurate simultaneous quantification of VEGF-A and FGF-2 in human body fluids. Only 4 mL of test sample are required for simultaneous analysis. The total time for simultaneous analysis of both biomarkers does not exceed 20 min. The developed analytical method is superior to ELISA in terms of analysis time and sample volume for analysis, and it offers lower LOD and LQQ values and allows for the simultaneous analysis of two biomarkers. There is also no need to collect a large number of samples. Standard ELISA usually have 96 reaction wells. The proposed biosensor can be used to analyse only one sample, without the need to waste reagents on unused reaction sites. In addition, it is possible to regenerate the biosensor and reuse it.  
SPRi biosensor; VEGF-A; FGF-2; analytical procedure validation 202212212699. [[https://doi.org/10.3390/app122412699](https://doi.org/10.3390/app122412699)]([https://doi.org/10.3390/app122412699](https://doi.org/10.3390/app122412699))
## 3 The Deployment Models
### Inspection Using Cryptographic Functions
#### 3.2.1 TLS Inspection with a Private Key  
In certain configurations of TLS, such as TLS 1.2 with Rivest-Shamir-Adleman (RSA)-based ciphersuite, when the server shares the certificate private key with the middlebox as shown in Figure 2, TLS-encrypted traffic can be decrypted [27]. Similarly, Wireshark, a well-known network protocol analyzer, has a feature to decrypt TLS-encrypted traffic in the aforementioned configurations. Therefore, out-of-band (passive) TLS inspection is possible using a private key and appropriate configurations. However, this approach cannot  
Figure 1: TLS interception.  
be applied in other ciphersuites in TLS 1.2, such as Diffie-Hellman-based ciphersuites. In addition, TLS 1.3 does not support ciphers without forward secrecy, such as RSA-based ciphersuites. Additionally, [PERSON] and [PERSON] [27] discussed static Diffie-Hellman key sharing use cases and issues in detail.
## 3 Toward the Generation of Isolated Attosecond Hard X-ray Pulse
### An Isolated Attosecond Hard X-ray Pulse Generation  
Ultimately, the electron bunch shown in Figure 10 is sent to an undulator, where the electrons residing in the central peak part radiate most strongly (enhanced SASE), because of its high current. The electrons outside the central peak part also contribute to radiation, but the magnitude is relatively small due to the weak current. To calculate the radiation from this electron bunch, we use the three-dimensional time-dependent undulator radiation code, GENESIS [43]. This whole calculation has been done at 0.1 nm. In a simulation, one electron bunch is divided into many slices. The radiations from each electron are averaged over many slices. What is presented below is the result of this average.  
Figure 11a shows the logarithmic plot of x-ray output power from the 6 kA electron bunch with a strong current peak and two very weak side peaks (Figure 10f) as a function of distance along an undulator. The saturation occurs around z = 34 meters, and the radiation power at z = 34 meters' distance is around 58 GW. The saturation position is the point where the exponential growth of the radiation power stops. After this point, the radiation power produced by the main current peak does not grow, because the slippage lengthens the pulse width. As we note, the radiation power produced by the main peak is two orders of magnitude greater than the power produced by side peaks. Figure 11b shows the radiation pulse at z = 34 meters for the 6 kA electron bunch. The radiation pulse width is around \(\sim\)146 attoseconds with a total power of 58 GW.  
We have also calculated the saturation point for the cases of the 3 kA (Figure 8) and 4 kA (Figure 9) electron bunch. We find that the saturation length is larger for the low current cases. For the 3 kA electron bunch, the saturation point is around 73 meters
## 6 Evaluation and Experimental Results
### Energy Model
#### 6.4.3 Evaluation of the Computational Cost of the Proposed HIP DEX Compression Model  
The computational cost is calculated using the following equation where \(T_{cpu}\) is the task execution time in CPU, the current is equal to 20.5 mA and the voltage core is 1.2 V [38]:  
\[\text{HipEnergyCompt}=T_{cpu}\times 20.5\;\text{mA}\times 1.2\;\text{V}. \tag{4}\]  
We can assume that solutions that do not adopt any compression technique as in [PERSON] et al.'s and D-HIP's solutions present the most expensive energy consumption in comparison with those that implement compression. Besides, we prove that HIP DEX is a more efficient authentication protocol than HIP DEX in terms of computational cost due to the use of Elliptic Curve Cryptography (ECDH). Our solution minimizes the number of signaling messages in comparison with [PERSON] et al., C-HIP, D-HIP, Slimfit, and CD-HIP that is the reason for the minimization of the computational overhead and as a consequence the minimization of the overall energy consumption.
# Crop Disease Diagnosis with Deep Learning-Based Image Captioning and Object Detection
###### Abstract  
The number of people participating in urban farming and its market size have been increasing recently. However, the technologies that assist the novice farmers are still limited. There are several previously researched deep learning-based crop disease diagnosis solutions. However, these techniques only focus on CNN-based disease detection and do not explain the characteristics of disease symptoms based on severity. In order to prevent the spread of diseases in crops, it is important to identify the characteristics of these disease symptoms in advance and cope with them as soon as possible. Therefore, we propose an improved crop disease diagnosis solution which can give practical help to novice farmers. The proposed solution consists of two representative deep learning-based methods: Image Captioning and Object Detection. The Image Captioning model describes prominent symptoms of the disease, according to severity in detail, by generating diagnostic sentences which are grammatically correct and semantically comprehensible, along with presenting the accurate name of it. Meanwhile, the Object Detection model detects the infected area to help farmers recognize which part is damaged and assume them of the accuracy of the diagnosis sentence generated by the Image Captioning model. The Image Captioning model in the proposed solution employs the InceptionV3 model as an encoder and the Transformer model as a decoder, while the Object Detection model of the proposed solution employs the YOLOv5 model. The average BLEU score of the Image Captioning model is 64.96%, which can be considered to have high performance of sentence generation and, meanwhile, the mAP50 for the Object Detection model is 0.382, which requires further improvement. Those results indicate that the proposed solution allows the precise and elaborate information of the crop diseases, thereby increasing the overall reliability of the diagnosis.  
crop diseases diagnosis; farm-tech; deep learning; Inceptionv3; transformer; image captioning; YOLOv5; object detection +
Footnote †: journal: Medical Image Computing
## 6 Experiments
### Settings  
In order to evaluate the efficiency of our solutions, we used TelosB motes. These motes have an 8-MHz microcontroller with 10 Kb of RAM, 48 Kb of ROM and a CC2420 radio using the IEEE 802.15.4 standard. Due to the limitation of their computing resources, these motes are used as a basis for comparison between the different protocols and not for obtaining the best results in terms of performance. Our implementation of ECC is based on the TinyECC library [33], on ECIES with a key of \(160\) bits and on an optimized implementation of symmetric encryption AES [35] in CTR mode with a key of \(128\) bits. In order to explain why we choose AES with CTR mode and the 128-bit key \(k\), we recall the mechanism of this scheme: let us consider a message \(m\) composed of \(k\) blocks \(m_{1}||m_{2}||\ldots||m_{p}\) and an initial counter value \(IV\) randomly chosen. The cipher of the block \(m_{i}\) is \(c_{i}=\{IV+i\}_{k}\oplus m_{i}\), where \(\oplus\) denotes the bitwise exclusive-or operator. If you know \(IV\) and the key \(k\), then you can easily recover from \(c_{i}\) the message \(m_{i}=\{IV+i\}_{k}\oplus c_{i}\). When the size of the last block \(m_{p}\) is smaller than 128 bits, it is usual to pad it with \(0\) up to 128 bits in order to have both operands with the same length to perform the bitwise exclusive-or. In this case, the size of the transmitted encrypted packets is always a multiple of 128 bits. However, in CTR mode, we can just cut the \(\{IV+p\}_{k}\) message to the size of \(m_{p}\). Hence, we can transmit an encrypted message that has exactly the same size as the original message and, therefore, avoid any overhead in terms of transmission time. For example, in protocol KR, if the list of revoked nodes and the nonce is \(l\)-bits long, that is smaller than 128 bits (AES block size), it is sufficient to only transmit \(l\) bits.  
During the experiments, we considered topologies without intermediate nodes, since these nodes would only forward packets, in a multihop manner, without doing any modification on the packet. The cost of these communications is therefore negligible compared to the encryption and decryption costs. Moreover, this cost is the same for all protocols; only the load of the network can change between unicast and broadcast protocol. Hence, for each situation, we only consider a minimal topology containing only the nodes involved in the cryptographic operations. Moreover, malicious nodes were only included in the verification model automatically done by Scyther. No malicious nodes were included in the experiments. When detecting (IDS), a malicious or a compromised node, the sink is informed. Then, the sink can launch the revocation process in executing the protocol KR. Afterwards, the sink should launch the renewing key process in executing one of protocols RAK for the nodes in the neighborhood of the malicious node and RNK for all of the concerned nodes of the network.
## 4 Discussion
### Evidence for a Pupping Location  
Two lines of evidence, documentation of near-term gravid and recently postpartum females and frequent catches of YOY, point to the area around Bimini being a pupping location for tiger sharks. Ultrasonography results support pupping occurring near Bimini during the late spring and summer. Given the two-year reproductive cycle and estimated 15 month gestation period of tiger sharks [47], it was expected that approximately 50% of the mature females would be at some stage of pregnancy at any one point in time. For example, in the waters of Tiger Beach, The Bahamas, [PERSON] et al. [27] found that 23 of the 40 (57.5%) mature female tiger sharks were pregnant and also estimated a late spring or summer pumping season. In our study, eight of the nine mature females (88%) were pregnant or recently postpartum (within days or weeks). However, mechanical failures with the ultrasound prevented a more robust assessment of all mature female sharks, and future research should focus on this to incorporate seasonality of reproduction across multiple locations in the region.  
Our catch data support previous research conducted near Bimini, in which the tiger shark mean size of capture was correlated with the month of the year, and YOY tiger sharks caught during the presumed pupping season of July to September [31]. The size composition of tiger sharks caught near Bimini was consistent from 1984 to 2019 [32], indicating this area has potentially been continually used as a pupping location over multiple decades. Higher relative abundance is one of the criteria for distinguishing important pupping and nursery areas [13], but comparing between different surveys can be a challenge due to differences in sampling methods across research groups. However, comparisons of studies can at least provide some context within the region. For example, a fishery-independent longline survey conducted in the eastern Bahamas had a mean fork length for tiger sharks of 248.65 cm, compared to 172.8 cm in our study, potentially suggesting a higher relative frequency of smaller tiger sharks near Bimini. The shallow water bank habitat sampled in our study is a commonly used habitat for juvenile tiger sharks [48], likely contributing to the smaller mean size.
## 4 Fairness-Aware Optimal Transmission Scheme  
In this section, we will investigate the optimal transmission scheme of an UAV-assisted FSO communication network from the perspective of fairness. We apply a DRL-based approach to arrive at a near-optimal allocation scheme for the transmit power levels. In particular, with a well-trained policy, while noting that a deep neural network (DNN) can model the relationship between the input and output, approximate closed-form expressions for near-optimal transmit power levels can be obtained.  
To ensure the fairness among all authenticated sensors, an objective function is set to their maximum decoding outage probability. Note that fairness performance becomes better when maximum decoding outage probability decreases. Combining all the above analyses, the optimization problem is formulated as follows  
\[\min_{P_{1},\,P_{2},\,\cdots,\,P_{M}} \max\{P_{out,1},\,\,P_{out,2},\,\,\,\cdots,\,P_{out,M}\},\] \[s.t. 0<P_{1}+P_{2}+\cdots+P_{M}\leq P_{\max},\,0<P_{1},\,\,\,\cdots,\, 0<P_{M}.\]  
Here, \(P_{out,i}\) was derived in Section 3, \(i=1,\,2,\,\,\cdots,\,M\). We can see that such an objective function is extremely complex and not convex with respect to all variables. Obviously, it is impossible to derive closed-form optimal solutions in this case. Note that the FSO channel parameters are unstable under dynamic weather conditions. With iterative algorithms, the optimization process needs to be repeated when the model parameters change, which will lead to a very high computational complexity. In addition, the objective function involves a complicated Meiger-G function, which does not have a gradient expression. As such, the gradient ascent approach is not feasible for handling the corresponding optimization issues. As such, we intend to use the DRL approach to arrive at a near-optimal policy model for approximating the closed-form expressions of the optimal transmit power levels. The state space \(\mathbb{S}\) is defined as \(\{\alpha_{SR},\,\beta_{SR},\,\,\alpha_{RD_{1}},\,\,\beta_{RD_{1}},\,\alpha_{ RD_{2}},\,\beta_{RD_{2}},\cdots,\,\alpha_{RD_{M}},\,\beta_{RD_{M}}\}\), and the action space \(\mathbb{R}\) is defined as \(\{P_{1},\,\,P_{2},\,\cdots,\,P_{M}\}\).
# Fundamental Spacetime Representations of Quantum Antenna Systems
## 6 Quantum Antenna Radiation Patterns: Basic Constructions
### The Probability Law of Receiving Radiated Quantum States: Source-Receiver Coupling Gain Estimation
We can use (66) to express the position vectors as  
\[\mathbf{x}_{\rm r}=|\mathbf{x}_{\rm r}|\hat{x}_{\rm r}(\Omega), \tag{77}\]  
where the unit angle-only dependent unit vector \(\hat{\mathbf{x}}_{\rm r}\) is defined by  
\[\hat{x}_{\rm r}(\Omega):=\frac{\mathbf{x}_{\rm r}}{|\mathbf{x}_{\rm r}|}. \tag{78}\]  
By substituting the relations (76), (77), and (78) into the gain Formula (75), the following angular expression is obtained:  
\[\mathcal{G}(\Omega;|\mathbf{x}_{\rm r}|)=\int_{\mathbf{x}^{\prime}\in S_{\rm r },t^{\prime}\in T_{\rm s}}\mathrm{d}^{3}\mathbf{x}^{\prime}\mathrm{d}t^{\prime }\,G_{q}^{a}(\Omega;|\mathbf{x}_{\rm r}|,x^{\prime})J_{\rm s}(x^{\prime}), \tag{79}\]  
where  
\[G_{q}^{a}(\Omega;|\mathbf{x}_{\rm r}|,x^{\prime}):=\int_{t\in T_{\rm r}} \mathrm{d}t\,G_{q}(|\mathbf{x}_{\rm r}|\hat{x}_{\rm r}(\Omega)-\mathbf{x}^{ \prime};t-t^{\prime})J_{\rm r}(t), \tag{80}\]  
and the notation of (27) was used. The integral (80) can be computed numerically for known current source distributions \(J_{\rm s}(x)\) by using the special functions representation of the Feynman propagator \(G_{q}(x-x^{\prime})\) given in Appendix H.  
**Remark 24**. : _We note that the q-antenna system gain Function (79) depends on the distance between the source and the receiver in addition to the angles. In general, one studies the asymptotic behavior of \(G_{q}^{a}(\Omega;|\mathbf{x}_{\rm r}|,x^{\prime})\) in the long distance limit \(|\mathbf{x}_{\rm r}|\to\infty\), with the hope that this behaves as \(|\mathbf{x}_{\rm r}|^{-n}\), with \(n\) often a small integer (usually \(1\) or \(2\)). From the asymptotic limits of the special functions used in Appendix H, some possible relations could be derived using the bessel function large argument approximation, which in turn then might be further utilized in order to eliminate the dependence on the radial distance in the gain pattern (79). However, such detailed computational considerations are outside the scope of this paper, which is mainly focused on the general fundamental theory._
[24] proposed an uncertainty planning model to address irrigation shortages and boost crop productivity. [PERSON] et al. [25] employed statistical models and neural networks to optimize irrigation under different climate scenarios. [PERSON] et al. [26] created a fuzzy interval model to tackle water management issues and maximize system benefits. [PERSON] et al. [27] showcased a multi-objective optimization model to improve water supply efficiency, while [PERSON] et al. [28] developed a model incorporating crop price uncertainty to optimize water allocation. In summary, research on the optimal allocation of water resources in irrigation areas has evolved from focusing on single objectives to multiple objectives, single sources to multiple sources, single levels to multiple levels, static to dynamic models, and certainty to uncertainty. Currently, most studies on optimal water resource allocation concentrate on river inflow and groundwater, with limited research considering the reuse of return flow in irrigation areas, especially under the uncertainty of return flow. This paper takes the Jingdian Irrigation District in Northwest China as a case study and establishes an optimal allocation model of water resources that accounts for both the reuse and uncertainty of return flow. It analyzes the impact of return flow reuse on irrigation efficiency, crop planting structure, and water resource allocation within the district. The study aims to provide theoretical support for the efficient utilization of water resources in irrigation districts.
# Spatial and Temporal Dependency of NDVI Satellite Imagery in Predicting Bird Diversity over France
## 4 Discussion
### Adding Habitat to Climate Variables Improve Predictions of Local Bird Patterns at National Scale  
We observed that adding NDVI or DHI data reflecting local habitats in bioclimatic models greatly improve the performances of species richness estimations. The utility of habitat variables was particularly important for woodland and farmland specialists, and much less for generalist species whose individuals can find their resources in a wide range of habitats. This result is in line with previous studies showing the role of climate as driver of bird species distribution at macro-scale, combined with land cover at finer spatial resolutions [59; 60]. However, the role of climate as causal determinant of species' geographic distributions is still in debate [61; 62]. Positive correlation with climate could appeared for non-climatic reasons, due to the existence of spatially structured processes which are themselves not necessary related to coarse environmental variables [63]. At fine resolution, landscape and habitat composition which is highly variable between plots effectively increases the performance of species richness models. NDVI data can thus be of great value for building bird models, especially for specialist species whose populations decline drastically [64].
## 4 Methodology
### Research Methodology  
Confirmatory factor analysis (CFA) and structural equation modeling (SEM) were performed using AMOS. SEM was chosen over other methods, such as multiple regression, because SEM offers higher statistical rigor [55]. SEM is a validation technique used to examine previous studies' conceptual models and assess the consistency of these theoretical frameworks with the collected data [56]. This method can estimate measurement errors for independent and dependent variables and simultaneously analyze all relationships in the proposed model. At the same time, this comprehensive approach reduces statistical errors and thus minimizes the risk of drawing incorrect conclusions [57].
# Integrating Prior Knowledge into Attention for Ship Detection in SAR Images
## 5 Experiments and Results
### Time Analysis  
We test the time required to represent and combine the prior knowledge individually. Tables 4 and 5 detail the total time spent, the average time spent per image, and the FPS for the different programs in the two phases. The images used to test the time performance are from the training set of SSDD, with a total of 812 images.  
Figure 8: The performance of different \(Eps\) and \(MinPts\) on SSDD and HRSID. The vertical coordinate is the value of IoU and the horizontal coordinate is \(Eps\): \(MinPts\).  
Figure 9: Display of partial clustering results on pictures. Ships from clustering are in red. (**a**) Original image of ships on the sea. (**b**) Clustering results for the ships on the sea. (**c**) Original image of ships near the shore. (**d**) Clustering results for the ships near the shore.  
Representing the prior knowledge includes calculating metrics (e.g., IoU metrics of DBSCAN) for better hyper-parameter selection, which is approximately eight times slower than combining the prior knowledge. However, because the hyper-parameters are preserved during training and inference of models, the heavy cost is one-off and is the disposal in time complexity. Table 5 demonstrates the complete elapsed time for generating and representing prior knowledge as well as the elapsed time for each component during training and inference. The complete program takes an average of 0.0223 seconds to process an image on the GPU and achieves an FPS of over 44.