esa-sceva/satcom-chunk-collection huggingface dataset:
# Building Scalable and Secure Multicast Delivery Infrastructure in a Local Area Network
## 2 The Motivation
### Router Authentication  
Apart from the management data, routing protocols must also deliver routing table updates securely to their peer routers. In a large-scale network, any rogue or a malicious router may immediately become a peer to other routers and send either forged default route or bogus route to every network with the best cost. This will make routing table in every legitimate routers to point towards the malicious router for the best path. The rogue router does so to launch a DoS attack or to sniff the network for any valuable information.  
Router authentication is used to avoid this problem. Neighbouring routers use the authentication information to verify the contents of the routing information received from their peers. In other words, router authentication prevents routers in the network from accepting and processing routing updates from unauthorized routers that a hacker can use to create a DoS attack.  
Not all routing protocols support authentication. Protocols such as Intermediate System-to-Intermediate System (IS-IS), Open Shortest Path First (OSPF), IP Enhanced Interior Gateway Routing Protocol (EIGRP) and Routing Information Protocol version 2 (RIPv2), and Resource Reservation Protocol (RSVP) allows network administrators to configure an authentication method and password [3].  
The following authentication methods are supported:  
1. **Plaintext based authentication:** In a plain-text authentication, symmetric keys are used by peer routers for authentication. Some protocols allow multiple symmetric keys to be configured for authentication purposes. Whenever multiple keys are used, keys are uniquely numbered for authentication purposes. In a plain-text authentication, an updating router sends its router update along with its key in plain-text and the key-number (in case multiple keys are configured). The remote router that receives this routing update first compares the received key with its stored key along with the key number. If the two keys match, the remote router then will process the routing update; otherwise, it will ignore the routing update. Since keys are transmitted in plain-text, this scheme is highly insecure.
2. **Message Digest Version 5 (MD5) and Keyed-Hash Message Authentication Code (HMAC):** In this scheme, keys are not sent like a plain-text authentication. Instead, keys are used to create a message digest. Based on the key number, a receiving router will recompute the hash value. If there is a match, the routing updates are processed; otherwise, they are rejected.  
As we mentioned earlier, one or more keys need to be manually configured onto every router. This task is labor-intensive. Whenever a router is decommissioned, keys need to be removed to avoid attackers using these devices to launch an attack. The network administrators must also ensure that the startup configuration on each router is protected using passwords. Otherwise, an attacker can gain the knowledge of these keys.
# Adaptive Solar Power Forecasting based on Machine Learning Methods
## 4 Adaptive Solar Power Forecasting Algorithm
### Effectiveness of the ASPF  
As more data collected, there will be more and more historical data in database, so it is not proper to use an invariant parameter as \(\Delta_{1}\) to search for the similar day. Meanwhile, the potential function in [21] has a good performance of slow decaying. Based on this, the decaying of \(\Delta_{1}\) is defined as:  
\[\Delta_{1,up}=\Delta_{1}e^{-\frac{2}{\hbar}(1-\frac{L_{\alpha}}{L_{\alpha}}) \arctan(NL)} \tag{16}\]  
where \(\Delta_{1}\) is current value, \(\Delta_{1,up}\) represents the updated value of \(\Delta_{1}\) in the presence of multiple similar days, \(L_{\alpha}=\frac{1}{NL}\sum_{l=1}^{NL}\mathbb{L}(l)\), and \(\mathbb{L}=\{L(n)\leqslant\Delta_{1}\}\), \(L_{\alpha}=\min\{\mathbb{L}\}\). In Equation (16), \(\Delta_{1}\) is updated only when more than two similar days exist. The flow chart in SDM is shown in Figure 5(a).  
Similarly, the forecasting error gradually decreases with the increasing amount of the testing data. In addition to this, in the system, the networks can be re-trained when the predictive error beyond the \(\Delta_{2}\). Meanwhile, if \(\,\text{MxAE}\) of the testing day \(H(d)=\max|\hat{y}_{d}(t)-y_{d}(t)|\), \(t=1,2,\cdots,T\) is less than the initial \(\Delta_{2}\), it is then to decrease \(\Delta_{2}\), obtained from Equation (9), where \(d\) represents the index of the day. The decreasing of \(\Delta_{2}\) follows  
\[\Delta_{2,up}=\Delta_{2}e^{-(1-\frac{H(d)}{\Delta_{2}})\frac{H(d)}{\Delta_{2}}} \tag{17}\]  
where \(\Delta_{2,up}\) is the updated value of \(\Delta_{2}\). The flow chart of updating BPNN and \(\Delta_{2}\) is shown in Figure 5(b).  
Figure 7 shows the complete flow chart of ASPF, in which the forecasting accuracy can be improved gradually, and the historical data is thus used more effectively. Moreover, according to  
Figure 6: The flow chart for searching similar days.  
the similar daily information, ASPF adaptively identifies and compensates for the predicted solar power day-ahead.
# A V2V Identity Authentication and Key Agreement Scheme Based on Identity-Based Cryptograph
## 3 Requirement Analysis of V2V Identity Authentication and Key Agreement  
The C-V2X system security includes confidentiality (unauthorized equipment cannot obtain valid information), authentication (the sender and receiver of the information are registered equipment), and integrity (the data cannot be tampered). Cryptographic protocols need to be used to ensure the information will not be stolen, tampered, forged, and used in other attacks. The C-V2X system is complex, involves a wide variety of information, including user identity, location, route, and other privacy data. It is necessary to design appropriate security protection schemes for different scenarios.  
The C-V2X equipment communicate with each other through the PC5 interface to achieve data interaction between vehicles (V2V communication). Using V2V communication, vehicles can share information, such as traffic conditions, traffic lights, and traffic flow collected by sensors with each other. The driver can enhance auxiliary driving safety with the information provided by other vehicles. Multiple vehicles cooperatively driving can achieve complex driving scenes, such as formation driving and automatic driving. The vehicles can exchange speed, position, and heading information with each other to avoid accidents, such as collisions. For example, when the vehicle in front finds a traffic accident on the road ahead, the vehicle in front will brake urgently and send the accident information to the vehicle behind. The vehicle behind will use visual, tactile, and auditory alarms to warn the driver and automatically take avoidance measures, such as braking when necessary, to avoid traffic accidents, such as rear end collisions.  
The PKI mechanism based on a public key certificate is used to ensure the secure authentication and secure communication between the equipment, using digital signature and encryption. The CA (certification authority) system is a complex system and may adopt a hierarchical structure. In small- and medium-sized C-V2X systems (for example,the Internet of Vehicles system in small- and medium-sized cities, the auto drive system in a logistics center, the vehicle management system in an industrial park, etc.), certificate verification requires large computing and time overhead and has a great impact on the communication delay. Therefore, the authentication protocol based on PKI cannot fully meet the C-V2X lightweight requirement [9]. It is not necessary for the C-V2X system to establish a complex CA system. A flat and efficient authentication and encryption scheme is needed to ensure the C-V2X system security. The V2V communication needs a low-bandwidth and low-latency security scheme [10].
## 4 Atmospheric Turbulence Channel  
Differences in the air and variation between the refractive index structure (RIS) \(\text{C}_{\text{n}}^{2}\) and Earth's surface temperature are responsible for the scintillation effects [8]. These findings are focused on the probability density function (pdf) of the Gamma-Gamma (GG) model [5; 8; 9; 29; 30; 31; 32; 33; 34].  
\[p_{\text{GG}}\ (h_{Z})=\frac{2(\alpha\beta)^{(\alpha+\beta)/\ 2}}{\Gamma( \alpha)\ \Gamma(\beta)}h_{Z}^{((\alpha+\beta)/2)-1}K_{\alpha-\beta}\ \Big{(}2\sqrt{\alpha\beta h_{Z}}\Big{)};\ h_{Z}>\ 0 \tag{16}\]  
where \(h_{Z}\) is the total attenuations due to the signal \(\left(\text{h}_{\text{sig}}\right)\) and interfering \(\left(\text{h}_{\text{int}}\right)\) and pointing errors. \(\alpha\) and \(\beta\) are the scattering methods of the influence of the large and small-eddies, respectively, and \(\Gamma(\cdot)\) is the gamma function, and \(\text{K}_{\text{n}}(\cdot)\) is the modified second kind Bessel function [8; 10].  
\[\alpha=\left\{\exp\left[\frac{0.49\sigma_{R}^{2}}{\left(1+0.65d^{2}+1.11\sigma_ {R}^{12/5}\right)^{7/6}}\right]-1\right\}^{-1} \tag{17}\]  
\[\beta=\left\{\exp\left[\frac{0.51\sigma_{R}^{2}\Big{(}1+0.69\sigma_{R}^{12/5} \Big{)}^{-5/6}}{1+0.9d^{2}+0.62d^{2}\sigma_{R}^{12/5}}\right]-1\right\}^{-1} \tag{18}\]  
\[\text{d}=\sqrt{\text{k}\text{D}_{\text{RX}}^{2}\ /4l_{fso}} \tag{19}\]  
where d is the normalized RCLs [8; 9; 10; 30; 33] and D\({}_{\text{RX}}\) is the RCL diameter. \(\text{C}_{\text{n}}^{2}\) is the RIS constant, \(l_{fso}\) is the free-space distance, \(k=2\pi/\lambda\) is the wave-number, and \(\lambda\) is the wavelength [29; 30; 34; 35; 36; 37; 38; 39; 40]. The Rytov variance (RVAR) \(\text{\sigma}_{\text{R}}^{2}\) characterize the different AT regimes over the GG pdf, if the resulting \(\text{\sigma}_{\text{R}}^{2}>\ 1\); we use the ST, if the RVAR \(\text{\sigma}_{\text{R}}^{2}\ \approx\ 1\), we use the MT, if RVAR \(\text{\sigma}_{\text{R}}^{2}<1\), we apply the WT [8; 9; 10; 29]; and if saturated, turbulence \(\text{\sigma}_{\text{R}}^{2}\rightarrow\infty\) are given as in [8; 9; 10; 40; 41; 42; 43; 44; 45; 46]:  
\[\text{\sigma}_{\text{R}}^{2}=1.23\text{C}_{\text{n}}^{2}\text{k}^{7/6}\ l_{fso}^{11/6} \tag{20}\]
# Hybrid Multi-Granularity Approach for Few-Shot Image Retrieval with Weak Features
## 4 Experimental Design
### Datasets  
1. SubCATHR-DET Dataset: A subset of the Carrier-based Aircraft Tail Hook Recognition Dataset (CATHR-DET) [43]. The CATHR-DET dataset consists of 400 real samples and 3600 synthetic samples, with an image resolution of 1300 x 600 pixels. It encompasses various weather conditions, including sunny, cloudy, and rainy days. The dataset is characterized by small targets, weak features, and complex backgrounds. Small targets refer to the fact that the tail hook occupies an extremely small area in the image, with a size of only 50 x 50 pixels, accounting for merely 0.32% of the entire image (1300 x 600 pixels). Compared to the overall structure of the aircraft, the tail hook is even more insignificant. Weak features indicate that the contours and textures of the tail hook are faint, making it difficult to distinguish from other parts of the aircraft and background elements. Moreover, the shape and position of the tail hook vary significantly due to the different angles and perspectives during landing, which further increases the difficulty of distinguishing the tail hook from the background. Complex backgrounds mean that the signal-to-noise ratio (SNR) between the tail hook and background elements (such as the flight deck, sea surface, and clouds) is low. Particularly under adverse weather conditions (such as cloudy or rainy days), the visibility of the tail hook is further reduced, which may interfere with target recognition. Therefore, the carrier-based aircraft tail hook recognition data are classified as weak-feature, small-target data. For this study, 260 images (30 real samples and 230 synthetic samples) were randomly selected from the CATHR-DET dataset to form the SubCATHR-DET dataset. The training set comprises 1600 images with a resolution of 50 x 50 pixels, which were cropped from 10 images of carrier-based aircraft landing  
Figure 6: The multi-granularity retrieval algorithm workflow.  
with a resolution of 1300 x 600 pixels. To avoid data redundancy, images with high similarity were removed. The test set includes 200 images of carrier-based aircraft landing. The query images consist of 50 images with a resolution of 50 x 50 pixels containing tail hooks, which were cropped from images of carrier-based aircraft landing that include tail hooks.
2. CUB-200-2011 Dataset: A fine-grained bird dataset with 200 subcategories that has a total of 11,788 images [44]. Each subcategory contains approximately 60 images. For experiments, 100 bird species were randomly selected, with 80% for training, 2 images per class for queries (200 total), and the remainder as the retrieval database.
## 2 Methods
### Data Processing, Image Registration, and Contouring of Structures of Interest  
To ensure their compatibility with the software used for the further analyses (i.e., RayStation(r)), the daily CBCTs were converted from their native format, a combination of RAW images and TXT files containing their DICOM tags, into the standard DICOM format using an in-house algorithm developed with MATLAB version R2020b (MathWorks(r)). The script allowed us to merge and normalize the imaging data stored on the RAW file, as well as the DICOM information present in the TXT file, into one single DICOM file. All the lPlannet(r) plans and daily CBCTs were then imported into Raystation(r).  
A rigid registration between the simulation CT (s-CT) (CT Optima 580, General Electric Healthcare, (2.5 mm slice thickness) and each daily CBCT (1 mm slice thickness) was performed. The registration was based firstly on the bony anatomy and secondly on the gross tumor volume (GTV) in order to reproduce the condition of the target verification position at the time of treatment, and the position was corrected for the translation, pitch, and roll direction through the robotic couch. The delineation of the GTV, planning target volume (PTV) (margins of 3 mm or 5 mm), and OARs for each patient was performed using the s-CT and each daily CBCT to observe the volume variations of the structures and evaluate geometric discrepancies due to their deformation during the treatment (inter-fraction variability). The contouring was performed by the same radiation oncologist for both the s-CTs and CBCTs in order to avoid inter-observer variability [16], according to the RTOG guidelines.  
As far as the colon and ileum were concerned, only the intestinal loop closest to the target was contoured. Since the field of view of the Vero system was smaller than that of the CT (20 cm vs. 65 cm diameter), only the OARs visible in their entirety were outlined in the daily CBCTs.
# Effect of Zr Modification on NH\({}_{3}\)-SCR Reaction Performance of Cu-Ce/SAPO-34 Catalysts
## 4 Conclusions  
The overall denitration performance of 4 Cu-4 Ce-4 Zr/SAPO-34 was only inferior to that of 4 Cu-4 Ce/SAPO-34. At 350-400 \({}^{\circ}\)C, the denitration efficiency was more than 80%, and at 400-500 \({}^{\circ}\)C, the denitration efficiency was more than 99%. Zr could improve the low-temperature performance of NH\({}_{3}\)-SCR below 200 \({}^{\circ}\)C but reduced its overall denitration performance. The main reason was that Zr could reduce the amount of Cu and Ce metal ions on the catalyst surface, thus decreasing the total acid content of the acidic sites on the catalyst surface.  
An appropriate amount of Zr could increase the relative crystallinity of the molecular sieve. There was an interaction between Zr and the CHA structure of SAPO-34, which could enhance its skeleton structure. In addition, Cu, Ce, and Zr were well dispersed, but excessive Zr damage the CHA structure, leading to a decline in the catalyst's denitration performance.  
There were Lewis acid sites and Bronsted acid sites on the surface of the 4 Cu-4 Ce-4 Zr/SAPO-34 catalyst, which was dominated by Lewis acid sites. Zr could increase the amount of weak Lewis acid sites and enhance the catalyst's adsorption capacity for NO\({}_{x}\), which is of great significance for its NH\({}_{3}\)-SCR reaction performance. The reaction process only followed the E-R mechanism, not the L-H mechanism.  
Conceptualization, X.W.; software, X.Z.; formal analysis, C.L. and M.L.; investigation, X.W., L.X., X.C., X.Z. and H.L.; resources, C.L., X.Z. and H.L.; data curation, C.L., L.X., H.L. and M.L.; writing--original draft, C.L.; writing--review & editing, L.X.; supervision, X.W. and X.C.; project administration, X.C., H.L. and M.L.; funding acquisition, X.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No. 50806020), Excellent Youth Foundation of Henan Scientific Committee (No. 114100510010), Henan Province Science and Technology Research Project (No. 152102210280), and The Natural Science Foundation of Shandong Province (No. ZR2021 QE066).  
The authors declare no conflict of interest.
## 4 Mean Function Evaluation
### Evaluation Results  
The evaluation results for the different settings are shown in Figure 6. The root-mean-square error (RMSE) between the measurement from the test set and the predicted value of the GP with the indicated mean function (\(m_{\text{3 GPP}-\text{UMi}}\), \(m_{\text{CD}}\), \(m_{\text{3 GPP}}\), \(m_{\text{SIM}}\), \(m_{\text{ZERO}}\)) or the simulation without a GP (\"only SIM\") is plotted in different colors using logarithmic scaling.  
It can be seen that in all settings, the RMSE of the simulation-based mean function (\(m_{\text{SIM}}\)) and the simulation without a GP (\"only SIM\") is mostly independent of the number of training points. For settings (a) and (b) (see Figure 5) \"only SIM\" cannot be distinguished from the GP-based \(m_{\text{SIM}}\). However, in setting (c), the RMSE for \"only SIM\" is even lower than that of the GP variant, starting at around 350 training points. For setting (d), it is the other way around, starting at roughly 250 training points. This indicates that the GP can improve the prediction if the prediction location is not too far away from the training points. However, it is also evident that in settings (b) and (d), both simulation-based configurations are outperformed by the other GPs for larger numbers of training points.  
Both \(m_{\text{ZERO}}\) and \(m_{\text{3 GPP}}\) are unable to significantly improve within settings (a) and (c). For settings (b) and (d), the improvement is correlated with the number of training points.  
Figure 5: Evaluation setups with different means to separate the test set from the training set. Training point (candidates) are labeled in green, test points are labeled in blue, and discarded points are labeled in red. The base station is marked with a drop pin. (a) Height separation. (b) Cluster separation. (c) Distance separation. (d) Random split (example). Background: © OpenStreetMap contributors, CC BY-SA.  
\(m_{\text{CD}}\)m on the other hand, shows a visible improvement with the first few training points. In setting (d), \(m_{\text{CD}}\) shows the lowest RMSE for most runs above 250 training points.  
The pre-parameterized \(m_{\text{3 GPP-UMi}}\) mean function starts with the lowest RMSE when using fewer than 15 training points, except for the simulation-based configurations. However, with more training points, it is outperformed by the \(m_{\text{CD}}\) mean.
## 3 Results and Discussion
### Cu(II) and Phosphate Removal Efficiency  
To determine the capability of FHO and the effect of alginate for the removal of Cu(II) as well as phosphate, a variety of batch tests were conducted at different concentrations of Fe and alginate on FHO. Figure 2a shows the removal efficiency of Cu(II) and phosphate for bare FHO and FHO-A at a concentration of 1.0 and 2.0 mM of Fe. For Cu(II) removal, FHO was shown to remove only 2\(\sim\)3% of Cu(II) at both tested Fe concentrations, whereas FHO-A showed \(\sim\)40% Cu(II) removal efficiency in the same Fe concentration range, indicating that Cu(II) removal was not affected by the presence of Fe. As such, these results show that Cu(II) removal is dependent only on the presence of alginate in these systems. Completely different results were shown for phosphate. The increased concentration of Fe from 1.0 and 2.0 mM increased the phosphate removal efficiency from 40 to 60% and the effect of alginate was negligible. Therefore, it can be concluded that Cu(II) and phosphate removal occurred only due to the presence of alginate and Fe, respectively. Figure 2b shows the impact of different alginate concentrations added on FHO. The removal of phosphate was consistent, regardless of the concentration of alginate, indicating that the presence of alginate on FHO does not affect phosphate removal by FHO, whereas the efficiency of Cu(II) increased from \(\sim\)4% up to \(\sim\)55% in the presence of increased alginate levels. Based on these results, FHO and alginate act independently from each other for cation and anion removal. Based on these findings, the mechanism detailed in Figure 3 which shows the electrostatic force between the carboxyl group of alginate and Cu(II), the surface charge of FHO and phosphate is proposed to account for Cu(II) and phosphate removal on FHO-A.  
Figure 1: FTIR spectra for (**a**) alginate, bare FHO and FHO-A and (**b**) after Cu(II) and phosphate adsorption for FHO and FHO-A.  
A linear regression analysis of alginate concentration versus Cu(II) removal efficiency, shown in Figure 2c, clearly demonstrates a strong correlation between these factors (R\({}^{2}\) = 0.992). Consequently, the direct and proportional removal of Cu(II) showed that only the alginate on the FHO-A was involved in Cu(II) adsorption.
# Similarity Model of a Rotary Lobe Expander Working with Various Fluids
###### Abstract  
This paper presents the similarity model of a rotary lobe expander (Roots expander) and calculates its power based on its other reference power. The rotors in these expanders transfer unchanged gas volume within one rotation. In this model, the expander's construction is represented by the single value of this transferred volume. The derived model concerns the similarity of pressure loss due to the fluid viscosity, which decreases the ideal maximum power of the limiting case of no viscosity. The basic thermodynamic analysis of various fluids is also presented for this case. The novelty is the similarity model of this expander type, which may be used in the analytical and fast engineering calculation of the power before the use of the more precise but expensive numerical calculation. The similarity model is applied to the exemplary rotary lobe expander GGP16 XA from Armak Motors. In this paper, the similarity model is applied in two cases. The first application concerns the manufacturer's data on the power from the compressed air of various inlet pressures and rotational speeds. The second one concerns the numerically calculated power from various fluids: Air, R245 fa, SES36, and HFE7100, with the power from Air as a reference. The analyzed fluids are used in Organic Rankine Cycle (ORC) applications. In both cases, the obtained similarity power is in good agreement with the reference, with the same maximum relative error \(\pm 5\%\).  
rotary lobe expander; Roots expander; similarity model; various fluids; Organic Rankine Cycle (ORC) 2024