esa-sceva/satcom-chunk-collection huggingface dataset:
# Joint Pedestrian and Body Part Detection via Semantic Relationship Learning
## 2 Related Work
### Non-Maximum Suppression  
The success of deep learning based pedestrian detection methods relies on not only the powerful representation ability of CNNs, but also the non-maximum suppression (NMS) [3] to handle the duplicated detections. The principle of NMS is that a bounding box with the local maximum score is selected as the final detection and the other bounding boxes within the pre-defined overlap threshold are suppressed. According to the algorithm, if a foreground object lies within the pre-defined overlap threshold of a local maximum score, it will be suppressed and lead to a miss detection. To solve this problem, [PERSON]. [29] proposed a variant of NMS, named as Soft-NMS, which replaces the suppression strategy in NMS by reducing the scores of the bounding box, which should be suppressed by NMS, according to their overlap degrees. Different from NMS which works as a post-processing step, [PERSON]. [16] implemented a duplicated detection sub-network, making it possible to perform detection and duplication removal in an end-to-end manner. While these traditional NMS methods have been widely used in single-class or multi-class object detection tasks, they are not well suited for the joint detection of pedestrian and its body parts.  
Figure 1: Some example images from the CUHK-SYSU Person Search Dataset [25] illustrate the challenges for joint pedestrian and part detection because of image blurring, occlusion, scale diversity, etc.
## 3 Simulation and Experiment
### Loading and Lifting Test
#### 3.3.2. Loading Test  
After the lower link was connected to the device at the initial position, the hydraulic loading cylinder was applied to the lower link to perform a load test. The actual load-bearing performance of the designed device was tested at the particular locations during the lifting process. The loading force of the lower link was increased by 5 kN. The initial  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**No**} & \multirow{2}{*}{\(\beta\) **(\%)**} & \multirow{2}{*}{\(t_{1}\) **(s)**} & \(\dfrac{H}{\text{(mm)}}\) & \multirow{2}{*}{\(t_{2}\) **(s)**} & \multirow{2}{*}{**No**} & \multirow{2}{*}{\(\beta\) **(\%)**} & \multirow{2}{*}{\(t_{1}\) **(s)**} & \(\dfrac{H}{\text{(mm)}}\) & \multirow{2}{*}{\(t_{2}\) **(s)**} \\  & & & & & & & & & \\ \hline
1 & 100 & 5 & 760 & 7 & 11 & 100 & 4 & 760 & 6 \\
2 & 100 & 6 & 762 & 8 & 12 & 100 & 7 & 760 & 7 \\
3 & 100 & 4 & 761 & 8 & 13 & 100 & 5 & 760 & 6 \\
4 & 100 & 6 & 760 & 9 & 14 & 100 & 5 & 759 & 7 \\
5 & 100 & 6 & 759 & 8 & 15 & 100 & 5 & 758 & 7 \\
6 & 100 & 4 & 759 & 7 & 16 & 100 & 7 & 756 & 8 \\
7 & 100 & 4 & 761 & 6 & 17 & 100 & 6 & 761 & 6 \\
8 & 100 & 5 & 760 & 9 & 18 & 100 & 4 & 761 & 7 \\
9 & 100 & 5 & 760 & 8 & 19 & 100 & 5 & 761 & 7 \\
10 & 100 & 6 & 760 & 7 & 20 & 100 & 4 & 760 & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hitch and lifting test.  
Figure 8: The load-lifting device of the lower link.  
value of the loading force of the lower link was 5 kN, and the maximum value was 40 kN. The loading test was repeated five times under the test conditions of different load forces.  
During the lifting process, the particular positions were as follows: initial position of the lower link, the horizontal position of the lower link and the lever, the maximum height of the lower link. The four special positions were defined as I, II, III, and IV, respectively. When the components were in different special positions during the loading test, the loading force of the lower link was collected by the force sensor. The force sensor was purchased from Shenzhen Gensun Technology Co., Ltd. (China), with a capacity load of 100 kN and an error of \(\pm\)0.05% of the full scale. The experimental results were shown in Figure 9.  
According to the test results, when the initial loading force was 5 kN and 10 kN, the loading force of the lower rod could not be well maintained, and the loading force was increased with the continuous rotating of the lower link. This could be solved by increasing the hydraulic pipe diameter and increasing the return oil speed. When the loading force of the lower link exceeded 15 kN, the variation of the loading force on the lower link was small, and the load could be kept stable.
## 5 Security Analysis
### Access Control  
Assume that \(l\) data generators (\(dg=\{dg_{i},i\in[1,l]\}\)) are involved in the system in the suggested manner. Data generators create a search trapdoor for the \(dr\) using an \(SC\) in the blockchain to accomplish access control. First, each data generator checks \(\varphi_{i}\) to confirm that the keyword (\(\hat{k}\in K\)) is genuine. The aggregated contract calculates \((E^{y_{i}}_{0 ij}=\hat{k}\oplus E_{5 ij})\) after obtaining the data generators' authorization \((T_{0i},X)\) and request \((X,\hat{k})\). It then extracts the corresponding file keys \((P_{agg},S_{agg})\) that compose the targeted keyword (\(\hat{k}\)) in the file set (\(Z_{i}\)). After that, using a searchable key (\(PP_{agg}\)) for the permitted \(dr\), it creates a search trapdoor (\(\tau=(\tau_{1},\tau_{2})\)). Only the authorized \(dr\) may use the search trapdoor (\(\tau_{2}\)) to look for term \(\hat{k}\). Nevertheless, the \(dr\) is unable to generate additional trapdoors for a new set (\(Z^{\prime}_{i}\)) and search for \(k\)s in files that have nothing to do with recognized trapdoors. Additionally, the plan creates a search trapdoor and extracts the relevant file keys using the aggregate \(SC\). Thus, access control can be achieved with our technique.
## 6 Experiments and Results
### Training Efficiency  
The time efficiency of different models is also evaluated during the experiments. From Table 3, it is obvious that adding images will lead to the rapid growth of training time due to the increase of the model complexity. When all five camera images are introduced into the input data, training takes much more time than only using front camera images. However, the previous result has shown that the difference between the performance of the model using only the front camera and that using all cameras is small. Thus, it is more reasonable to only use the front camera image as the image input because it is more computationally efficient than using images from all five cameras.
# Prediction of Microwave Characteristic Parameters Based on MMIC Gold Wire Bonding
## 1 Introduction
[PERSON] and [PERSON] [23] proposed a design method based on the integrated structure of chip packages, which showed that the Random Forest algorithm can predict the stresses of chip design. [PERSON] et al. [24] predicted the radiated electric field in bonding wire packages by deep learning methods, optimizing the model parameters to reduce the prediction error. Although many scholars optimized and predicted the model parameters by deep learning methods, fewer scholars studied the microwave transmission characteristics by the arch height and span of the gold wire bonding interconnect wires in the field of integrated circuit packaging. Compared with the  
Figure 1: Microwave multi-chip circuit. traditional model structure, this paper designed a capacitive compensation structure, where the chip is connected to the microstrip line through the gold wire bonding interconnect wires, which improves the microwave transmission characteristics. At the same time, the gold wire bonding technology used in the gold wire becomes thinner and thinner, the bonding arch height decreases, the bonding span decreases, the number of bonding alloy wires in the chip increases, and the complexity of the traditional measurement of the microwave transmission characteristics of bonding alloy wires increases, so that this hinders the rapid development of the encapsulation technology. In this paper, we propose a deep learning-based method to achieve the prediction of bonding alloy wire parameters. Through an artificial neural network model built independently, the correspondence between the arch height and span of the bonding alloy wire and the insertion loss, return loss and standing wave ratio is predicted, and compared with previous studies, the method can quickly and accurately deduce whether the microwave characteristics of the bonded products are qualified, which greatly reduces the time and economic cost of engineers, improves the efficiency, and lays a solid technical and theoretical foundation for higher frequency research. To verify the effectiveness of our proposed method, we scanned the S-parameters, obtained a relatively large amount of data, and conducted extensive experiments on the dataset. The experimental results show that our proposed prediction method is faster and more accurate than the previous tested methods. This paper is organized as follows: Section 2 introduces the model and relevant data for this experiment. Section 3 presents our proposed method. Section 4 outlines the experimental results and analyzes them. In summary, this work has made the following contributions:  
1. Construction of a model for gold wire bonding and continuous optimization of this model for data set acquisition and comparison of prediction results;
2. Designing a fully connected neural network model with a prediction accuracy generally better than the conventional model for the prediction of microwave transmission characteristics;
3. The experimental results show that our method achieves fast and accurate prediction compared with the simulation results of the model.
## 3 Results and Discussion
### EE2 Biodegradation in Minimal Salt Medium (MSM)  
We examined the ability of the SBP-encapsulated _R. zopfii_ and _P. putida_ F1 cultures to biodegrade EE2 as the sole carbon source. Figure 2A presents the EE2 biodegradation rate in MSM medium by the two encapsulated cultures. The initial EE2 concentration was 0.415 mg/L for the control system, 0.459 mg/L for the _R. zopfii_ system and 0.45 mg/L for the _P. putida_ system. Both _R. zopfii_ and _P. putida_ treatments showed an overall reduction of over 70% within 24 h, while the sterile control did not change the hormone concentration. After 48 h, the EE2 concentration in the _R. zopfii_ system decreased to 0.044 mg/L, whereas no further decrease was observed in the _P. putida_ system, indicating a final biodegradation of about 90.4% and 72.7%, respectively, demonstrating that the SBP-encapsulated _R. zopfii_ showed a better biodegradation ability compared to encapsulated _P. putida_.  
Interestingly, we did not expect _P. putida_ to be able to use EE2 as the sole carbon source, since it was reported that the EE2 breakdown mechanism by _P. putida_ is related to co-metabolism, and occurs via the ammonium monooxygenase (AMO) mechanism and/or nitrification [12]. This result can be explained by three possible mechanisms: (1) The bacteria had completed their growth phase by consuming the available carbon sources and nutrients that were provided by the capsule itself during the activation phase and entered into a stationary phase. EE2 could therefore be co-metabolically degraded by the _P. putida_ capsules for energy production and was not utilized for growth-supporting mechanisms. (2) The AMO mechanism can use EE2 in the presence of a high ammonia concentration, during the nitrification process. The MSM medium contains both Mn\({}^{+2}\) and ammonium, indicating possible involvement of either nitrification by the ammonia-oxidizing and/or manganese-oxidizing process which exist in _P. putida_ strains [18; 24; 25]. However, the ability of _P. putida_ F1 to use EE2 as a sole carbon source requires further research in order to elucidate this mechanism.  
Figure 2: _Cont._
## 3 Material and Methods
### System Design
#### 3.1.4 Dynamic ID allocation  
Dynamic ID allocation indicates that location-dependent IDs are only allocated when the user performs the authentication process. Because the IDs are generated using time and location information, the user has a unique ID each time they perform an authentication process. Of course, the time and location information are automatically managed in the user's digital twin. Therefore, if the malicious user has the user's identity information,it is difficult to interfere with the authentication process. Dynamic ID allocation using location-dependent ID improves the security of the authentication process in the system.
# Free-Space QKD with Modulating Retroreflectors Based on the B92 Protocol
## Appendix B  
The signal passing through the intensity modulator is equivalent to the signal passing through the beam splitter. The transmittance \(t\) (or reflectivity \(r\)) of the beam splitter is equal to the modulation efficiency \(M\) of the intensity modulator, as shown in Figure A3.  
The beam splitter can be expressed as follows:  
\[\left\{\begin{array}{l}a_{in}^{+}=\sqrt{\tau}a_{r}^{+}+\sqrt{1-\tau}a_{t}^{ +}\\ a_{u}^{+}=\sqrt{1-\tau}a_{r}^{+}-\sqrt{\tau}a_{t}^{+}\end{array}\right. \tag{11}\]  
The coherent state incidents the \"\(in\)\" port of the intensity modulator and exists from the \"\(t\)\" port. This model is equivalent to the vacuum state incidents the \"\(in\)\" port of the beam splitter, the coherent state incidents the \"\(in\)\" port and the combined beam exists from the \"\(t\)\" port.  
\[\begin{array}{l}\left|\alpha\right\rangle_{in}\!\!\left|0\right\rangle_{u}=D (\alpha)\!\left|0\right\rangle_{in}\!\!\left|0\right\rangle_{u}=\exp\!\left( \alpha a_{in}^{+}-\alpha^{*}a_{in}\right)\!\left|0\right\rangle_{in}\!\!\left|0 \right\rangle_{u}\\ \rightarrow\exp\!\left(\alpha\!\left(\sqrt{\tau}a_{r}^{+}+\sqrt{1-\tau}a_{t}^{ +}\right)-\alpha^{*}\!\left(\sqrt{\tau}a_{r}+\sqrt{1-\tau}a_{t}\right)\right) \!\left|0\right\rangle_{r}\!\!\left|0\right\rangle_{t}\\ =\exp\!\left(\sqrt{\tau}a_{r}^{+}-\sqrt{\tau}\alpha^{*}a_{r}\right)\exp\! \left(\sqrt{1-\tau}a_{t}^{+}-\sqrt{1-\tau}\alpha^{*}a_{t}\right)\!\left|0 \right\rangle_{r}\!\!\left|0\right\rangle_{t}\\ =\left|\sqrt{\tau}\alpha\right\rangle_{r}\!\left|\sqrt{1-\tau}\alpha\right\rangle _{t}\end{array} \tag{12}\]  
The output at the \"\(t\)\" port is coherent state with \(\sqrt{1-\tau}\alpha\) amplitude. The reflectivity of the beam splitter satisfies \(r+t=1\). In this study, the modulation efficiency \(M\) of the intensity modulator was equal to the transmittance \(t\) of the beam splitter.
## 2 Radiation in Space and Its Effects on Electronic Components
### Methods for Assessing Radiation Exposure in Space Missions
The whole set of ECSS regulates the design, development, implementation, and verification of space systems and their components. In particular, ECSS cover these aspects for Electrical, Electronic, and Electromechanical Components (EEEs), including radiation effects and mitigation, in the Radiation Hardness Assurance (RHA). As for each project the RHA requirements depend on mission profile and hardware design, the standard ECSS-Q-ST-60-15 [23] considers the three radiation types previously mentioned (TID, TNID, SEE), defining how to describe the radiation environment and how to analyze the radiation effects on EEE, and identifies the EEE groups that are potentially sensitive to each type of radiation. The space environment, including radiation, is defined in the standard ECSS-E-ST-10-04 [24], which provides a preliminary starting point for the mission trade-off when selecting the orbit. The calculation then needs to be refined mainly via Monte Carlo analysis (described in ECSS-E-ST-10-12 [25]) considering spacecraft orientation, geometry, and shielding. Adherence to ECSS [26] standards is critical for the success of space missions and the safety of crew members. Designing with RDM is one way to meet these standardsand ensure that radiation tolerance requirements are met. By incorporating an additional margin of radiation tolerance into the systems (from 1.2 to 2 depending on the mission [27]), RDM helps to mitigate the effects of radiation and ensure the reliability of electronic and electrical systems for the entire lifespan of a spacecraft. EEE components are defined in ECSS-Q-ST-60, and a tailoring is provided for the commercial parts, also known as commercial off-the-shelf, by ECSS-Q-ST-60-13 [28]. However, the latter standard does not provide an effective relaxation of the RHA requirements, but just a flow down. As a consequence, under RHA perspective, there is no tailoring available for the New Space business, where COTS are generally used. For this reason, New Space projects are either developed outside the European institutional perimeter, losing the benefit of a good set of ECSS standards, or are required, regardless, to propose a tailoring of ECSS that is difficult to justify, reducing the opportunity of cost optimization for low-budget missions.
## 4 Methodology and Methods
### Pattern-Based Model Training  
Given the input dataset \(\mathcal{D}\) containing information about the historical stations' statuses and the set \(\mathcal{S}\) containing the stations and their geographical locations, the pattern-based model training phase (depicted in Figure 1) is based on the following steps:  
**Neighbourhood identification.** Given the stations' geographical locations and the neighbourhood radius \(d\), the neighbourhood \(N\)(s) for each station \(s\in\mathcal{S}\) is computed.  
**Occupation rate computation.** Given the input dataset \(\mathcal{D}\) and the neighbourhood \(N\)(s) for each station \(s\in\mathcal{S}\), the occupation rate \(OR_{s}(t)\) for all stations \(s\in\mathcal{S}\) and all timestamps \(t\in\mathcal{D}\) is computed, i.e., the occupation is computed for each pair \((s,t)\in\mathcal{D}\).  
**Identification of critical stations.** Given the criticality threshold \(cr\), the occupation rate for each pair \((s,t)\in\mathcal{D}\) and the identified neighbourhoods, the critical rate for all the pairs \((s,t)\) is computed. Then, only the pairs \((s,t)\) associated with either a positively or a negatively critical situations are selected and stored in the dataset \(\mathcal{D}_{\mathcal{CR}}\), enriched with the critical status (positive or negative).  
**Contextualised data partitioning.** Given a contextualised partitioning schema based on timestamp, \(\mathcal{D}_{\mathcal{CR}}\) is split into N non-overlapping partitions \(P_{i}\). A partition \(P_{i}\) is a logical group defined on input data, related to a specific temporal context, on which we are interested in training a tailored model, e.g., if we are interested in a contextualised model for each of day of the week, \(\mathcal{D}_{\mathcal{CR}}\) is split in seven partitions (one for each day of the week).  
**Generation of transactional datasets.** Given the partitions \(\mathcal{P}_{i}\), a transactional dataset \(\mathcal{TR}_{i}\) that encodes the critical stations in each timestamp \(t\) is built from \(\mathcal{P}_{i}\). Each transaction \(tr_{t}\in\mathcal{TR}_{i}\) includes the set of stations that are positively or negatively critical at timestamp \(t\) and their status (positive or negative).  
Rule extraction.Finally, the association rules are mined from each transactional dataset \(\mathcal{TR}_{i}\) to extract for each context the set of frequent patterns \(\mathcal{R}_{i}\) representing recurrent critical situations among nearby stations.  
The set of extracted association rules represents the training model that is used to plan online rebalancing actions using the approach described in Section 4.2.  
In the following subsections we provide more details about the contextualised data partition step and the rule mining one, which are the building blocks of the proposed methodology.
# Dynamic Shortest Paths Methods for the Time-Dependent TSP
## 3 A Branch-and-Price Algorithm
### Column Generation
#### 3.3.1 Lagrangean Pricing & Dual Stabilization  
It is fairly straightforward to derive a Lagrangean relaxation of the LP-relaxation of (TDTSP-P), which is solved during the pricing problem. The constraint \(\sum_{P\in\mathcal{P}}\alpha_{s,P}\cdot x_{P}=1\) is equivalent to  
\[\sum_{P\in\mathcal{P}}x_{P}=1,\]  
since every path in \(\mathcal{P}\) leaves \(s_{0}\) exactly once. By penalizing all covering constraints in the objective and removing all but this constraint, we obtain the Lagrangean relaxation  
\[L_{\mathcal{P}}(\lambda)\coloneqq\left(\sum_{v\in V}\lambda_{v}\right)+\left\{ \begin{aligned} \underset{x}{\text{min}}&\sum_{P\in\mathcal{P}} \overline{c}_{P}\cdot x_{P}\\ \text{s.t.}&\sum_{P\in\mathcal{P}}x_{P}\leq 1\\ & x_{P}\geq 0\quad\forall\,P\in\mathcal{P}.\end{aligned}\right.\]If there exists a path with negative reduced costs with respect to \(\lambda\), an optimal solution of \(L_{\mathcal{P}}(\lambda)\) is obtained by setting \(x_{p}^{*}=1\), where \(P\in\mathcal{P}\) has the minimum reduced cost. If there is no such path, we set \(x^{*}\equiv 0\). In any case, we obtain a lower bound on the LP-relaxation of (TDTSP-P) during the pricing. It is easy to adapt this approach to (TDTSP-A), enabling us to use path-based pricing in this case as well.  
We use the Lagrangean relaxation in order to perform a dual stabilization based on the weighted Dantzig-Wolfe decomposition method introduced in [22]. The weighted Dantzig-Wolfe decomposition method judges the quality of dual values according to the value of their Lagrangean relaxation. By maintaining a center of stability and only tentatively moving the center towards the current dual values, the technique can significantly decrease the time required to solve the LP-relaxations of the TDTSP formulations.
# Utilizing Duplicate Announcements for BGP Anomaly Detection
## 2 Related Work
### Duplicate Announcements
#### 2.2.1 Effect of Duplicate Announcements  
Duplicate announcements can affect the posture of the internet in a certain fashion. It can be measured to analyze the health of the internet. Initially, it was considered that this announcement would not considerably obstruct the routing mechanism. However, studies revealed that this announcement is liable for increased processing load over most of the routers in the network when the traffic is very busy [33]. When the processing load is increased on the router, then it can cause session resets. It can also lead to the creation of routing loops, which are required to be avoided; packet losses also increase due to it. The measurement is taken on the contribution of duplicates over the processing loads of the router in their busiest times. The 'busiest times' is defined as the 0.01% of seconds when the maximum number of updates were created.  
The negative effects that duplicate announcements can create on the overall posture of the internet are well understood. The creation of duplicate messages is explored comprehensively to get a better analysis of the routing path. The reason for duplicate announcement generation is also discussed. The spreading of duplicate announcements is not only measured by the structure of the internet nowadays but also by the number of duplicates, which have been observed in recent years. The topological vectors, like the shape and structure of the AS and the internal and external connectivity among the peers of the BGP, exhibit correlation with the eventuates of the duplicates.  
Duplicate message generation is just not a psychiatric behavior that is observed on the internet. In March 2009, the number of duplicate announcements was seen along with the total number of updates by every 90 monitors [34]. The collected number of total routings BGP update packets was nearly 67.7 cores, among which the received duplicate updates were 9.1 cores. The duplicate announcements contribute 13.44% of the total collected traffic of the BGP. Various monitors have observed the time interval up to which the duplicate announcements have resided from March 2002 to 2009. The duplicate announcements have undergone inspection for various cases. However, a router following the BGP update must not feed a duplicate message as it obtains the message. Hence, when a duplicate message is obtained at any AS, for example, A. Let this message follow the route of A-B-C. Here, C denotes the origin AS from where the message originated, and the generation of the duplicate message is performed by A. It is not generated by B or C.