esa-sceva/satcom-chunk-collection huggingface dataset:
# SD-BROV: An Enhanced BGP Hijacking Protection with Route Validation in Software-Defined eXchange
## 2 Background and Related Work  
In global networks, AS is the fundamental element managed by the Internet Service Provider (ISP), and BGP is a widely used protocol to exchange routes among ASes. In the route exchange process (so-called peering), each AS has to use recognized ASN to announce its responsible IP prefixes. To advertise IP prefixes between two ASes, routers have to establish BGP session and send advertising messages first. On the next step, by collecting received advertisements, routers are able to calculate the route weight and update their forwarding tables. After the above steps, routers are capable of knowing where to forward the network traffic according to the destination IP address in the end.  
Due to the complexity of global network architecture, it is common to find multiple AS paths to reach a specific destination IP prefix. However, because the advertised routing information is spreading among numbered peering routers, it is hard to guarantee whether the advertisement is reliable or not. For example, a misconfigured advertisement might simply cause the unreachable issue to affected BGP neighbors. Therefore, making sure received routes are coming from trusted sources is very important.
## 2 Results and Discussion  
Free radical polymerization and copolymerization of allyl acrylate (AA) with alkyl (meth)acrylates and styrene leads to a cross-linked polymer even at low conversion. This result is the consequence of the structure of AA with two reactive double bonds; a highly reactive C,C double bond in the acrylate - and a less reactive C,C double bond in the allyl group. In contrast, polymerization of AA and copolymerization of AA with alkyl acrylates using ATRP [16] offers the possibility to obtain soluble copolymers suitable for cross-linking in a later stage. The control over the polymerization does prevent the formation of an extended cross-linked network by the creation of more uniform chains [17].  
Copolymerization of _tert_.butyl acrylate and allyl acrylate via ATRP was chosen because the method allows synthesizing polymers containing _tert_.butyl and allylic side groups in the same molecule. By removing the _tert_.butyl group, a highly hydrophilic copolymer - poly(acrylic acid-_co_-allyl acrylate) - is obtained with the potential of cross-linking due to the C,C double bonds in the allyl group. This copolymer cannot be obtained by direct copolymerization of acrylic acid with allyl acrylate because the acid monomers poison the ATRP catalyst by coordination of the transition metal or by protonation of the nitrogen atoms of the ligand [18].  
In the first step the commercially available acrylic monomers were copolymerized in a controlled manner using ATRP (Scheme 1). The results are summarized in Table 2 (cf. experimental part).  
In the first step, two linear precursors of poly(_tert_-butyl acrylate-_co_-allyl acrylate) with different ratio of the repeating units were successfully prepared: _t_BuA/AA: (95/5) and (90/10). The reproducibility of the reaction was proven by three-fold repetition of the experiment.  
\({}^{1}\)H-NMR analysis shows all expected signals: for the allylic groups at \(\delta\) = 4.55 (-CH\({}_{2}\)-CH=CH\({}_{2}\)-), 5.23 (-CH\({}_{2}\)-CH=CH\({}_{2}\)) and 5.91 ppm (-CH\({}_{2}\)-CH=CH\({}_{2}\)-) and for the _tert_-butoxy- group (-OC(CH\({}_{3}\))\({}_{3}\) ) at \(\delta\)= 1.27 ppm. GPC analysis indicates that the copolymer has an average molecular weight of _circa_ 5000 g/mol which is in very good agreement with the theoretical value. In addition a low polydispersity index (PDI = 1.23) was observed as expected for a controlled radical polymerization. Hydrolysis of the _tert_.butyl ester groups was performed in CH\({}_{2}\)Cl\({}_{2}\) in presence of trifluoracetic acid (TFA) at room temperature for 24 h. \({}^{1}\)H-NMR analysis clearly indicates that the signal derived from the _tert_-butoxy groups at \(\delta\)= 1.27 ppm vanished and therefore the hydrolysis is quantitative. Moreover,during hydrolysis the copolymer becomes insoluble and precipitates spontaneously, allowing an easy isolation of the product as a white powder.  
**Scheme 1.** A Synthesis of poly(acrylic acid-_co_-allyacrylate), the linear precursor for the cross-linked hydrophilic polymer film.
###### Abstract
(3) It fails to fully utilize the semantic information of relationships. These relationships are often mapped to an ID and cannot establish intrinsic connections with the context and relevant entities, making it difficult to capture fine-grained semantic information between relationships. Inspired by ref. [15], in this paper, we propose a single-table-filling framework model, which no longer maintains multiple relational tables but instead uses a shared representation to express the relationship between entities. Specifically, the relationships are first extracted as words with a special meaning. These relationship words are then concatenated with the original text and transformed into word embeddings, which are input into  
Figure 1: Overall cases of normal, EPO, SEO, and SOO overlapping triples. Different colors represent different entities in the case sentence. the BERT [16] language model for uniform encoding. Unlike the table-filling approach in ref. [15], we rely on the entity span information within the relationship labels. This means that we construct continuous subject-relation pair labels and object-relation pair labels to identify the spans of subject and object entities, thereby addressing the problem of complex triple overlap. To capture more global information, BiGRU [17] is employed to extract additional hidden layer information, and a multi-head tensor dot-product operation is employed to encode table features. These encoded features are subsequently fused with the output of the self-attention mechanism within the transformer [18] to iteratively acquire the intermediate encoding of table cells. Finally, the sigmoid function is employed to obtain the probability table for the final result. To address the extreme imbalance of positive and negative samples, as well as difficult samples, we employ the focal loss [19] as a loss function to replace the traditional binary cross-entropy, as it exhibits better training performance in situations with imbalanced sample difficulty and positive-negative sample distribution. The main contributions of this work are as follows:  
1. We propose a novel table-filling scheme, which can extract entities with multiple relations and tokens end to end. Even when applied to a simple network architecture, it achieves high accuracy. 2. We propose a new framework model that combines the attention mechanism inside the transformer with the multi-head tensor dot-product results of sentence representations, enriching the feature vectors of the table and improving accuracy compared to extracting results using only the attention mechanism. 3. We apply the focal loss function to entity relation extraction table-filling methods. To the best of our knowledge, most current relation extraction models based on table-filling methods use cross-entropy as the loss function for training. 4. We evaluate the proposed model on two public datasets, NYT [20] and WebNLG [21], and select classic models from the past three years as baselines. Our model achieves the best accuracy on the NYT dataset and demonstrates higher training efficiency.
## 2 Proposed Enhanced PNCC Algorithm
### Noise Suppression
#### 2.2.2 Channel Bias Minimizing  
Most of the speech information values are concentrated at low and medium frequencies, while the noise spectrum values distribution depends on each type of noise. After gammatone filtering, the PSD values for of each noisy speech frame is smoothed according to each gammatone filter function. However, the large-time power averaging filter will cause smoothing of the power values of each consequent gammatone channel, but differently. Since the energy values of additive noise change are faster than the speed energies over the frames, the smoothing effect will cause spreading of the noise energies more than the speech energies along each gammatone channel. Therefore, it will produce a channel bias. This bias depends on the spectral noise distribution more than the speech spectral distribution. Since the noise PSD is usually not uniform, specifically in the case of environmental noise, the bias value varies from each gammatone channel to another. In Equation (4), the channel bias effect is minimized by subtracting channel values from the minimum number within each channel, multiplied by the constant bias factor \(d\) where \(0\;<\;d\;<\;1\). This constant bias factor depends on the value of the coefficient. It was also chosen experimentally, and its value is equal to 0.6.  
\[\widetilde{Q}[l]=Q[l]-d\times min(Q[m,\;l]) \tag{4}\]
# Phytomicrobiones: A Potential Approach for Sustainable Pesticide Biodegradation
## 3 Phytomicrobiome and Pesticide Biodegradation
### 3.1. Shoot Microbiome  
Shoot and stem microbiomes are hubs of versatile microbial communities akin to their rhizospheric counterparts, yet uniquely adapted to the host plant species, soil type, and environmental milieu in which they thrive. These microbiomes transcend their role in enhancing plant growth; they emerge as potent allies in the realm of biodegradation, venturing into the deconstruction of various foreign biochemicals, including pesticides. Highlighting the promising potential within these microbiomes, [PERSON] et al. (2021) introduced a novel endophytic _Bacillus_ strain isolated from cucumber stalks [96]. This remarkable bacterium swiftly dismantled 8.03 umol/L of pentachlorophenol (PCP) within a mere 24 h, subsequently generating PCP-phosphate as a metabolite [96]. Furthermore, in the quest for bioagents capable of pesticide degradation, two exceptional endophytic bacterial strains, _Acinetobacter_ spp. TW and _Sphingomonas_ spp. TY, were uncovered within the annals of tobacco plant wastes [45]. _Acinetobacter_ spp. TW, thriving at temperatures between 25 and 37 \({}^{\circ}\)C with a pH range of 7-8, displayed an astounding ability to entirely degrade nicotine. Simultaneously, _Sphingomonas_ spp. TY, accustomed to conditions spanning 25-30 \({}^{\circ}\)C with a pH range of 6-7, demonstrated an equivalent prowess in nicotine degradation. Remarkably, these two strains unveiled their versatility in degrading neonicotinoid pesticides such as acetamipid, imidalcoprid, and thiamethoxam. Similarly, the endophytic microbial consortia can be a perfect tool for harnessing pesticide degraders. For instance, the bacterial consortia of _Achromobacter xylosoxidans_ (BD1), _Achromobacter pulmonis_ (BA2), _and Oxtrobactrum intermedium_ (BM2) was proven to be effective in bispyribac sodium (a common herbicide) degradation [61]. The shoot and stem microbiomes, with their unique microbial inhabitants, stand as promising reservoirs of pesticide-degrading capabilities, offering a pathway to sustainable pest management within the realm of agriculture and beyond.
# Towards Single 2D Image-Level Self-Supervision for 3D Human Pose and Shape Estimation
## 3 Method
### Network Architectures
#### 3.2.2 Pre-Training Stage  
The aim of this stage was to train five different networks (\(f^{\text{jigsaw}}\), \(f^{\text{Rot}}\), \(f^{\text{Imp}}\), \(f^{\text{Disc}}\), and \(f^{\text{Tex}}\)) that were used to constitute the losses in the subsequent \"mesh training\" stage. We trained the networks by running \(T_{1}=10\) epochs of training with the Adam optimizer on the losses \(L_{\text{jigsaw}}\), \(L_{\text{rot}}\), \(L_{\text{imp}}\), \(L_{\text{adv}}\) and \(L_{\text{RGB}}\) (Equations (2)-(6)) with learning rates of 0.01, \(5\times 10^{-4}\), \(1\times 10^{-4}\), and \(1\times 10^{-4}\), and 0.01, respectively. **Jigsaw puzzle loss \(L_{\text{jigsaw}}\)**. As in [19], the jigsaw puzzling task could be formulated as the standard classification task. We applied the cross-entropy loss \(L_{\text{jigsaw}}\) for the jigsaw puzzle solver \(f^{\text{jigsaw}}\) to make its output close to the permutation order:  
\[L_{\text{jigsaw}}(f^{\text{jigsaw}}|\mathbf{x},\mathbf{x}_{\text{jigsaw}})=- \sum_{c=1}^{C}y_{c,\text{jigsaw}}\log(\hat{\mathbf{a}}_{c}) \tag{2}\]  
where \(y_{c,\text{jigsaw}}\) is the \(c\)-th dimensional element of the one-hot encoded vector from the permutation ground-truth, and \(\hat{\mathbf{a}}_{c}\) is the \(c\)-th dimensional response of the \(f^{\text{Jigsaw}}\) network's output. **Rotation prediction loss \(L_{\text{rot}}\). ** The rotation prediction could be also formulated as the standard classification task as in [20]. Images were rotated with four possible angles \(\theta\), 0, 90, 180, and 270, and then mapped to the classification label set \(\{1,2,3,4\}\). To achieve this, the cross-entropy loss \(L_{\text{rot}}\) was applied on the output of the rotation predictor \(f^{\text{Rot}}\):  
\[L_{\text{rot}}(f^{\text{Rot}}|\mathbf{x}_{\text{rot}})=-\sum_{c=1}^{4}y_{c, \text{rot}}\log(\hat{\mathbf{r}}_{c}) \tag{3}\]  
where \(y_{c,\text{rot}}\) is the \(c\)-th dimensional element of the one-hot encoded vector from the rotation ground-truth, and \(\hat{\mathbf{r}}_{c}\) is the \(c\)-th dimensional response of the \(f^{\text{Rot}}\) network's output. ```
Input: -Training data \(D=[D_{\text{H36M}},D_{\text{COCO}},D_{\text{MPII}},D_{\text{LSP}},D_{\text{Youtube}},D_{ \text{MPI-INF-3 DHF}}]\) whose size is \(N\). -RGB image of training data; -Hyper-parameters: number \(T_{1}\), \(T_{2}\) of epochs, size \(N^{\prime}\) of mini-batch; Output: -Jigsaw puzzle solver output \(\hat{\mathbf{o}}_{c}\); -Rotation predictor output \(\hat{\mathbf{r}}_{c}\); -Part in-painter output \(\hat{\mathbf{p}}\); -Rendered RGB image \(\hat{\mathbf{x}}_{\text{render}}\). Initialization: -Pre-train \(f^{\text{Feat}}\), \(f^{\text{Mesh}}\) based on [5]; -Initialize \(f^{\text{c-cnn}}\) from [58]; -Implement \(f^{\text{NMR}}\) from [22]; -Randomize (parameters) of \(f^{\text{Jigsaw}}\), \(f^{\text{Rot}}\), \(f^{\text{Imp}}\), \(f^{\text{Disc}}\), \(f^{\text{Tex}}\).
# Automated Tunnel Point Cloud Segmentation and Extraction Method
## 5 Conclusions  
(1) For the raw point cloud data collected by 3D laser scanning, the proposed GuSAC algorithm can be used to segment and fit the tunnel in long, complex tunnels, achieving overall automatic denoising of the tunnel point cloud. The output data clearly show the boundary features between different lining segments.  
(2) For the denoised point cloud processed by GuSAC, the TMatch algorithm, based on clustering algorithms and Gaussian Mixture Models, directly performs inter-ring and intra-ring segmentation of the tunnel. The segmentation accuracy for regular tunnels can reach 95%.  
(3) A cross-section extraction method independent of the centerline is designed. By fitting the tunnel's central plane using SVD-based least squares, the extracted cross-section data have a fixed position, allowing for a more accurate reflection of tunnel deformation.  
In summary, the method proposed in this paper is not only theoretically innovative but also demonstrates significant effectiveness in practical applications. It provides a new technological solution for the safety monitoring and maintenance of tunnel structures, which is of great significance in enhancing the automation and intelligence of tunnel engineering. In the future, we will continue to enhance the capabilities of tunnel automation processing by focusing on aspects such as controlling the tunnel point cloud acquisition density, improving curved section segmentation performance, and establishing error analysis standards.  
Conceptualization, Z.W., Y.W. and S.X.; Methodology, Z.Z., Y.W., Q.H. and S.X.; Software, Z.Z.; Validation, Q.H.; Writing--original draft, Z.Z.; Writing--review & editing, Z.Z., D.J. and J.F.; Supervision, D.J. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data is contained within the article.  
Author [PERSON] was employed by the company Zhejiang Huadong Surveying and Engineering Safety Technology Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 4 Experiments
### Experimental Results
The results demonstrate that accuracy steadily improves with longer training for nearly all models, reaffirming the compatibility of the augmented data with extended training. Notably, all models surpass 93% validation accuracy at 2000 epochs, with Transformer-based models consistently ranking at the top. Figure 6: Effectiveness of IK-based data augmentation across five deep learning architectures. (**a**) compares validation accuracy before and after augmentation, showing consistent improvements across all models. (**b**) presents the proportional contribution of each model to the total accuracy gain, highlighting that Transformer-based models (Transformer, Informer, Sparse Transformer) account for the majority of performance improvement. Figure 7: Comparison of IK-AUG versus 2D rotation-based augmentation. Validation accuracy of five deep learning models trained with traditional 2D random rotation and with the proposed IK-based augmentation. IK-AUG yields consistent performance gains across all models. Collectively, these findings validate that the IK-based augmentation not only yields immediate performance boosts, but also maintains its effectiveness over prolonged training periods, particularly when combined with Transformer-family architectures. To further validate the impact of IK-based augmentation on test-time performance, we conducted a comparative analysis across all models using the test set. As summarized in Table 2, all five architectures exhibit consistent improvements in final test accuracy after being trained on the augmented dataset. CNN3D and TCN achieve relative gains of +5% and +6%, respectively, while Transformer-based models Transformer, Informer, and Sparse Transformer show marginal but stable improvements of +2% to +3%, further confirming the compatibility of the synthesized gestures with generalization tasks. Additionally, Figure 9 presents the confusion matrices of the Transformer model trained with and without augmentation. The augmented model (right) demonstrates stronger diagonal dominance and reduced inter-class confusion compared to the baseline (left), highlighting the improved discriminative ability and classification robustness introduced by the synthetic diversity. Figure 8: Impact of training duration on the effectiveness of IK-based augmentation. (**a**) presents raw accuracy and augmentation gain for five models across 500, 1000, and 2000 training epochs, illustrating stable improvements with longer training. (**b**) shows the gain trends over epochs, where Transformer achieves the largest boost at 1000 epochs, while Informer and Sparse Transformer maintain consistent gains, indicating strong scalability of augmentation across model types. These results emphasize that the proposed augmentation framework not only boosts training efficiency and validation performance but also translates into measurable generalization gains on unseen data, making it a practical and scalable solution for gesture-based recognition systems.
# A Fitting Recognition Approach Combining Depth-Attention YOLOv5 and Prior Synthetic Dataset
## 2 Related Works
### YOLO Series  
YOLO is a regression method based on deep learning to directly obtain object position and class. YOLO performs prediction on an image and outputs all recognized results at once, without the process of candidate region extraction, so it is fast.  
YOLOv1 [22] learned the GoogleNet classification network structure, including 24 convolutional layers and 2 fully connected layers. Although the detection speed of YOLOv1 was fast, the localization accuracy and the recall rate were low. YOLOv2 [23] improved on YOLOv1 using a series of optimization strategies (multi-scale training, a new network, Darknet-19, and added anchor) to increase accuracy while maintaining the original speed. YOLOv3 [24] improved YOLOv2 by using a new network structure, Darknet-53, fusing FPN, and using logistic regression instead of softmax as a classifier. The neck structure of YOLOv4 [25] used the SPP modules, and the Mosaic data augmentation was used for the input part. YOLOv5 [41] added self-adapting anchor frames at the input, using the focus structure and CSP structure at the backbone and the FPN+PAN structure at the neck. The YOLOv2 [26] was to decouple the head based on the architecture of YOLOv3. The anchor-free and the SimOTA were also proposed. PP-YOLO [27] improved anchor-free and proposed the novel RepResBlock, ET-head, and dynamic label assignment algorithm TAL. The structure of YOLOv7 [28] was similar to YOLOv5, and the efficient aggregation networks ELAE(E-ELAN), the reparametrized convolutional (RepConv), and the auxiliary head detection were proposed.  
YOLOv5 has been updated to v6.1 version; its accuracy, functionality, and robustness have greatly improved over the first version. Algorithms after YOLOv5, such as YOLOX, PP-YOLOE, and YOLOv7, have multiple heads in the head layer, and it is relatively easy to verify the YOLOv5 fusion depth-attention mechanism.
and the Endocrine Society recommend counseling and offering fertility preservation (FP) prior to undertaking gender affirmation hormone therapy (GAHT) [4, 5, 6]. However, the data show that few TGNB-AMAB patients undergo FP prior to starting GAHT or gender-affirming surgery despite a desire for biological parenthood and a high rate of decisional regret when not initially pursued [7, 8].  
Sperm cryopreservation remains the gold standard for FP among TGNB-AMAB patients [9]. Stored samples may be used for future assisted reproductive technologies (ARTs) including intrauterine insemination or in vitro fertilization with intracytoplasmic sperm injection if necessary [10]. Patients who have started GAHT prior to sperm cryopreservation often require the cessation of GAHT to improve semen parameters given the downregulatory effects of estrogen and anti-adrogens on the hypothalamic-pituitary-gonadal axis [11].  
Currently, there are no established guidelines for the duration of hormone cessation, and the results from small, retrospective studies show that changes in semen parameters may be irreversible [12]. Additionally, this interruption of or delay in hormonal therapy is associated with worsening gender dysphoria and is a contributing factor in the low adoption of FP in this population [13, 14]. Few prior studies have evaluated semen parameters for transgender women on active GAHT; however, among the available data, the rates of azoospermia are 42-50% [15, 16]. Therefore, it is critically important that TGNB-AMAB patients receive appropriate counseling and referral for FP early in the transition process.  
As part of the Gender Pathways Program (GPP) at Northwestern University--a multidisciplinary care model designed to meet the medical and surgical needs of TGNB patients--we developed a formalized referral process for the fertility preservation of sperm, oocytes, and embryos. We aimed to evaluate the effect of this formalized FP program on sperm cryopreservation since its inception in 2016. We hypothesized that referrals increased over time and that the rates of sperm cryopreservation improved with a formally instituted referral program.