esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background
### Dynamic Identity and SRUP  
When initially registering with the system (or when replacing a previous identity with a newly created one), devices are required to establish contact with a web application overSecure Hyper-Text Transfer Protocol (HTTPS)--to request an identity and to perform key exchange with the system. By using HTTPS, the device is able to positively determine the identity of the web service, and prevent a _man-in-the-middle_ attack versus the key exchange process. This may exploit public Certificate Authorities (CAs) for Internet-based resources (where the CA certificate is already present within the root of trust on the device); or by the a priori provision of a private CA certificate to the devices, for systems designed to operate on private networks. Using HTTPS also ensures that the traffic between the device and web application is encrypted against eavesdropping.  
This process generates a SRUP key-pair to be used by devices and the C2 server when communicating via SRUP messages. In all cases the sending device signs the message--enabling the receiver to positively determine that the message in question has originated from a valid source, and that it has not been tampered with (or otherwise modified) in transit. Additionally, for systems electing to use MQTT over TLS for message security, the service also issues the device with an TLS certificate and key, enabling the device to participate in encrypted communications with the MQTT broker. This approach also permits the broker to restrict the device's access to topics, other than those associated with the device itself--and thus prevents a malicious device connecting in order to eavesdrop on the message traffic at the broker.  
Within the SRUP protocol, identity revocation can be conducted either from the device (for example as a result of user interaction), or from the C2 server. Revocation from the device ensures that the old identity no longer exists, regardless of whether it remains registered within the C2 system:this has the advantage that it does not require consent from the C2 system, and enables the device to join a new (or rejoin the previous) C2 system under a new identity. Devices should send a _deregister request_ to inform the server that that identity is being revoked--but this is not mandatory. Revocation from the C2 server will result in the device being sent a _deregister command_ message. On receipt of this the device should revert to an _unregistered_ state--since it will no longer be able to communicate with servers using the identity it currently holds. Within the protocol it is not possible for a third-party to cause the revocation of an identity.
## 6 Evaluation
### Effectiveness Evaluation (RQ1)  
We evaluated SqliGPT, LLM4 Sqli-Sqlmap, and six other state-of-the-art scanners on SqliMicroBenchmark. The experimental results are shown in Table 2. **SqliGPT successfully detected all 45 targets in the SqliMicroBenchmark, and its performance across the three LLMs was consistent and significantly better than that of the other scanners.**  
PentestGPT detected only 18 of the basic difficulty targets and 4 of the advanced difficulty targets. Upon analyzing the test logs of PentestGPT, we found that it focuses more on the overall planning of the penetration testing process and does not sufficiently analyze the target's information. For example, it tends to generate uniform Sqlmap commands that do not take into account the target parameter delivery method (POST or GET): _sqlmap -u <url> -dbs._ Therefore, PentestGPT's performance is consistent across the three LLMs despite their differences in capabilities. This suggests that within PentestGPT's framework, the main focus of LLMs is on the penetration testing process, with insufficient attention to capturing detailed information. SQIRL detected 20 basic difficulty targets and 2 advanced difficulty target. We believe its weaker performance is mainly due to the poor transferability of its SQLI detection capabilities, which were learned in the original training set. ZAP detected 20 basic difficulty targets and 4 advanced difficulty targets.  
The results reported for BurpSuite and Arachni indicate that both tools detected 7 targets of advanced difficulty despite only identifying 21 and 23 targets of basic difficulty, respectively. This discrepancy arises because, even though some input parameters were filtered in the SqliMicroBenchmark's targets, database error messages continued tobe echoed in the HTTP responses. BurpSuite and Arachni employed relatively lenient criteria for determining injection points. These tools inferred vulnerability to injection when they detected database error information in the content of a target's HTTP response. However, neither BurpSuite nor Arachni actually succeeded in bypassing defenses and executing the injection statement effectively. Consequently, we disregarded these targets and acknowledged that only 4 of the advanced difficulty targets were successfully detected by each tool.  
Sqlmap successfully detected 27 targets of basic difficulty and 4 of advanced difficulty. These results demonstrate that SqliGPT leverages the capabilities of Sqlmap and enhances its performance. In contrast, the subpar results of PentestGPT in SQLI black-box detection with Sqlmap can be ascribed to its broader focus on the overall penetration testing process, which results in a relative deficiency in pinpointing specific vulnerabilities.
# Information Consumer Experience: A Systematic Review
## 5 Answering Research Questions
### What Methods Are Used for ICX Evaluation?
\begin{table}
\begin{tabular}{l l l} \hline
**Type** & **Method** & **Studies** \\ \hline Interview & Semi-structured Interviews & [1; 3; 14; 33; 37; 46; 52; 78; 81; 90; 95; 96; 111; 112; 126] \\ \hline Questionnaire & Adapted Scaled Questionnaire & [13; 16; 28; 30; 42; 47; 50; 53; 57; 63; 68; 72; 74; 76; 82; 84; 86; 87] \\  & SSTQUAL Questionnaire & [121] \\  & ACRL Questionnaire & [27] \\  & Quality of Work Life Scale Questionnaire & [60] \\  & WEIS-SR Questionnaire & [118] \\  & Job Satisfaction Survey & [59; 60] \\  & Intra-Company Crowdsourcing Questionnaire & [9] \\ \hline Conceptual & Customer Journey Map (CJM) & [25] \\  & Social Network Analysis & [68; 71; 96; 133] \\ Modelling & Grounded Theory & [103; 134; 135] \\  & Ontological methods & [14; 71] \\ \hline Statistical & Information Processing Support Index (IPSI) & [136] \\ Modelling & Statistical Analysis (Regression, PLS, SEM, LGM) & [13; 16; 27; 28; 30; 42; 47; 50; 53; 56; 59; 63; 68; 72; 74; 76; 82; 84; 87; 90; 101; 118; 121; 124; 125; 137; 138] \\ \hline \end{tabular}
\end{table} 
Table 6: Methods used to address ICX-related problems.
###### Abstract
In contrast, in PC the methylated ammonium group does not allow the formation of such close PO--N contact and thus PC does not form intermolecular, lipid-lipid hydrogen bonds. In PC, water molecules of hydration replace the ammonium links of PE and are thus incorporated into the headgroup lattice to form hydrogen bonds with the phosphate oxygens linking phosphate groups into ribbons and shielding groups of an equal charge. In other words, water molecules form water bridges, with each molecule being simultaneously bonded to the phosphate  
Figure 1: Chemical structures of DPPE and DMPC lipids are reported together with a schematic illustration of a fully hydrated bilayer structure. Functional groups that are H-bond donors (acceptors) are colored in yellow (violet). oxygen atom of two lipid molecules. Thus, PC bilayers have been described as an extended network of inter-lipid links via water bridges and charge pairs [10]; in contrast with the rigid PE lattice, the PC lattice exhibits considerable flexibility [8]. A second main difference between PC and PE lipids involves hydration interactions. In PE, water can be strongly hydrogen bound to the \(-\)NH\({}_{3}^{+}\) group, as well as to the phosphate group and ester oxygens. On the other hand, in PC water molecules associate with the choline group via polar interactions, which are weaker than the direct hydrogen-bonding interactions that occur with the phosphate oxygens and the ester oxygens [4]. Additionally, it has been found that the solvent structure around PC headgroups is significantly different from that around PE headgroups. In the former case, the formation of clathrates in the water hydrating the PC headgroup is observed, while in the latter the structure is determined by hydrogen-bonding interactions in the environment of the PE headgroup [11]. Other fundamental differences between the PC-based and PE-based lipid bilayers concern (i) the space requirements--e.g., the area per lipid and the interlamellar solvent distance, which are both larger in PC membranes; (ii) the degree of hydration of the headgroup, which is lower for smaller PE headgroups; and (iii) the gel to liquid crystalline temperature, which is higher for PE headgroups [11]. Here, we report the results of an experimental study of the far-infrared region on DMPC and DPPE stack bilayers (1,2-dipalmitoyl-sn-glycero-3-phosphoethanolamine, C\({}_{37}\)H\({}_{74}\)NO\({}_{8}\)P) at various hydrations, with the aim of highlighting the spectral differences in the THz frequency range of lipid membranes that differ with respect to their intermolecular interaction modalities. Measurements carried out at different hydration degrees were aimed at revealing if and how the intrinsic THz dynamics of the two investigated phospholipid membranes, deducible from the dry sample, may be differently affected by, and coupled to, the vibration excitations of their hydration water.
## 4 Conclusions  
This study developed an ARRS for improving operational efficiency and reducing human errors in the processing of receipt information for tax declaration tasks. In this system, a receipt is scanned into a high-resolution image, and the characters on the receipt are automatically classified into two categories according to the characteristics of the receipt characters: printed and handwritten receipts. Images of receipts with two types of characters are preprocessed separately. For handwritten characters, template matching and the fixed features of the receipt are used for text positioning, and a projection is for character segmentation. Finally, a CNN is used to achieve character recognition. For printed characters, the proposed YOLOv4-s model executes precise text positioning for character recognition. The proposed YOLOv4-s reduces downsampling features, thereby enhancing small-object recognition. The proposed system also provides a human-machine interface to enable users to review the results and perform error correction. The effectiveness of the proposed ARRS was tested experimentally. The experimental results reveal that the system had a recognition accuracy rate of 80.93% for handwritten receipt characters. Moreover, the recognition accuracy of the proposed YOLOv4-s model for printed receipt characters was 99.39%, and only 33 characters were misjudged. The recognition accuracy of the YOLOv4-s model was higher than that of the traditional YOLOv4 model by 20.57%.  
The proposed ARRS's recognition accuracy for handwritten receipts was approximately 80%, which is not up to the industry standard. On the other hand, ARRS runs on a personal computer device, which causes a lot of inconveniences for users to carry-outs. In future research, we will focus on collecting more handwritten receipts and improving the recognition accuracy of the system. In addition, we will consider using ISO GUM as an indicator to evaluate the experimental results. ARRS will also be implemented in FPGA embedded systems to obtain faster recognition speed and improve its portability.  
Conceptualization, C.-J.L. and Y.-C.L.; Methodology, C.-J.L., Y.-C.L. and C.-L.L.; Software, C.-J.L. and Y.-C.L.; Data Curation, C.-J.L. and Y.-C.L.; Writing--Original Draft Preparation, C.-J.L.; Writing--Review and Editing, C.-J.L. and C.-L.L.; Supervision, C.-J.L.; Funding Acquisition, C.-J.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Science and Technology of the Republic of China, grant number MOST 110-2634-F-009 -024 and MOST 109-2218-E-005-002.  
Data Availability Statement: Data sharing not applicable.  
The authors declare no conflict of interest.
## 4 Performance Results and Discussion
### Evaluation of the Results Regarding the Smart Sensor Use Case
The results of the measurements show, on the one hand, an increased reliability of the communication using the redundant paths. On the other side, the setups used show the different needs for more hardware and spectral resources when using various configurations. Whereas TSN networks typically incorporate multiple switches and redundant paths, 5G networks offer limited redundancy between the end device and the core network. Therefore, the use of FRER creates different setups that necessitate additional hardware. Thus, the choice of network architecture and level of redundancy directly influences the hardware needed and, therefore, the costs for the infrastructure.
# Thermo-Responsive Shape-Memory Effect and Surface Features in Polycarbonate (PC)
## 3 Characterization of Heating-Responsive SME  
A standard thermo-responsive SME cycle, programmed by means of, for instance, uni-axial tension in this study, includes two processes, namely programming and shape recovery, with three major steps as illustrated in Figure 5 [28].  
Figure 4: Dimensions of dog-bone shaped sample (according to ASTM D638, Type IV).  
Figure 5: Illustration of stress versus strain relationship in a standard shape memory effect (SME) cycle. The numbers inside of the circles are meant for the steps.  
Figure 3: Typical differential scanning calorimetric (DSC) result (the second cycle).  
**Step 1** Loading: at a prescribed programming temperature, stretching the sample to the required maximum strain of \(\varepsilon_{m}\) at a prescribed strain rate;  
**Step 2** Unloading: after cooling the sample back to the room temperature (if the programming temperature is above the room temperature), the sample is unloaded at the same strain rate as that in loading. For materials without significant relaxation/creeping at room temperature, as observed in this PC, the residual strain after unloading may be defined as \(\varepsilon_{1}\). This ends the first process of programming;  
**Step 3** Heating: the programmed sample is heated and the remaining strain after heating is defined as \(\varepsilon_{2}\). This ends the second process of shape recovery.  
The actual heating recovery strain can be estimated as \((\varepsilon_{1}-\varepsilon_{2})\), and hence, the shape fixity ratio (\(R_{f}\)) and shape recovery ratio (\(R_{r}\)) may be defined as [19],  
\[R_{f}=\frac{\varepsilon_{1}}{\varepsilon_{m}} \tag{1}\]  
\[R_{r}=\frac{\varepsilon_{1}-\varepsilon_{2}}{\varepsilon_{1}} \tag{2}\]  
A series of tensile tests were conducted on dog-bone shaped samples at different programming temperatures to different maximum programming strains using an Instron 5565 (with a temperature controllable chamber). To be more precise, the actual temperature during testing was monitored using a thermal couple attached to the tested sample. A constant strain rate of \(10^{-3}/\)s was applied in both loading and unloading in all tensile tests in the course of this study.  
Unless otherwise stated, the stress and strain mentioned here are meant for engineering stress and engineering strain.  
Pre-stretching was conducted in two different manners, one is at room temperature (about 25 \({}^{\circ}\)C) for tests with the maximum programming strains of 5%, 10%, 15%, 20% and 25%, respectively, and the other is at high temperatures (around the glass transition range) for three maximum programming strains of 20%, 30% and 100%, respectively. After programming, thermally (heating) induced shape recovery test was conducted at 180 \({}^{\circ}\)C for samples programmed at room temperature, or at previous programming temperature and then at 180 \({}^{\circ}\)C for samples programmed at high temperatures. Since the applied maximum programming strain is 5% or more, thermal strain is relatively smaller and thus, it is ignored herein.
# Automating Visual Blockage Classification of Culverts with Deep Learning
## 3 Dataset
### Labeling Criteria  
Dataset was manually labeled for binary classification of a given image with culvert as \"clear\" or \"blocked\". A culvert being visually blocked or clear is not as simple and may require defining detailed criteria in collaboration with flood management officers; however, for this article, simple occlusion based criteria was used. Following subjective annotation criteria was used for labeling.  
Figure 2: Sample instances of clear (First row) and blocked (Second row) culverts from VHD.  
If all of the culvert openings are visible, classify it as \"clear\";  
If any of the culvert openings is visually occluded by debris material or foreground object (e.g., debris control structure, vegetation, tree), classify it as \"blocked\".
## 4 Experimental Results
### Data-Set Specification  
This study makes use of the \"MS COCO\" [57] and \"Pascal VOC\" [2] datasets. Generally, object detection, image classification, and segmentation are performed with these two datasets. The \"Pascal VOC\" dataset consists of \"Pascal VOC 2007\" and \"Pascal VOC 2012\". There are 8540 images of human beings from the \"Pascal VOC\" dataset used for this experiment. \"MS COCO\" is more challenging while \"Pascal VOC\" is easier to train. Generally, the performance on the MS COCO dataset of a method for object detection models is more inclined. There are 45,174 images of human beings used from the \"MS COCO-train2014\" dataset for accomplishing this study. Both datasets are split 80/20 for training and validation, respectively. The IOU (\"intersection over union\") is set 0.5 by default for both datasets while calculating mAP values. The \"INRIA\" dataset [6] (1208 images) is used to test the proposed ReSTiNet model's detection speed.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Computing Platform** & **Graphics Processor** & **Memory** \\ \hline \multirow{4}{*}{Tesla K80} & GK210 \(\times\) 2, & 12 GB \(\times\) 2, \\  & 2496 \(\times\) 2 shading units, & 384 bit \(\times\) 2, \\  & 208 \(\times\) 2 TUMs, & GDDRS, \\ \cline{1-1}  & 48 \(\times\) 2 ROPs & 240.6 GB/s \(\times\) 2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Configuration of Tesla K80.
## 3 Results and Discussion  
### Genetic Diversity of A. triphylla Genotypes  
As mentioned above, _A. triphylla_ has a wide range of morphological variations [15; 16]. Additionally, _A. triphylla_ is native to Japan and is classified into rheophytic and nonrheophytic types based on leaf morphology and anatomy [15]. The leaves of the genotypes NT, NG, and L are lancelate or cuneate, whereas those of BT and BG are elliptic or ovate. This shows that genotypes NT, NG, and L are of the rheophytic type (Figure 1a). Furthermore, these genotypes have been classified based on the presence of trichomes on the stem or the wavy phenotype (Figure 1a). This morphological diversity in _A. triphylla_ is likely due to the adaptation of plants to different natural environments [17].  
Figure 1: Phenotype (**a**) and phylogenetic analyses (**b**) of _A. triphylla_ genotypes based on the nucleotide sequences of the combined ITS2 + matK + psbA-trmH regions.  
DNA barcoding is a special tool for the rapid identification of species based on short and standard DNA fragments (400-800 bp) [18; 19]. Although the mitochondrial cytochrome oxidase I gene has been widely used for the identification of animal species and phylogenetic analysis [20], the chloroplast gene sequence (matK), the gene sequence of the noncoding region of chloroplast RNA (psbA-trnH), and the nuclear gene internal transcribed spacer (ITS) sequence are used in plant phylogeny research and to determine genetic diversity in species [21; 22; 23]. To investigate the genetic relationships between the _A. triphyllla_ genotypes, we collected and amplified the selected barcode regions using the ITS2, matK, and psbA-trH primers (Table S1, Supplementary Materials). The sequence lengths of ITS2, matK, and psbA-trnH were 257-259 bp, 601 bp, and 311-312 bp, respectively, as determined through amplification of template gDNA extracted from each sample using universal primers (Table 1). Based on the ITS2 region, the phylogenetic analysis indicated that genotypes BG, BT, NG, and NT were closely related species and shared close affinities with _A. verticillata_ var. hirsuta, whereas genotype L exhibited a more distant relationship from the other genotypes (Figure S1a). For the matK region, all genotypes, except for the NG genotype, exhibited close similarity to _A. triphyllla_ (Figure S1c). Moreover, the psbA-trnH sequences of all genotypes showed high similarity to _A. triphyllla_ (Figure S1c). Based on the trees generated using the regions ITS2 + matK + psbA-trnH (Figure 1b), all genotypes were grouped within the same clade as _A. triphyllla_ (90% bootstrap), indicating their close genetic relationships. Taken together, these results suggested that all collected genotypes constitute varieties of _A. triphyllla_ exhibiting morphological variations.
## 6 Discussion
### Applicability of the Crop Yield Forecasting System to Other Areas  
The presented system contains a module for automatic training of yield forecasting models. It includes the step of removing the correlated features, forward feature selection, and selection of the best prediction method. It requires only the inclusion of predictors, i.e., agro-meteorological and satellite data, and reference statistics on crop yields for administrative units. Therefore, the model can be easily adapted to other regions featuring similar crop growth conditions. Otherwise, a problem may occur related to a double growing season, as computation of the GDD in the current system assumes one main crop per year.
# New Multi-View Feature Learning Method for Accurate Antifungal Peptide Detection
## 2 Materials and Methods
### Classifiers  
To test the efficiency of our extracted features and identify the best classifier to build our model, we investigated eight different classifiers, most of which have been effectively used for similar studies [25]. These eight classifiers are support vector machine (SVM), logistic regression (LR), decision tree (DT), rotation forest (RT), stochastic gradient descent (SGD), AdaBoost, naive Bayes (NB), and random forest (RF).