esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of Remote Upgrade System for Data Processing Unit in Marine Engine Room Simulator
## 5 Testing and Applications  
### Firmware Upgrade Function Test  
To ensure the reliability and stability of the remote upgrade system, fifteen DPUs in the marine engine room simulator were selected for testing. Figure 8 is the Photo of the Data Processing Unit. We tested the DPU with firmware upgrades [35] in two ways. In normal upgrades, we select different sizes of firmware for the DPU. In abnormal upgrades, we first disconnected the communication module and the web server individually during the upgrade process to simulate a communication failure. Then, we manually downloaded the buggy firmware to simulate a human error. Table 2 shows the results of each DPU's upgrade test under different conditions.  
Figure 7: Flow chart of backup upgrade.  
When the network is disconnected, the DPU will seek to connect to the network every 30 s. If the network is disconnected for too long and the DPU is unable to access the web server, the upgrade will fail. This is the reason why DPU upgrades failed in Table 2. However, if it is connected to the network after a period of time, the DPU can restart the upgrade request to the web server and complete the remote upgrade. In summary, the remote upgrade system has a high upgrade success rate and will ensure that the DPU completes the firmware upgrade successfully.  
Table 3 shows the types of faults that can occur during the upgrade process and the handling mechanism of DPU. Upgrade faults are divided into data communication faults and human operation faults during the upgrade process [36]. Data communication faults include: web server errors during remote upgrades, sudden network interruptions, etc. When such types of failure occur, DPU will automatically upgrade the upgrade flag \"UpgradeState\" to 1 after timeout detection and Wait for communication reestablished to complete the firmware upgrade. Undetected buggy firmware deployments are common human issues in existing systems [37]. The buggy firmware is the wrong program that will prevent the DPU from working. It can be resolved by the backup upgrade.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Title** & \begin{tabular}{c} **Faults** \\ **Description** \\ \end{tabular} & **File Size (KB)** & **Success Times** & \
\begin{tabular}{c} **Success Rate** \\ **(\%)** \\ \end{tabular} \\ \hline \multirow{4}{*}{abnormal upgrades} & Network & \multirow{2}{*}{270} & \multirow{2}{*}{14} & \multirow{2}{*}{93} \\  & disconnection & & & \\ \cline{1-1} \cline{3-5}  &
\begin{tabular}{c} Web server \\ error \\ \end{tabular} & & & & \\ \cline{1-1}  & pgrade buggy & \multirow{2}{*}{270} & \multirow{2}{*}{15} & \multirow{2}{*}{100} \\  & firmware & & & \\ \hline \multirow{2}{*}{normal upgrade} & \multirow{2}{*}{40} & \multirow{2}{*}{15} & \multirow{2}{*}{100} \\  & & & & \\ \end{tabular}
\end{table}
Table 2: DPU upgrade testing results.  
Figure 8: Data Processing Unit.
# Correlation between Biogenic Amines and Their Precursors in Stored Chicken Meat
## 2 Materials and Methods
### Preparation of Samples for Biogenic Amines and Free Amino Acid Content Analysis  
Sample preparation and determination of BAs and AAs were conducted as described by [PERSON] et al. [4]. Two grams of the homogenized meat sample was weighed into a 50 mL centrifuge tube. This was then spiked with 50 \(\upmu\)L of the 1,7-diaminoheptane internal standard solution (1 mg \(\times\) L\({}^{-1}\)) and with 40 mL of 5% trichloroacetic acid. Subsequently, the sample was shaken and centrifuged at 10,000\(\times\)\(g\) for 10 min. The supernatant was then filtered using filter paper. For the derivatization process, 1 mL of distilled water, 1 mL of 5% borax solution, and 100 \(\upmu\)L of the sample supernatant were combined in a 15 mL polypropylene tube. Then, 2.5 mL of dansyl chloride (20 mM) dissolved in acetonitrile was added. The tube was thoroughly mixed and then placed in a shaking water bath, maintained at 30 \({}^{\circ}\)C for 1 h, ensuring it was kept away from light exposure. After this period, 125 \(\upmu\)L of a 400 mM ammonia solution was added to the tube, which was then allowed to sit in a dark environment for an additional 15 min. The mixture was finally passed through a 0.45 \(\upmu\)m syringe filter, and the filtrate was transferred to a chromatographic vial, ready for LC-MS analysis.  
BA levels were analyzed for putrescine, cadaverine, tyramine, tryptamine, histamine, agmatine, phenethylamine, spermine, and spermidine, as well as their precursors, which included arginine, lysine, histidine, tyrosine, Phenylethylalanine, Tryptophan, and ornithine. The following were also analyzed: BAI--biogenic amines index (sum of putrescine, cadaverine, tyramine, and histamine); TOTAL BA-1--sum of histamine, putrescine, cadaverine, tyramine, phenylethylamine, tryptamine, and agmatine; and TOTAL BA-2--sum of Total BA-1, spermine, and spermidine. For reasons of stability, spermine and spermidine were not included in TOTAL BA-2, but the TOTAL BA-2 index was distinguished. For amino acids, the index sum of precursors of BAI =sum of lysine, histidine, tyrosine, and ornithine was distinguished. All raw results have been included in the database available in Supplementary Materials link.
## 2 Materials and Methods
### EDC and NEEB  
The carbon and Nr footprints were assessed along with the EDC, NEB and NEEB of each treatment. The EDC caused from Nr and GHGs was calculated as follows [26]:  
\[\mathrm{EDC}=\mathrm{N_{ri}}A\times\mathrm{P_{i}}+\mathrm{CO_{2}}A\times\mathrm{ P_{CO2}} \tag{6}\]  
where \(\mathrm{N_{ri}}\)\(A\) (kg N ha\({}^{-1}\)) denotes the total emission of Nr \"\(i\)\"; \(\mathrm{P_{i}}\) (\(\mathrm{\Psi}\) kg\({}^{-1}\) N) denotes the EDC per unit mass of Nr \"\(i\)\"; \(\mathrm{CO_{2}}\)\(A\) (t CO\({}_{2}\)-eq ha\({}^{-1}\)) represents the total emission of GHGs; and \(\mathrm{P_{CO2}}\) (\(\mathrm{\Psi}\) t\({}^{-1}\) CO\({}_{2}\)-eq) represents the greenhouse effect caused by the CO\({}_{2}\) emission  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Comonents** & **Items** & **Unit** & **Coefficients** & **CK** & **SN** & **NB** & **NM** & **NMB** \\ \hline Chemical input & Urea & kg N & 8.30 & 0 & 1992 & 1992 & 996 & 996 \\ Organic fertilizer & kg N & 11.30 & 0 & 0 & 0 & 1356 & 1356 \\ Biochar & Kg C & 0 & 0 & 0 & 0 & 0 & 0 \\ Phosphorus & kg P\({}_{2}\)O\({}_{5}\) & 1.50 & 360 & 360 & 360 & 331 & 331 \\ Potassium & kg K\({}_{2}\)O & 0.98 & 235 & 235 & 235 & 169 & 169 \\ Plastic Film & kg & 19.00 & 699 & 699 & 699 & 699 & 699 \\ Pesticide & kg & 18.00 & 119 & 119 & 119 & 119 & 119 \\ \hline Farm operation & Fuel & kg & 3.94 & 376 & 376 & 376 & 376 & 376 \\ Electricity & kW-h & 0.95 & 269 & 269 & 269 & 269 & 269 \\ \hline Soil GHG & \(\mathrm{N_{2}O}\) & kg N & 273.00 a  & 101 & 1279 & 1132 & 929 & 1037 \\  & \(\delta\)SOC & kg C & - & 118 & 213 & 35,680 & 1038 & 38,829 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Carbon emissions (kg CO\({}_{2}\)-eq ha\({}^{-1}\) crop\({}^{-1}\)) and coefficients (kg CO\({}_{2}\)-eq unit\({}^{-1}\) input) under different strategies in intensive vegetable production.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Treatment** & **NECB** & **\(\delta\)SOC** & **Re** & **Harvest** & **CH4** & **Carbon** & **GPP** & **NPP** & **Yield** \\ \hline CK & 0.15 & 0.032 & 0.19 & 0.33 & \(-\)0.00044 & 0 & 0.67 & 0.35 & 16.35 \\ SN & 0.28 & 0.058 & 0.28 & 0.53 & \(-\)0.00030 & 0 & 1.09 & 0.57 & 24.17 \\ NB & 9.73 & 9.73 & 0.33 & 0.67 & \(-\)0.00038 & 9.34 & 1.39 & 0.73 & 29.78 \\ NM & 1.35 & 0.28 & 0.25 & 0.58 & 0.0016 & 0.98 & 1.20 & 0.62 & 27.89 \\ NMB & 10.59 & 10.59 & 0.27 & 0.51 & 0.0015 & 10.32 & 1.05 & 0.54 & 27.50 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Components of NECB and \(\delta\)SOC (t C ha\({}^{-1}\) crop\({}^{-1}\)) under different strategies in intensive vegetable production.  
(expressed by the international carbon trading price). The specific EDC are shown in Table 3, where the updated EDC per unit of GHG emissions is 103.7  
the differences between treatments was analyzed using SPSS 20.0 software (Chicago, IL, USA) (_p_ < 0.05), and multiple comparisons were made using the Duncan method.
## Appendix B Information Retrieval (IR)  
In this section, we describe how the BM25 re-weighted BM25 scoring function works using the following equation:  
\[s(Q,D)=\sum_{i=1}^{n}\frac{BM_{25}(q_{i},D)\cdot IDF(q_{i})\cdot f(q_{i},Q) \cdot(k_{Q}+1)}{f(q_{i},Q)+k_{Q}\cdot(1-b_{Q}+b_{Q}\cdot\frac{queryLen}{avgQurysLen})},\]\[BM_{25}(q_{i},D)=\frac{IDF(q_{i})\cdot f(q_{i},D)\cdot(k_{D}+1)}{f(q_{i},Q)+k_{D} \cdot(1-b_{D}+b_{D}\cdot\frac{docLen}{avgDocLen})},\]  
where \(Q=\{q_{1},q_{2},...,q_{n}\}\) is the query consisting of \(n\) query terms (each query term in our method is actually a n-gram); \(IDF(q_{i})\) the inverse document frequency of the query term \(q_{i}\); \(f(q_{i},Q)\) and \(f(q_{i},D)\) represents the frequency of query term \(q_{i}\) in the query \(Q\) and the document \(D\), respectively; \(k_{D}\), \(k_{Q}\), \(b_{D}\), and \(b_{Q}\) are hyper-parameters and their values in our experiments are summarized in Table 11; \(queryLen\) and \(docLen\) are the length in tokens of the current query \(Q\) and document \(D\), respectively; \(avgQueryLen\) and \(avgDocLen\) are the average length of all queries and documents.
# Smart Grid Data Management in a Heterogeneous Environment with a Hybrid Load Forecasting Model
## 5 Results and Discussion (Case Study 1)
### Case Study 1: Data Management
#### 5.1.3 Data Visualization
These three cities were chosen based on the population density, as they are considered the three largest cities in Iraq, and their geographical location is distinctive (north, center, and south of Iraq, respectively) with three different climatic conditions in temperatures, the amount of rainfall and the number of daylight hours in the day. Figure 10 shows the dynamic analysis of the monthly average rainfall, monthly average read value, and monthly average minimum temperature in Baghdad. The figure shows that rainfall is highest in January, February, and March. Read value does not show much fluctuation and remains almost constant for the last nine months, with the highest value recorded in August. Baghdad shows the highest minimum temperature in July and August and has the lowest minimum temperature in January and September, offset by a decrease in energy consumption rates. Figure 11 shows Basrah's dynamic analysis with the highest rainfall in January with no rainfall from May to September. It has the same trend of reading value as Baghdad, i.e.,  
Figure 10: Dynamic Analysis of The City of Baghdad. Figure 9: Dynamic Analysis of Rainfall Distribution and Minimum Temperature and Account Types Over Region and Months. uniform, and has the highest minimum temperature in August, with the lowest in January and September, which is also offset by a decrease in energy consumption rates. Figure 12 shows the dynamic analysis of Ninevch city (Mosul). Unlike Baghdad and Basrah, it has the highest rainfall in February, with no rainfall from June to September. Again, the read value trend remains the same for Mosul, i.e., uniform, and has the highest minimum temperature in August, with the lowest in January. The results reinforce the following understanding that the rate of power consumption is greatly affected by different climate conditions, where consumption rises as the temperature rises, and the power consumption decreases with lower temperatures recorded. It was also noticed that the power consumption increases gradually whenever there is a decrease in the rainfall rate. All of this strengthens the research hypotheses, as the researcher could predict power consumption rates by linking them with different climatic condition data.
# Joint Balanced Routing and Energy Harvesting Strategy for Maximizing Network Lifetime in WSNs
###### Abstract  
Nowadays, wireless sensor networks (WSNs) are becoming increasingly popular due to the wide variety of applications. The network can be utilized to collect and transmit numerous types of messages to a data sink in a many-to-one fashion. The WSNs usually contain sensors with low communication ability and limited battery power, and the battery replacement is difficult in WSNs for large amount embedded nodes, which indicates a balanced routing strategy is essential to be developed for an extensive operation lifecycle. To realize the goal, the research challenges require not only to minimize the energy consumption in each node but also to balance the whole WSNs traffic load. In this article, a Shortest Path Tree with Energy Balance Routing strategy (SPT-EBR) based on a forward awareness factor is proposed. In SPT-EBR, Two methods are presented including the power consumption and the energy harvesting schemes to select the forwarding node according to the awareness factors of link weight. First, the packet forwarding rate factor is considered in the power consumption scheme to update the link weight for the sensors with higher power consumption and mitigate the traffic load of hotspot nodes to achieve the energy balance network. With the assistance of the power consumption scheme, hotspot nodes can be transferred from the irregular location to the same intra-layer from the sink. Based on this feature, the energy harvesting scheme combines both the packet forwarding rate and the power charging rate factors together to update the link weight with a new battery charging rate factor for hotspot nodes. Finally, simulation results validate that both power consumption and energy harvesting schemes in SPT-EBR achieve better energy balance performance and save more charging power than the conventional shortest path algorithm and thus improve the overall network lifecycle.  
Wireless Sensor Network; Dijkstra routing algorithm; load balance; power consumption; energy harvesting +
Footnote †: journal: Accepted: 17 June 2019; Published: 18 June 2019
###### Abstract  
Gross primary productivity (GPP) represents total vegetation productivity and is crucial in regional or global carbon balance. The Northeast China (NEC), abundant in vegetation resources, has a relatively large vegetation productivity; however, under obvious climate change (especially warming), whether and how will the vegetation productivity and ecosystem function of this region changed in a long time period needs to be revealed. With the help of GPP products provided by the Global LAnd Surface Satellite (GLASS) program, this paper gives an overview of the regional feedback of vegetation productivity to the changing climate (including temperature, precipitation, and solar radiation) across the NEC from 1982 to 2015. Analyzing results show a slight positive response of vegetation productivities to warming across the NEC with an overall increasing trend of \(\mathrm{GPP_{GS}}\) (accumulated GPP within the growing season of each year) at 4.95 g C/m\({}^{2}\). yr\({}^{-2}\) over the last three decades. More specifically, the growth of crops, rather than forests, contributes more to the total increasing productivity, which is mainly induced by the agricultural technological progress as well as warming. As for GPP in forested area in the NEC, the slight increment of GPP\({}_{\mathrm{GS}}\) in northern, high-latitude forested region of the NEC was caused by warming, while non-significant variation of GPP\({}_{\mathrm{GS}}\) was found in southern, low-latitude forested region. In addition, an obvious greening trend, as reported in other regions, was also found in the NEC, but \(\mathrm{GPP_{GS}}\) of forests in southern NEC did not have significant variations, which indicated that vegetation productivity is not bound to increase simultaneously with greening, except for these high-latitude forested areas in the NEC. The regional feedback of vegetation productivity to climate change in the NEC can be an indicator for vegetations growing in higher latitudes in the future under continued climate change.  
vegetation productivity; Northeast China; climate change; spatiotemporal distribution +
Footnote †: journal: Remote Sensing  
1  
[PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Spatiotemporal Productivity and Its Feedback to Climate Change in Northeast China over the Last 30 Years  
1  
[PERSON] 1 Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China; [EMAIL_ADDRESS] (L.H.); [EMAIL_ADDRESS] (H.R.); [EMAIL_ADDRESS] (Y.C.)  
Beijing Key Lab of Spatial Information Integration and 38 Application, Peking University, Beijing 100871, China  
School of Atmospheric Sciences, Sun Yat-Sen University, Guangzhou 519082, China; [EMAIL_ADDRESS]  
[PERSON]L_ADDRESS]; Tel.: +86-010-6275-5085
# Future Scenarios for Software-Defined Metro and Access Networks and Software-Defined Photonics
## 4 Photonic Devices and Components
### Technologies for Optical Processing  
In the Section 3.2.4, optical switching was presented with efficient techniques to switch optical signals in a re-configurable network. Optical switches can thus be ascribed to the category of SDP devices, since their switching table (implemented in the switch fabric) can be considered a key feature re-configurable by software, i.e., through the header information. Several solutions have been proposed in order to implement header (or label) processing wholly in the optical domain.  
In particular, nonlinear optical (NLO) transfer functions of optical components can be exploited for signal processing. Optical logic gates have been proposed: ultrafast nonlinear interferometers (UNIs) can be used to realize a high-speed logic processor, as well as cascaded semiconductor optical amplifier-Mach-Zehnder interferometers (SOA-MZIs) can be used to construct an optical XOR logic gate useful for header detection and processing [72]. Moreover, SOA-MZI can be used as a building block to implement other subsystems, such as wavelength converters, label/payload separation, clock-recovery circuits, or optical flip-flops [73]. All-optical subsystems based on SOA-MZIs capable of performing on-the-fly packet clock recovery, 3R regeneration, label/payload separation, and packet routing have been reported by [PERSON] et al. [74].  
A different approach for label pattern recognition is using optical code-division-multiple-access (O-CDMA) techniques to code the header. If such a coding is used, correlation techniques based on fiber Bragg gratings (FBGs) can be used to perform label detection [72].  
[PERSON] et al. [75] have proposed an innovative packet switching mechanism for OPS and OBS, based on cross-phase modulation (XPM) in a silicon nanowire fabricated in SOI technological platform. When the OOK control signal at the wavelength \(\lambda_{C}\) is 'on', the packet at the wavelength \(\lambda_{D}\) is switched out at the wavelength \(\lambda_{C}+\Delta\lambda\), as sketched in Figure 10a. If different wavelengths (\(\lambda_{C1},\lambda_{C2},\ldots\lambda_{CN}\)) are used as control signals, the incoming packet can be switched out at different wavelengths (\(\lambda_{C1}+\Delta\lambda,\lambda_{C2}+\Delta\lambda,\ldots\lambda_{CN}+ \Delta\lambda\)) to different outputs, as sketched in Figure 10b.
# Instance Hard Triplet Loss for In-video Person Re-identification
## 4 Experiments
### Cross-camera ReID Results  
We first compared our proposed instance hard triplet loss with the widely-used batch hard triplet loss on the IDE [30] network for the cross-camera ReID task. The result is shown in Table 2. As we can see, on all three datasets, our instance hard triplet loss outperformed the batch hard triplet loss. In the meantime, our new loss could train the network faster with an iteration time of 0.53 s, saving about 35% training time, which meant the proposed loss was both better and faster.  
The faster speed could be attributed to the smaller computation complexity, and the better performance could be attributed to the new hard triplets mining mechanism with more balanced positive and negative pairs.  
With a large input image size (like \(384\times 128\)), more data augmentation (like random crop and random erasing [68]), a stronger backbone (like ResNet-101 [49]), multiple part-based features (like six horizontal features in PCB+RPP [2]), and other tricks, we may achieve a better performance, but that was not our main purpose. We kept our network design simple to demonstrate the flexibility, speed, and efficiency of our new loss; hence, the results were easy to reproduce, and we will also release our implementation.
## 4 Evaluation and Results
### Cross-Corpus Validation  
To assess the generalization ability of our YOLO11-based malware classifier beyond the CICMalDroid2020 dataset, we conducted a cross-corpus validation experiment using a balanced external set comprising 500 malware samples from the Drebin dataset and 500 benign samples from CICAndMal2017. Rather than retraining, we used the final checkpoint weights (best.pt) of each pre-trained YOLO11 variant, previously fine-tuned on CICMalDroid2020. Each model was evaluated on the 1000-sample test set using the original RGB image representations, and predictions were obtained through direct inference.  
Table 14 reports the results, showing that all YOLO11 variants generalize well to unseen APKs from independent sources, with YOLO11x achieving perfect scores (100% across all metrics).  
For multi-class validation, we merged 1000 APK samples without access to fine-grained malware family labels, and we used the best-performing YOLO11n model (selected based on CICMalDroid2020 results) to perform inference on the combined dataset. The goal was to assess the model's distribution of predictions and confidence scores across known malware families (e.g., SMS, Adware, Riskware, Banking).  
As summarized in Table 15, the YOLO11n classifier predicted 515 samples as benign with an average confidence of 97.9%, followed by 197 as SMS (92.3%), 188 as Adware (82.7%), 53 as Riskware (64.8%), and 47 as Banking (61.0%). These predictions indicate that the model can meaningfully differentiate among malware types even in the absence of exact ground-truth labels.  
This cross-corpus validation strategy mitigates dataset bias and confirms the model's robustness against distributional shifts. The strong results across two independent datasets support the applicability of YoloMal-XAI in real-world malware detection scenarios involving diverse and previously unseen APKs.
## 4 Discussion: Comprehensive Analysis of the Deep-Learning-Oriented Framework
### Contribution to Seaport Operations' Efficiency and Sustainability
#### 4.1.3 Efficient Resource Allocation and Dynamic Route Planning  
Efficient resource allocation is fundamental to seaport operations, ensuring the optimal utilization of manpower and equipment. Our framework provides valuable insights that facilitate intelligent resource allocation. During off-peak hours, the system intelligently deploys fewer staff, optimizing the operational costs while maintaining service quality. Furthermore, the framework employs dynamic route planning, rerouting vehicles in response to real-time obstacles, such as road closures [48]. This adaptive approach minimizes delays, enhances delivery timelines, and augments operational resilience.