esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 LDCP Problem  
In this section, we formulate the latency-aware DU/CU placement problem in NGFI networks. In particular, LDCP concerns jointly:  
1. placement (in selected PP nodes) of DU and CU entities realizing baseband processing functions for a set of RU nodes, assuming given constraints on * maximum processing capacities of the PP nodes, * maximum latencies of the fronthaul, midhaul, and backhaul flows realized over the packet transport network between the RUs, the PP nodes selected (for DU and CU processing), and the DC node, and
2. allocation of bandwidth in network links so that to transport FH, MH, and BH flows, assuming given constraints on links capacities.  
We illustrate the DU and CU placement in PP nodes and the resulting traffic flows in Figure 2. The network consists of three RUs, three PPs, and one DC. These nodes are linked to five switches in the transport network. The data from RU\({}_{1}\) are carried as fronthaul flow \(FH_{1}\) through switch \(v_{1}\) to node PP\({}_{1}\), where a DU entity is located. After DU processing in PP\({}_{1}\), midhaul flow \(MH_{1}\) goes through switches \(v_{1}\) and \(v_{5}\) to node PP\({}_{2}\), where CU processing is performed. After completing the CU processing, the data are carried as backhaul flow \(BH_{1}\) through switches \(v_{5}\), \(v_{3}\), and \(v_{4}\) to the DC node, where the flow is terminated. RU\({}_{2}\) and RU\({}_{3}\) are grouped into a cluster and, therefore, their DU entities are located in the same PP node, namely in node PP\({}_{3}\). The data from RU\({}_{2}\) and RU\({}_{3}\) are transported in flows \(FH_{2}\) and \(FH_{3}\) to PP\({}_{3}\). Flows \(FH_{2}\) and \(FH_{3}\) are routed over switches \(v_{2}\) and \(v_{3}\). The CU processing for RU\({}_{2}\) and RU\({}_{3}\) is also performed in node PP\({}_{3}\). Therefore, the midhaul flows are not present in the network for these two RUs. After the DU and CU processing in PP\({}_{3}\), flows \(BH_{2}\) and \(BH_{3}\) are carried over nodes \(v_{3}\) and \(v_{4}\) to the DC.  
In the following, we introduce the notation used in problem formulation. Next, we propose an MILP model for the the LDCP optimization problem.  
Figure 2: Example of distributed unit (DU) and central unit (CU) placement in processing pool (PP) nodes and resulting traffic flows in the NGFI network.
## 1 Introduction
[15] showed that for IoT-based heating and cooling load forecasting, the Ada boost algorithm is best, but it lacks AI integration. These limitations have inspired new studies focused on improving prediction accuracy and integrating IoT-enabled smart data collection systems. To address these challenges, this study proposes a Smart Environment and Energy Monitoring and Prediction (SEEMP) system. The SEEMP system combines an AI-driven approach, a novel RNN-based hybrid LSTM-GRU model, and an IoT application server platform. It facilitates real-time data collection, curation, and centralized storage, enabling the application of advanced AI models for accurate energy consumption forecasting in real-life scenarios. Enabling power and environmental forecasting features in an IoT environment is crucial for improving its effectiveness and automation. For instance, hour-ahead temperature forecasting can optimize the control of heating or cooling elements, enhancing energy efficiency. Similarly, monitoring power consumption by these elements helps energy management systems allocate energy effectively. The IoT platform supports local and global cloud servers, offers sensor data analytics, and features a plug-in architecture that allows external systems to access data through a REST API. To ensure secure data transmission, the system employs Message Queue Telemetry Transport (MQTT) and Hypertext Transfer Protocol Secure (HTIPS) protocols. The SEEMP system was tested for predictive performance using environmental parameters such as office temperature and humidity, along with energy consumption for appliances like water dispensers, air conditioners (AC), and refrigerators. The main contributions of this work are summarized below:  
* A robust, real-time data collection framework for diverse office equipment is designed and implemented, where secure data exchange is ensured through the utilization of HTTPS and MQTT protocols. * The hybrid LSTM-GRU model is deployed for real-time processing and prediction on edge devices, addressing existing challenges effectively. * The reliability and performance of the hybrid model are tested with evaluation matrices and compared with the traditional model on different forecasting conditions. The structure of the paper is as follows: Section 2 discusses data collection and preprocessing, Section 3 reviews the model descriptions, Section 4 addresses performance evaluations, and Section 5 presents the conclusion.
## 3 Attacks on AES
### Information Processing
Key bytes of the last round, \(K_{i,j}^{N_{r}}\), are recovered from the output state \(\overline{S}^{N_{r}+1}\) (ciphertext) and the information about the accesses to each table \(\mathtt{Te}_{i}\), which is stored in \(\overline{X}_{i}\) and recovered using Algorithm 1. The output state (\(\overline{S}^{N_{r}+1}\)) is obtained by arranging the known ciphertext vector as a matrix, as indicated in Section 3.1. The algorithm first initializes an array of 256 elements for each of the 16 bytes of the key (\(K_{i,j}\)). This array (\(\mathrm{CK}_{i,j}^{N_{r}}\)) will contain the discarding votes for each key byte candidate. The candidate with less negative votes is the value of the secret key. ```
0:\(\overline{X}_{0},\overline{X}_{1},\overline{X}_{2},\overline{X}_{3},\overline {S}^{N_{r}+1}\)\(\triangleright\) Information about the accesses and ciphertext collected in the previous step. 0:\(\mathbf{K}_{i,j}^{N_{r}}\)\(\triangleright\) Secret key values
1:for\(l=0\)to\(256\)do
2:\(\mathrm{CK}_{i,j}^{N_{r}}\left[l\right]=0\); \(\triangleright\) Initialization of the candidates array
3:endfor
4:for\(t=0\)to\(number\_of\_encryptions\)do
5:if\(\overline{X}_{i+2}\left[t\right]==0\)then
6:for\(l=0\)to\(n\)do\(\triangleright\)\(n\) stands for the number of entries that a cache line holds
7:\(\mathrm{CK}_{i,j}^{N_{r}}\left[\overline{S}_{i,j}^{N_{r}+1}\left[t\right] \oplus Te_{i+2}\left[l\right]\right]+\); \(\triangleright\) Vote for discard key candidate
8:endfor
9:endif
10:endfor
11:return\(\mathbf{argmin}(\mathrm{CK}_{i,j}^{N_{r}})\); \(\triangleright\) The candidate with the fewest votes for discard is the secret key. ```  
**Algorithm 2** Recovery algorithm for key byte \(K_{i,j}^{N_{r}}\). Figure 3 shows the values of one of the 16 \(\mathrm{CK}_{i,j}^{N_{r}}\) array, which contains the discarding votes each possible key value has received during the attack for a concrete byte of the key. The x-axis represents each possible key value, and the y-axis the number of times each option has been discarded. The minimum is well distinguishable from the rest of possible key values and is highlighted in red. The index with the minimum score represents the secret key. This approach allows recovering the 128 bits of the key with fewer than 3000 encryptions on average in the best case. We present further results and provide further details in Section 4.
###### Abstract  
Significant engineering of _Saccharomyces cerevisiae_ is required to enable consolidated bioprocessing (CBP) of lignocellulosic to ethanol. Genome modification in _S. cerevisiae_ has been successful partly due to its efficient homology-directed DNA repair machinery, and CRISPR technology has made multi-gene editing even more accessible. Here, we tested the integration of cellulase encoding genes to various sites on the yeast genome to inform the best strategy for creating cellulolytic strains for CBP. We targeted endoglucanase (EG) or cellobiohydrolase (CBH) encoding genes to discreet chromosomal sites for single-copy integration or to the repeated delta sites for multi-copy integration. CBH1 activity was significantly higher when the gene was targeted to the delta sequences compared to single gene integration loci. EG production was comparable, though lower when the gene was targeted to a chromosome 10 site. We subsequently used the information to construct a strain containing three cellulase encoding genes. While individual cellulase activities could be assayed and cellulose conversion demonstrated, it was shown that targeting specific genes to specific loci had dramatic effects on strain efficiency. Since marker-containing plasmids could be cured from these strains, additional genetic changes can subsequently be made to optimize strains for CBP conversion of lignocellulosic.  
consolidated bioprocessing; heterologous cellulase production; multi-gene expression; CRISPR-Cas9; _Saccharomyces cerevisiae_
# Pico-Sat to Ground Control: Optimizing Download Link via Laser Communication
## 4 Results
### Link Budget for FSO Nano-Satellite
#### 4.1.2 Photon-Counting LED Link-Budget Analysis  
* A single Lumen equals \(4.09\times 10^{15}\) photons per second.
* An LED, as used in smartphones' flash and in the discussed pico-satellite-SATLLA-2B, induces about 60 Lumen (lm), which is \(L_{1}=60\times 4.09\times 10^{15}=2.4\times 10^{17}\) photons per second.
* SATLLA-2B has an array of 10 LEDs, such a combined array induces about 600 lm which is \(L_{10}=600\times 4.09\times 10^{15}=2.4\times 10^{18}\) photons per second.
* The angle of the LED FoV (field of view) is about 60 degrees and the satellite distance from Earth is about 500 km (\(500,000\) m). Yet, in order to perform an FSO communication, a distance of 1000 km is assumed, which leads to an area of \(10^{12}\) m\({}^{2}\).
* The telescope width is assumed to be 14\", which has an area of about 0.1 m\({}^{2}\).
* Therefore, the geometric ratio between telescope area and the emitted area on ground is about \(10^{-13}\).
* Based on the geometric ratio, the telescope is expected to receive \(2.4\times 10^{18}\times 10^{-13}=2.4\times 10^{5}\) photons in optimal conditions.
* The atmospheric loss (\(Atm_{loss}\)) and the telescope loss (\(Rx_{loss}\)) are assumed to be about 3 dB (50%) each (or about 25% combined), allowing about \(2.4\times 10^{5}\times 0.25=6\times 10^{4}\) photons per seconds received in the the telescope detector.
* Using a sensitive optical detector (with expected ratio of 100 photons for bit, and 20% header overhead), a bit rate of 500 bits per second is expected.
* Finally, a sensitive camera mounted on the telescope should be able to detect the pico-satellite and estimate the actual FSO path loss and link budget.
## 3 Results and Discussion
### Effect of Storage Time and Storage Temperature  
The storage time and storage temperature have a great influence on determining the antioxidant activity and total phenolic content of black tea. Figures 6 and 7 show the DPPH scavenging activity and total phenolic content of black tea at different temperatures for different storage times, respectively. As shown in Figure 6, the difference between the two means of the DPPH scavenging ability is not statistically significant at 4 \({}^{\circ}\)C and 25 \({}^{\circ}\)C. In addition, at 9 \({}^{\circ}\)C, the trend was similar for 0-day to 15-day storage periods, except for the 9-day storage period, which was lower than the other storage times. These results indicated that the DPPH scavenging activity of black tea did not decline within 15 days even though it was kept at 25 \({}^{\circ}\)C. No notable difference was observed in the scavenging activity against the DPPH radical during 15-day storage. [PERSON] et al. [15] reported that the DPPH scavenging activity of black tea exhibited considerable decline at 25 \({}^{\circ}\)C after 3 months; however, no perceptible difference was observed in the DPPH scavenging activity among the three storage temperatures in this study because of the relatively short 15-day storage period.  
Figure 6: Effects of storage time on the DPPH scavenging activity of black tea: (a) 4 \({}^{\circ}\)C, (b) 9 \({}^{\circ}\)C and (c) 25 \({}^{\circ}\)C. Means with the same capital alphabetical letters are not significantly different according to the results of the Tukey’s test.  
In terms of the total phenolic content, no significant difference was observed from 0-day to 15-day storage periods at 4 \({}^{\circ}\)C and 25 \({}^{\circ}\)C, as shown in Figure 7. As with the result of the DPPH scavenging ability, there was a similar trend for the total phenolic content from 0-day to 15-day storage at 9 \({}^{\circ}\)C, except 12-day storage, which was lower than the other storage times.  
In summary, the black tea exhibited similar scavenging ability and total phenolic content at 4, 9, and 25 \({}^{\circ}\)C regardless of storage time in this study. Although at 9 \({}^{\circ}\)C, the performance of DPPH scavenging ability and total phenolic content was lower than the other storage times at 9-day and 12-day, respectively. The situation might have resulted from sampling and the significant level of \(p<0.05\). Moreover, the performance of the total phenolic content in this study concurred with a previous report indicating that the total phenolic content of black tea remained almost constant after storage at 25 \({}^{\circ}\)C or 50 \({}^{\circ}\)C for 6 months [15].
# SIMECK-T: An Ultra-Lightweight Encryption Scheme for Resource-Constrained Devices
### Motivation and Strategy  
The scope of the research is to investigate encryption algorithms that can be downgraded to a reduced number of rounds, without compromising their security level. To preserve the cipher's strength, the reduced-round algorithms are enhanced by inserting new layers of security, and by twining.  
SPECK-R [19] is a cipher that combines the round function of SPECK [7], with a key scheduling algorithm to create RC4 substitution boxes [20]. Our public implementation of SPECK-R [21], an algorithm proposed by [PERSON] et al. in [19], is an improved version using the RC4D key schedule algorithm [22], and a round key of 26 words [23].  
Because SPECK-R provides the same security level as SPECK [7], using only seven rounds [19], we apply the same technique for a selection of high-speed, software-oriented, ARX block ciphers. The selected ciphers are fortified with dynamic substitution boxes, an additional security layer that has been used to convert SPECK into SPECK-R [19]. The modified ciphers are based on our previous work with SPECK-R in [21; 23].  
The output of the augmented ciphers in our selection, using reduced-round versions, is verified to be indistinguishable from random data. This is achieved using NIST SP800-22 [24] and AIS.31 [25; 26] standards, and using the law of the iterated logarithm (LIL) [27; 28].  
The best performing ciphers that we obtain are combined, in a manner not involving any lookup tables.
# Improved Arabic-Chinese Machine Translation with Linguistic Input Features
## 7 Conclusions and Future Work  
Integrating additional linguistic knowledge is one of the core problems in the PBSMT model, which we addressed in this work. To investigate the benefits of linguistic input features for Arabic  
Figure 5: Examples of Arabic-to-Chinese MT output over the baseline model, tokenized models, and best-factored models. The English glosses are presented to ease readability.  
\(\rightarrow\) Chinese MT, we compare a linguistically augmented MT model and PBSMT. We performed a preliminary evaluation of several deep linguistic features for Arabic, including lemmas, POS, and morph features. Several configurations were applied to evaluate the results of the factored model approach. We also empirically tested the contribution of various tokenization schemes to the PBSMT system, in addition to the effects of all models on reducing the data sparsity.  
Our results show that using tokenization schemes for Arabic pre-processing helps to deal with the major issue of data spareness in the translation from Arabic as a morphologically rich and complex language. Linguistic factors, which we utilized to annotate the Arabic corpus, improved things even further. Factored systems achieved better performance compared with the baseline and tokenized PBSMT. The best system (POS model) yielded a BLEU score of 2.17 over the baseline and 0.63 over the tokenized phrase-based model, while lemma backoff model reduced the OOV rates from 5.74% to 0.70% for tokens, and from 22.80% to 6.71% for types.  
To the best of our knowledge, this work is the first to test factored MT on the Arabic-Chinese language pair. Considering the following aspects can help improve this project: (1) using a big dataset to explore the linguistic input features on Arabic-to-Chinese with neural machine translation that has been proven useful for multiple languages; (2) adding factors to the target side language (Chinese); (3) testing other preprocessing tools that may perform better in Arabic-Chinese translation; and (4) conducting experiments on Chinese-to-Arabic translation that may reveal new insights.  
[PERSON] designed, wrote the paper and did the experiments; [PERSON] supervised the work and offered financial support; [PERSON] provided ideas to enrich the research, did the review and editing of the original draft; [PERSON]-M. did data analysis and surveyed the related works.  
This research was supported by the National Natural Science Foundation of China under Grant No. 61876190.  
The authors declare no conflict of interest.
# M-Emu: A Platform for Multicast Emulation
## 3 Implementation
### Service-Forwarding Architecture  
The Service-Forwarding Architecture contains a control plane and a data plane. As for the control plane, M-Emu adds basic function module and algorithm module on the basis of the existing functions of NS4's control plane. The data plane of M-Emu is divided into a forwarding module and service module. The forwarding module contains all the functions of the NS4's data plane and adds the function of interacting with the service module. The service module is the core of M-Emu's Service-Forwarding Architecture, which enables the data plane of M-Emu to deal with complex multicast protocols and algorithms. The architecture diagram is shown in Figure 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Emulators** & **Protocol Independent** & **Accuracy** & **Multicast Function** \\ \hline ns-3 & No & High & No \\ Mininet & No & Medium & No \\ EstiNet & No & High & No \\ OMNet++ & No & Medium & No \\ NS4 & Yes & High & No \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of emulators.
## 5 Discussion
The correction of VZA and swath gaps obviously results in the overestimation of MODIS grid FRP and alters a temporal pattern compared to the observed VIIRS grid FRP, especially during days from Jan 5-10 and Jan 15-20 (Figure 11). The MODIS equatorial swath gaps also significantly affect its observation of actively burning fires at low latitudes. By referencing to VIIRS FRP, MODIS swath gaps result in a mean monthly omission error of \(\sim\)12.5% in MODIS FRP, which varies largely in different seasons. It is similar to a previous finding that swath gaps explain 14% of omission error by comparing MODIS fire detections with field observations in the Yucatan forest, Mexico [38]. At low latitudes, it is well known that cloud obscuration is one of key factors that limit fire observations [56]. For instance, cloud contamination could lead to an omission error of 11% in MODIS fire detections across the Amazon region [56], which is very close to the omission error caused by swath gaps. Thus, MODIS swath gaps play an equally  
Figure 11: Mean VZA and daily grid FRP from (**a**) VIIRS and (**b**) MODIS in a 1\({}^{\circ}\) grid centering at 30.232\({}^{\circ}\)E and 4.139\({}^{\circ}\)N in January 2017. The black triangle represents grid FRP and the gray solid cycle is VZA. In (**b**), the cyan squares indicate the days without valid MODIS fire observations due to swath gaps and the dashed line of 35\({}^{\circ}\) VZA is considered as a VZA threshold to adjust MODIS FRP (results are denoted as pink triangles) based on the algorithm proposed by [PERSON] et al. [35]. important role in affecting MODIS active fire data as clouds. Therefore, the prediction of MODIS missing FRP inside swath gaps for historical MODIS fire data during the past two decades could significantly improve the estimation of biomass-burning emissions and benefit related applications. Mitigation of the swath-gap-caused underestimation of MODIS FRP is very challenging. Although MODIS swath gap has been a known problem in fire detection since the first release of the MODIS active fire product [57], very limited studies have investigated potential approaches to mitigate the swath gap effect on MODIS FRP. For any day without valid observations due to swath gaps, a commonly used strategy is to use the FRP observations of the previous day (or previous days with the minimal VZA) directly [24; 35]. This approach could largely underestimate or overestimate the missing FRP inside swath gaps, as illustrated in Figure 11, because burn sites most likely do not persist from day to day due to human activity and changes in fire weather [36]. For the time period with the VIIRS fire observations available, a potential mitigation approach is to examine the daily FRP relationship between the MODIS and VIIRS sensors over the areas outside swath gaps and apply it to predict FRP missed by MODIS inside swath gaps.
# Botnet Detection Based On Machine Learning Techniques Using DNS Query Data
## 4 Experiment and Evaluation
### Data Pre-Processing  
According to [8,9,14], automatically generated botnet domain names often have DNS characteristics, network characteristics and lexical features that are different from those of legitimate domain names. [PERSON] et al. [8] proposed to use the distribution analysis of vowels, numbers, and other characters in domain names to distinguish legitimate domain names and domain names generated by botnet algorithms. More broadly, [PERSON] et al. [9] proposed to use two groups of domain names' characteristics, including DNS characteristics (IP address, network address, etc.) and lexical features (the character distribution of the domain names). Meanwhile, [PERSON] [14] proposed to use 36 characteristics in two groups, including 18 vocabulary characteristics (mean, variance and standard deviation of 1-g, 2-g, 3-g and 4-g; entropy; characteristics of characters, numbers, vowels and consonants) and 18 network characteristics (TTL, number of network addresses, etc.). In this paper, we focus on exploiting the statistical vocabulary characteristics of 2-g and 3-g clusters, and the vowel distribution characteristics of domain names. Specifically, we use 16 statistical vocabulary features of 2-g (bi-gram) and 3-g (tri-gram) clusters, and 2 vowel distribution characteristics to extract 18 features of each domain name for the training phase and also for the detection phase.